Description:FORM 2

THE PATENTS ACT, 1970
(39 of 1970)
&
THE PATENT RULES, 2003

COMPLETE SPECIFICATION
(See Section 10 and Rule 13)

Title of invention:
SYSTEMS AND METHODS FOR NASAL VIRTUAL SURGERY

Applicant:
Tata Consultancy Services Limited
A company Incorporated in India under the Companies Act, 1956
Having address:
Nirmal Building, 9th Floor,
Nariman Point, Mumbai 400021,
Maharashtra, India

The following specification particularly describes the invention and the manner in which it is to be performed.
TECHNICAL FIELD
The disclosure herein generally relates to virtual nasal procedures, and, more particularly, to systems and methods for nasal virtual surgery for evaluating pre- and post-surgical outcomes and validating nasal surgical devices.

BACKGROUND
Clinical evaluation of the Nasal cavity comprises imaging, patient qualitative report, and the surgeon’s experience in treating nasal symptoms and obstruction. For instance, Nasal airway obstruction is a common disease in which the nasal passages are blocked and prevent a comfortable amount of air from passing through the nose. About 13% of adults (29.3 million people) in the US and 11% of the European population suffer from obstruction of nasal breathing, swelling, or inflammation of the nasal sinuses. It is estimated that approximately 5.8 billion is spent annually on surgery to relieve nasal obstruction. Surgical correction is one of the strategies to tackle nasal airway obstruction, chronic condition, and the like. Nasal surgical planning is challenging even for experienced Rhinologists due to the complex anatomical structure of the nasal airway passage. Therefore, understanding three-dimensional nasal airway anatomy is crucial for Rhinologists because it involves several evaluation parameters to mimic the human respiration or normal breathing pattern or breathing pattern disorder, such as internal airflow patterns, pressure drop across the cavity, heat and mass transfer rate and wall shear stress.

SUMMARY
Embodiments of the present disclosure present technological improvements as solutions to one or more of the above-mentioned technical problems recognized by the inventors in conventional systems.
For example, in one aspect, there is provided a processor implemented method for nasal virtual surgery for evaluating pre- and post-surgical outcomes. The method comprises obtaining, via one or more hardware processors, a Computed Tomography (CT) image of a nasal cavity of a user; rendering, via the one or more hardware processors, a three-dimensional (3D) model of the nasal cavity from the CT image based on at least one of (i) a segmentation of an air passage region in the CT image of the nasal cavity based on (a) one or more user inputs, and (b) an Artificial Intelligence (AI) based technique; iteratively performing: meshing the rendered 3D model of the nasal cavity to obtain a meshed geometry of the nasal cavity, wherein the meshed geometry of the nasal cavity comprises a plurality of cell identifiers (IDs) associated with a plurality of grids; performing a mesh independent analysis on the meshed geometry of the nasal cavity to obtain a first simulated nasal mesh model, wherein the mesh independent analysis comprises: performing a velocity gradient optimization technique of the plurality of grids, with a dynamic velocity gradient threshold determination to obtain one or more optimized gradient values; splitting one or more grids from the plurality of grids based on the one or more optimized gradient values to obtain a refined mesh; smoothing the refined mesh using one or more smoothing techniques to obtain a smoothened mesh; and simulating the smoothened mesh of the nasal cavity to obtain the first simulated nasal mesh model; determining one or more quality parameters of the simulated nasal mesh model; and regenerating a nasal cavity mesh based on the one or more determined quality parameters to obtain a regenerated nasal cavity mesh; and simulating, until a convergence is met, the regenerated nasal cavity mesh by solving one or more associated governing equations based on one or more setup parameters to obtain a first simulated nasal model, which provides a functional aspect of the 3D model of the nasal cavity.
In an embodiment, the meshed geometry of the nasal cavity is based on a first edge length and a second edge length being imposed on a plurality of surfaces of the 3D model of the nasal cavity of the user.
In an embodiment, the one or more quality parameters comprise at least one of a skewness, an aspect ratio, and a non-orthogonality of the first simulated nasal mesh model.
In an embodiment, the one or more setup parameters comprise at least one of air flow rate (e.g., such as sleeping, at rest / relax, sports and extreme condition); at one or more inlets of the nasal cavity, a selection of a turbulence model based on a Reynolds numbers, a nasal wall, an outlet boundary condition, and a selection of one or more discretization schemes.
In an embodiment, if the convergence is not met, the method comprises remeshing the first simulated nasal model or validating the one or more setup parameters.
In an embodiment, the convergence is obtained by using a Machine Learning (ML) technique (e.g., a ML based regression).
In an embodiment, if the convergence is met, the method comprises remeshing the first simulated nasal model for the one or more setup parameters, which provide a functional aspect comprising a wall shear, a velocity and pressure, a temperature and a relative humidity of the 3D model of the nasal cavity.
In an embodiment, the method further comprises generating a Virtual Reality (VR) enabled visualization of the nasal cavity with physical feedback using one or more haptic techniques.
In an embodiment, the method further comprises determining (i) an aerodynamic (e.g., air – respiration), a hydrodynamic (fluid, mucosal), and a flow distribution, (ii) a characteristic behavior of one or more surgical equipment, and one or more prosthetics using the Virtual Reality (VR) enabled visualization, and (iii) a training of a nasal procedure (e.g., secondary and territory training).
In an embodiment, the method further comprises determining a requirement for the nasal procedure based on at least one of (i) the aerodynamic (air – respiration), and hydrodynamic (fluid, mucosal), flow distribution, (ii) the characteristic behavior of one or more surgical equipment, and one or more prosthetics, and the training of the nasal procedure; modifying a specific region of the nasal cavity of the user based on the determined requirement, as a outcome of simulated Nasal mode, which could be called as “what if conditions”; performing a local meshing of the first simulated nasal mesh model based on the reduced region of the nasal cavity to obtain a second simulated nasal model, wherein the local meshing comprises: repeating the step of regenerating the nasal cavity mesh; and performing a comparison of the first simulated nasal model and the second simulated nasal model to obtain a performance report of the nasal procedure.
In another aspect, there is provided a processor implemented system for nasal virtual surgery for evaluating pre- and post-surgical outcomes. The system comprises: a memory storing instructions; one or more communication interfaces; and one or more hardware processors coupled to the memory via the one or more communication interfaces, wherein the one or more hardware processors are configured by the instructions to: obtain a Computed Tomography (CT) image of a nasal cavity of a user; render a three-dimensional (3D) model of the nasal cavity from the CT image based on at least one of (i) a segmentation of an air passage region in the CT image of the nasal cavity based on (a) one or more user inputs, and (b) an Artificial Intelligence (AI) based technique; iteratively perform: meshing the rendered 3D model of the nasal cavity to obtain a meshed geometry of the nasal cavity, wherein the meshed geometry of the nasal cavity comprises a plurality of cell identifiers (IDs) associated with a plurality of grids, ; performing a mesh independent analysis on the meshed geometry of the nasal cavity to obtain a first simulated nasal mesh model, wherein the mesh independent analysis comprises: performing a velocity gradient optimization technique of the plurality of grids, with a dynamic velocity gradient threshold determination to obtain one or more optimized gradient values; splitting one or more grids from the plurality of grids based on the one or more optimized gradient values to obtain a refined mesh; smoothing the refined mesh using one or more smoothing techniques to obtain a smoothened mesh; and simulating the smoothened mesh of the nasal cavity to obtain the first simulated nasal mesh model; determining one or more quality parameters of the first simulated nasal mesh model; and regenerating a nasal cavity mesh based on the one or more determined quality parameters to obtain a regenerated nasal cavity mesh; and simulate, until a convergence is met, the regenerated nasal cavity mesh by solving one or more associated governing equations based on one or more setup parameters to obtain a first simulated nasal model.
In an embodiment, the meshed geometry of the nasal cavity is based on a first edge length and a second edge length being imposed on a plurality of surfaces of the 3D model of the nasal cavity of the user.
In an embodiment, the one or more quality parameters comprise at least one of a skewness, an aspect ratio, and a non-orthogonality of the first simulated nasal mesh model.
In an embodiment, the one or more setup parameters comprise at least one of air flow rate at one or more inlets of the nasal cavity, a selection of a turbulence model based on a Reynolds numbers, a nasal wall, an outlet boundary condition, and a selection of one or more discretization schemes.
In an embodiment, if the convergence is not met, the one or more hardware processors are further configured by the instructions to remesh the first simulated nasal model or validating the one or more setup parameters.
In an embodiment, the convergence is obtained by a Machine Learning (ML) technique (e.g., a ML based regression).
In an embodiment, the one or more hardware processors are further configured by the instructions to generate a Virtual Reality (VR) enabled visualization of the nasal cavity with physical feedback using one or more haptic techniques.
In an embodiment, the one or more hardware processors are further configured by the instructions to determine (i) an aerodynamic (air – respiration), a hydrodynamic (fluid, mucosal), and a flow distribution,, (ii) a characteristic behavior of one or more surgical equipment, and one or more prosthetics using the Virtual Reality (VR) enabled visualization, and (iii) a training of a nasal procedure (e.g., a secondary and territory training).
In an embodiment, the one or more hardware processors are further configured by the instructions to determine a requirement for a nasal procedure requirement based on at least one of (i) an aerodynamic (air – respiration), a hydrodynamic (fluid, mucosal), and a flow distribution,, (ii) the characteristic behavior of one or more surgical equipment, and one or more prosthetics and the training of the nasal procedure; modifying a specific region of the nasal cavity of the user based on the determined requirement; performing a local meshing of the first simulated nasal mesh model during the nasal procedure based on the reduced region of the nasal cavity to obtain a second simulated nasal model, wherein the local meshing comprises: repeating the step of regenerating the nasal cavity mesh; and performing a comparison of the first simulated nasal model and the second simulated nasal model to obtain a performance report of the nasal procedure.
In yet another aspect, there are provided one or more non-transitory machine-readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors cause nasal virtual surgery for evaluating pre- and post-surgical outcomes by obtaining a Computed Tomography (CT) image of a nasal cavity of a user; rendering a three-dimensional (3D) model of the nasal cavity from the CT image based on at least one of (i) a segmentation of an air passage region in the CT image of the nasal cavity based on (a) one or more user inputs, and (b) an Artificial Intelligence (AI) based technique; iteratively performing: meshing the rendered 3D model of the nasal cavity to obtain a meshed geometry of the nasal cavity, wherein the meshed geometry of the nasal cavity comprises a plurality of cell identifiers (IDs) associated with a plurality of grids; performing a mesh independent analysis on the meshed geometry of the nasal cavity to obtain first simulated nasal mesh model, wherein the mesh independent analysis comprises: performing a velocity gradient optimization technique of the plurality of grids, with a dynamic velocity gradient threshold determination to obtain one or more optimized gradient values; splitting one or more grids from the plurality of grids based on the one or more optimized gradient values to obtain a refined mesh; smoothing the refined mesh using one or more smoothing techniques to obtain a smoothened mesh; and simulating the smoothened mesh of the nasal cavity to obtain the first simulated nasal mesh model; determining one or more quality parameters of the first simulated nasal mesh model; and regenerating a nasal cavity mesh based on the one or more determined quality parameters to obtain a regenerated nasal cavity mesh; and simulating, until a convergence is met, the regenerated nasal cavity mesh by solving one or more associated governing equations based on one or more setup parameters to obtain a first simulated nasal model.
In an embodiment, the meshed geometry of the nasal cavity is based on a first edge length and a second edge length being imposed on a plurality of surfaces of the 3D model of the nasal cavity of the user.
In an embodiment, the one or more quality parameters comprise at least one of a skewness, an aspect ratio, and a non-orthogonality of the first simulated nasal mesh model.
In an embodiment, the one or more setup parameters comprise at least one of air flow rate at one or more inlets of the nasal cavity, a selection of a turbulence model based on a Reynolds numbers, a nasal wall, an outlet boundary condition, and a selection of one or more discretization schemes.
In an embodiment, if the convergence is not met, the one or more instructions which when executed by one or more hardware processors further cause remeshing the first simulated nasal mesh model or validating the one or more setup parameters.
In an embodiment, the convergence is obtained by Machine Learning (ML) technique (e.g., a ML based regression).
In an embodiment, the one or more instructions which when executed by one or more hardware processors further cause generating a Virtual Reality (VR) enabled visualization of the nasal cavity with physical feedback using one or more haptic techniques.
In an embodiment, the one or more instructions which when executed by one or more hardware processors further cause determining (i) an aerodynamic (air – respiration), a hydrodynamic (fluid, mucosal), and a flow distribution, (ii) a characteristic behavior of one or more surgical equipment, and one or more prosthetics using the Virtual Reality (VR) enabled visualization, and (iii) a training of a nasal procedure (e.g., a secondary and territory training).
In an embodiment, the one or more instructions which when executed by one or more hardware processors further cause determining a requirement for a nasal procedure requirement based on at least one of (i) the aerodynamic (air – respiration), the hydrodynamic (fluid, mucosal), the flow distribution, (ii) the characteristic behavior of one or more surgical equipment, and one or more prosthetics, and (iii) the training of the nasal procedure; modifying a specific region of the nasal cavity of the user based on the determined requirement; performing a local meshing of the first simulated nasal mesh model during the nasal procedure based on the reduced region of the nasal cavity to obtain a second simulated nasal model, wherein the local meshing comprises: repeating the step of regenerating the nasal cavity mesh; and performing a comparison of the first simulated nasal model and the second simulated nasal model to obtain a performance report of the nasal procedure.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS
The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate exemplary embodiments and, together with the description, serve to explain the disclosed principles:
FIG. 1 depicts an exemplary system for nasal virtual surgery for evaluating pre- and post-surgical outcomes, in accordance with an embodiment of the present disclosure.
FIG. 2 depicts an exemplary high level block diagram of the system of FIG. 1 for nasal virtual surgery for evaluating pre- and post-surgical outcomes, in accordance with an embodiment of the present disclosure.
FIG. 3 depicts an exemplary flow chart illustrating a method for nasal virtual surgery for evaluating pre- and post-surgical outcomes, using the systems 100 of FIGS. 1-2, in accordance with an embodiment of the present disclosure.
FIG. 4 depicts a 3D model rendering of the nasal cavity from the CT image in slicer as implemented by the system of FIG. 1, in accordance with an embodiment of the present disclosure.
FIGS. 5A and 5B depict a sagittal view and a tilted sagittal view, showing boundary surfaces, respectively, in accordance with an embodiment of the present disclosure.
FIG. 6 depicts a representation of nasal model showing coronal slices (pattern hatching) in the left most panel, in accordance with an embodiment of the present disclosure.
FIG. 7 depicts a comparison of pressure drop across the nasal cavity at different flow rates among the simulated results and the experimental values reported in the literature, in accordance with an embodiment of the present disclosure.
FIG. 8 depicts a pressure contour plot at 10L/min, in accordance with an embodiment of the present disclosure.
FIG. 9 depicts a velocity magnitude contour plot at 10L/min, in accordance with an embodiment of the present disclosure.
FIGS. 10A and 10B depict Average (a) temperature and (b) relative humidity profiles across the nasal cavity at different cross-sections, in accordance with an embodiment of the present disclosure.
FIG. 11 depicts temperature contour plots at different cross-sections across the nasal cavity for the air with cold climatic condition, in accordance with an embodiment of the present disclosure.
FIG. 12 depicts contour plots of wall heat flux on the surface of nasal cavity for the air with cold climatic condition, in accordance with an embodiment of the present disclosure.
FIG. 13 depicts a velocity contour representation in the coronal cross-sectional view for different meshes at (a) slice-1 (for left and right nasal cavities) and (b) slice-2 (for the shared region of both cavities), in accordance with an embodiment of the present disclosure.
FIG. 14 depicts pressure drop as a function of flow rate for the pre-surgical and post-surgical models (a) left and (b) right nasal cavities, in accordance with an embodiment of the present disclosure.
FIG. 15 depicts average (a) temperature and (b) relative humidity profiles for the pre-surgical and post-surgical models, in accordance with an embodiment of the present disclosure.

DETAILED DESCRIPTION OF EMBODIMENTS
Exemplary embodiments are described with reference to the accompanying drawings. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. Wherever convenient, the same reference numbers are used throughout the drawings to refer to the same or like parts. While examples and features of disclosed principles are described herein, modifications, adaptations, and other implementations are possible without departing from the scope of the disclosed embodiments.
Clinical evaluation of the Nasal cavity comprises imaging, patient qualitative report, and the surgeon’s experience, in treating nasal symptoms and obstruction. For instance, Nasal airway obstruction is a common disease in which the nasal passages are blocked and prevent a comfortable amount of air from passing through the nose. About 13% of adults (29.3 million people) in the US and 11% of the European population suffer from obstruction of nasal breathing, swelling, or inflammation of the nasal sinuses. It is estimated that approximately 5.8 billion is spent annually on surgery to relieve nasal obstruction. Surgical correction is one of the strategies to tackle nasal airway obstruction. Nasal surgical planning is challenging even for experienced Rhinologists due to the complex anatomical structure of the nasal airway passage. Therefore, understanding three-dimensional nasal airway anatomy is crucial for Rhinologists because it involves several evaluation parameters to mimic the human respiration or normal breathing pattern or breathing pattern disorder, such as internal airflow patterns, pressure drop across the cavity, heat and mass transfer rate and wall shear stress.
Embodiments of the present disclosure provide system and method which enable the investigation of the mechanistic insight of the nasal airway, reconstructed from a tomographic image and morphometric analysis of the nasal with direct 3D interaction using advanced visualization (AR/VR). In addition, the proposed system allows parametric modification, i.e., nasal structural modification for the virtual surgery on the 3D nasal model, thereby mimicking clinical surgery. In the present disclosure, patient specific CFD modeling was simulated for the preoperative and postoperative conditions. The nasal airflow intervention presented in the present disclosure was validated and it agrees well with reported experimental data. It was observed that in the frontal region of nasal cavity, air flow attains the most significant velocity near the nasal wall and a high rate of heat transfer takes place between inhaled air and the nasal wall. While in the posterior region, the decrease in heat transfer rate is observed as the temperature difference between the air and the nasal wall/bone reduces. Additionally, it was found that the inhaled air humidifies sufficiently to 85 % before it reaches lungs, and in this process, the nasal mucosal wall plays an important role. In the postoperative conditions, it shows significant reduction in the nasal resistance after the increase in the cross-sectional area which allows the air to flow at a lower velocity. Thereby, the resulting system functions as an armamentarium for the Rhinologists, which will help optimize technique and time and enhance the postoperative surgical efficiency and the customer’s experience (surgeon and patient).
Rhinologists face challenges in monitoring, diagnosing, and delivering optimal therapy for the nasal cavity to improve the breathing function of nose. The foremost reason for these challenges is the complex anatomical morphology of the Nose, particularly the nasal structure, consisting of a series of bends and curves like nasal concha, conchal crest, and narrow space through and around the Nose. Additional causes are the pathological condition that can obstruct airflow, such as sinusitis, flu, etc., secondary to other pathological causes, such as nasal polyps, septal deviation, and ostiomeatal obstruction. Pathological inconsistency multiplies the challenges: for example, some individuals with significant structural variation do not report a nasal disorder, whereas, for some individuals, even a tiny deviation may need surgical correction therapy. Moreover, pathological disorders do not have any gold standards.
Due to nose’s unique features in primates and pathological inconsistency complexity, the mechanistic insight into the airflow dynamics through the nasal cavity is demanding. In the treatment procedure, the measurement phase is usually manually performed by an operator and investigate with 2D radiological images; therefore, this practice might be affected by inter and intra-operator errors. The state-of-the-art technique adopted by clinicians to monitor nasal airflow and resistances are endoscopy, standard rhinomanometry, acoustic rhinometry, and radiological imaging; these techniques are more cumbersome, painful, and time-consuming. Nonetheless, current techniques fail to report the details of aerodynamics. In addition, the real time scenario like breathing rate during exercise or outdoor activity like sport or sleeping rate could not be simulated or could not be analysed.
The above challenges were addressed in various investigations/literatures (e.g., refer 1) L. Li, H. Zang, D. Han, and N. R. London Jr, “Impact of varying types of nasal septal deviation on nasal airflow pattern and warming function: a computational fluid dynamics analysis,” Ear, Nose & Throat Journal, vol. 100, no. 6, pp. NP283–NP289, 2021.; 2) M. Moore and R. Eccles, “Objective evidence for the efficacy of surgical management of the deviated septum as a treatment for chronic nasal obstruction: a systematic review,” Clinical otolaryngology, vol. 36, no. 2, pp. 106–113, 2011.; 3) L. Li, D. Han, L. Zhang, Y. Li, H. Zang, T. Wang, and Y. Liu, “Aerodynamic investigation of the correlation between nasal septal deviation and chronic rhinosinusitis,” The Laryngoscope, vol. 122, no. 9, pp. 1915–1919, 2012.; 4) X. B. Chen, H. P. Lee, V. F. Hin Chong, and D. Y. Wang, “Assessment of septal deviation effects on nasal air flow: a computational fluid dynamics model,” The Laryngoscope, vol. 119, no. 9, pp. 1730–1736, 2009.; 5) B. M. Spector, D. J. Shusterman, A. N. Goldberg, E. M. Weaver, A. A. Farag, B. A. Otto, and K. Zhao, “Computational modeling of nasal nitric oxide flux from the paranasal sinuses: Validation against human experiment,” Computers in Biology and Medicine, vol. 136, p. 104723, 2021.; 6) S. Kaneda, M. Iida, H. Yamamoto, M. Sekine, K. Ebisumoto, A. Sakai, and Y. Takakura, “Evaluation of nasal airflow and resistance: computational modeling for experimental measurements,” Tokai J Exp Clin Med, vol. 44, no. 3, pp. 59–67, 2019.; 7) K. Inthavong, A. Chetty, Y. Shang, and J. Tu, “Examining mesh independence for flow dynamics in the human nasal cavity,” Computers in Biology and Medicine, vol. 102, 09 2018.; and 8) E. Ng, C. F. Lee, M. Z. Abdullah, K. A. Ahmad, and I. Lutfi Shuaib, “Standardization of Malaysian adult female nasal cavity,” Computational and Mathematical Methods in Medicine, vol. 2013, p. 519071, 2013.) and have shown computational fluid dynamics as a unified approach to analyzing airflow patterns in nasal cavities. These studies include pathological conditions like the impact of nasal septal deviation, nasal bone fracture impact, inferior turbinate hypertrophy, the post-surgery effect of the inferior turbinate, and post-endoscopy sinus surgery. However, the existing methodologies still need to evolve to be used as an aiding tool for the surgeon, specifically, as the framework to indicate the surgical or treatment correction and to act as a pre-surgical assessment platform.
On the other hand, in silico modeling approach for clinical trials, device development and clinical care have been widely adopted globally since 2018 after the US FDA’s congressional mandate. Further, the 2013 statistics report [10] from the FDA state that 38% to 75% of patients’ medication is ineffective for various diseases, from depression to cancer. One of the main reasons for ineffective drugs is inter-observation and intra-observation variabilities among patients; the human body is substantially more complex and unique. Even though personalized medicine is a rising field that aims to address or improve drug efficiency, there still exists a gap in drug delivery mechanisms and surgical treatment. Today’s growth in technology and computational power have enabled the personalization to understand the insight mechanistic and physiological facts in detail; digital twins are thus a nature (e.g., refer B. Marr, “What is digital twin technology and why is it so important?” Forbes, vol. Available online: https://www.forbes.com/sites/bernardmarr/2017/03/06/what-is-digital-twin-technology-and-why-is-it-soimportant/ (accessed on 2 July 2021)., 2017.; and M. N. Kamel Boulos and P. Zhang, “Digital twins: from personalized medicine to precision public health,” Journal of Personalized Medicine, vol. 11, no. 8, p. 745, 2021.). In addition, the COVID-19 pandemic period promoted distance care and digitization. Various industrial sectors have taken off the digitalization trend considering Digital Twin technology as part of the initiative due to its rich offerings, such as real-time reporting, risk-based assessment, and data-driven decision making, to improve efficiency, productivity, and profitability.
The healthcare and life science sectors hold great potential for positive human impact. For instance, in the life science sector, the in-silico model-based trials enable the virtual patients with digitally enabled organ cohort, which provide the same (clinical or experimental) degree of rigor and evidence, further reducing human enrollment by a sizable percentage. Additionally, the healthcare industry benefits by monitoring and predicting any resolving dilemma and evaluating or planning the procedure with a digital replica of the patient.
Even though the Digital Twin of humans, herein referred to as Digital BioTwin, has not been completely replicating the human, they have made significant progress. For example, “Living Heart” by the ‘Dassault system’ (e.g., refer “B. Baillargeon, N. Rebelo, D. D. Fox, R. L. Taylor, and E. Kuhl, “The living heart project: A robust and integrative simulator for human heart function,” European Journal of Mechanics - A/Solids, vol. 48, pp. 38–47, 2014.”) is the digital model of the heart used for discovering undeveloped illnesses and the pre-surgical platform for cardiac treatment or surgeries and testing implants. Thus, it is evident that the Digital Twin platform enables collaborative investigation of the various modules, deriving prediction hypotheses, integrating the physiology or pathological condition, and enlightening the mechanistic insight into an advanced visualization environment both in hospital and patient care.
The present disclosure provides a system and method that (a) mimics a Digital BioTwin of nose, b) assist in evaluating the pre and post-surgical procedure outcome, (c) validate surgical devices (e.g., new product devices (NPDs), and (d) training of surgeon for nasal procedure. Thus, the system and method of the present disclosure can enable pre-surgical planning, enabling the surgeon to assess the viability of a procedure option according to the patient’s specificity and choice of device, targeting personalized therapy.
Referring now to the drawings, and more particularly to FIG. 1 through 15, where similar reference characters denote corresponding features consistently throughout the figures, there are shown preferred embodiments and these embodiments are described in the context of the following exemplary system and/or method.
FIG. 1 depicts an exemplary system 100 for nasal virtual surgery for evaluating pre- and post-surgical outcomes, in accordance with an embodiment of the present disclosure. In an embodiment, the system 100 includes one or more hardware processors 104, communication interface device(s) or input/output (I/O) interface(s) 106 (also referred as interface(s)), and one or more data storage devices or memory 102 operatively coupled to the one or more hardware processors 104. The one or more processors 104 may be one or more software processing components and/or hardware processors. In an embodiment, the hardware processors can be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, and/or any devices that manipulate signals based on operational instructions. Among other capabilities, the processor(s) is/are configured to fetch and execute computer-readable instructions stored in the memory. In an embodiment, the system 100 can be implemented in a variety of computing systems, such as laptop computers, notebooks, hand-held devices (e.g., smartphones, tablet phones, mobile communication devices, and the like), workstations, mainframe computers, servers, a network cloud, and the like.
The I/O interface device(s) 106 can include a variety of software and hardware interfaces, for example, a web interface, a graphical user interface, and the like and can facilitate multiple communications within a wide variety of networks N/W and protocol types, including wired networks, for example, LAN, cable, etc., and wireless networks, such as WLAN, cellular, or satellite. In an embodiment, the I/O interface device(s) can include one or more ports for connecting a number of devices to one another or to another server.
The memory 102 may include any computer-readable medium known in the art including, for example, volatile memory, such as static random-access memory (SRAM) and dynamic-random access memory (DRAM), and/or non-volatile memory, such as read only memory (ROM), erasable programmable ROM, flash memories, hard disks, optical disks, and magnetic tapes. In an embodiment, a database 108 is comprised in the memory 102, wherein the database 108 comprises information Computed Tomography (CT) images of a nasal cavity of users. The database 108 further comprises three-dimensional (3D) models of the nasal cavity from the CT images, meshed geometry of the nasal cavity, simulated results, and the like. The memory 102 further comprises (or may further comprise) information pertaining to input(s)/output(s) of each step performed by the systems and methods of the present disclosure. In other words, input(s) fed at each step and output(s) generated at each step are comprised in the memory 102 and can be utilized in further processing and analysis.
FIG. 2 depicts an exemplary high level block diagram of the system 100 for nasal virtual surgery for evaluating pre- and post-surgical outcomes, in accordance with an embodiment of the present disclosure.
FIG. 3 depicts an exemplary flow chart illustrating a method for nasal virtual surgery for evaluating pre- and post-surgical outcomes, using the systems 100 of FIGS. 1-2, in accordance with an embodiment of the present disclosure. In an embodiment, the system(s) 100 comprises one or more data storage devices or the memory 102 operatively coupled to the one or more hardware processors 104 and is configured to store instructions for execution of steps of the method by the one or more processors 104. The steps of the method of the present disclosure will now be explained with reference to components of the system 100 of FIG. 1, the block diagram of the system 100 depicted in FIG. 2, and the flow diagram as depicted in FIG. 3.
At step 202 of the method of the present disclosure, the one or more hardware processors 104 obtain a Computed Tomography (CT) image of a nasal cavity of a user.
At step 204 of the method of the present disclosure, the one or more hardware processors 104 render a three-dimensional (3D) model of the nasal cavity from the CT image based on at least one of (i) a segmentation of an air passage region in the CT image of the nasal cavity based on one or more user inputs, and ii) an Artificial Intelligence (AI) based technique. The above steps 202 and 204 are better understood by way of following description:
The system 100 and method of the present disclosure adopted two sets of data for the analysis - a) an open-source 3D model from SimScale public project library (e.g., refer “SimScale, “simscale public project,” https://www.simscale.com. [Online] https://www.simscale.com/projects/”), which was used for further processing and analysis, and b) to evaluate the personalization of nasal cavity, the CT image collected from the open-source medical image repository is adopted (e.g., refer “3DSlicer, “Mr head, https://www.slicer.org/wiki/sampledata,” https://github.com/Slicer/SlicerTestingData/releases, commit=SHA256.”). The nasal CT image obtained from the open source, was imported into 3D Slicer (e.g., refer “A. Fedorov, R. Beichel, J. Kalpathy-Cramer, J. Finet, J.-C. Fillion-Robin, S. Pujol, C. Bauer, D. Jennings, F. Fennessy, M. Sonka, J. Buatti, S. Aylward, J. V. Miller, S. Pieper, and R. Kikinis, “3d slicer as an image computing platform for the quantitative imaging network,” Magnetic Resonance Imaging, vol. 30, no. 9, pp. 1323–1341, 2012.”), where the nasal airway was determined and exported as the healthy nasal model in STereoLithography (STL) file format ready for processing with the system 100. In subsequent phase of the method, to mimic the virtual surgery, the nasal cavity airway was increased by x% (e.g., say 10%). This process is a representation of removing the excessive mucosal thickening in real-life surgery by trained clinicians (e.g., refer “D. G. Becker, E. Ransom, C. Guy, and J. Bloom, “Surgical Treatment of Nasal Obstruction in Rhinoplasty,” Aesthetic Surgery Journal, vol. 30, no. 3, pp. 347–378, 05 2010.”); similar to the maxillary sinus, and mucous retention cysts are excised. The virtual surgical approach was adopted to compare the performance of the post-procedure model. In the present disclosure, the system 100 and method herein implemented 3D Slicer 4.11, an open-source tool to generate the nasal cavity model from a CT scan image (also referred as CT image of nasal cavity of the user) in DICOM (Digital Imaging and Communications in Medicine) file format. In the process of 3D model rendering from an imported CT image in 3D slicer, following steps are involved: i) Cropping: Using the ‘crop volume’ module, the region of interest, i.e., nasal airway passage sub volume, was cropped. This cropped sub-volume was further used for segmentation. ii) Segmentation: Using the ‘segment editor’, a new segment was added, and a threshold tool was applied to it. Setting a threshold range of -1024 to -299 HU (Hounsfield Units), obtained by heuristic approach, the CT volume was extracted. iii) Extraction: Further, to obtain the nasal airway passage, i.e., the nasal cavity 3D model, from this extracted CT volume, a few manual steps were performed using tools like paint, draw, scissors, and islands. iv) Verification: The nasal cavity 3D model was visually verified and smoothed to remove any small holes and/or spikes using ‘a Gaussian smoothing filter’, and finally, exported as a Standard Tessellation Language (STL) file. The human nose 3D obtained from the SimScale public project library was considered for the airflow analysis and structural — the 3D nasal cavity model obtained from the SimScale project (as shown in FIG. 4B) is used to assess the airflow. FIG. 4A, with reference to FIGS. 1-4, depicts a 3D model rendering of the nasal cavity from the CT image in slicer as implemented by the system 100 of FIG. 1, in accordance with an embodiment of the present disclosure. FIG. 4B, with reference to FIGS. 1-4A, depicts a Nasal 3D model of a user, in accordance with an embodiment of the present disclosure. FIGS. 4A and 4B are collectively referred as FIG. 4.
At step 206 of the method of the present disclosure, the one or more hardware processors 104 performs a plurality of steps. More specifically, at 206a of the method of the present disclosure, the one or more hardware processors 104 mesh the rendered 3D model of the nasal cavity to obtain a meshed geometry of the nasal cavity. The meshed geometry of the nasal cavity comprises a plurality of cell identifiers (IDs) associated with a plurality of grids. In an embodiment of the present disclosure, the meshed geometry of the nasal cavity is based on a first edge length and a second edge length being imposed on a plurality of surfaces of the 3D model of the nasal cavity of the user The above step 206 is better understood by way of following description:
Prior to the mesh generation on ANSYS (ICEM CFD 19.2), the STL file of the 3D nasal cavity model is imported to this tool for the preprocessing step that removes the unwanted edges and fills up holes. The boundary surfaces are identified as inlet, outlet, and cavity walls in the next step. Then, a thorough geometry check is performed using a ‘Build Diagnostic Topology’ filter with the default settings, which checks the surface connectivity, gaps, etc., to ensure a water-tight geometry. Now, the model is ready for discretization, and an unstructured mesh with 0.48 million tetrahedral elements is generated (see FIGS. 5A-5B) using the ‘Octree-based method’. For generating this mesh, a minimum edge length of 2 mm (e.g., say the first edge length) and the maximum edge length of 20 mm (e.g., say the second edge length) criteria were imposed on all the surfaces. After performing various mesh quality checks, it is then exported to ANSYS Fluent 19.2 and OpenFoam v1812 in compatible formats to carry out simulations. FIGS. 5A and 5B, with reference to FIGS. 1 through 4B, depict a sagittal view and a tilted sagittal view, showing boundary surfaces, respectively, in accordance with an embodiment of the present disclosure. FIGS. 5A and 5B are collectively referred as FIG. 5.
At step 206b of the method of the present disclosure, the one or more hardware processors 104 perform a mesh independent analysis on the meshed geometry of the nasal cavity to obtain first simulated nasal mesh model. The mesh independent analysis comprises performing a velocity gradient optimization technique of the plurality of grids, with a dynamic velocity gradient threshold determination (e.g., a technique as known in the art) to obtain one or more optimized gradient values. Further, one or more grids from the plurality of grids are split based on the one or more optimized gradient values to obtain a refined mesh. Furthermore, the refined mesh is smoothed using one or more smoothing techniques to obtain a smoothened mesh. The one or more smoothing techniques are smoothing techniques known in the art. The smoothened mesh of the nasal cavity is simulated to obtain the first simulated nasal mesh model.
At step 206c of the method of the present disclosure, the one or more hardware processors 104 determine one or more quality parameters of the first simulated nasal mesh model. In the present disclosure, the one or more quality parameters comprise, but are not limited to, at least one of a skewness, an aspect ratio, and a non-orthogonality of the simulated nasal mesh model.
The above steps 206b and 206c are better understood by way of following description:
Conventionally, studies have reported the importance of the number of mesh elements, as it plays a critical role in local and global parameter convergence such as velocity, pressure drop, and wall shear stress, respectively (e.g., refer 1) “K. Inthavong, et. al,; 2) C. M. King Se, K. Inthavong, and J. Tu, “Inhalability of micron particles through the nose and mouth,” Inhalation Toxicology, vol. 22, no. 4, pp. 287–300, 2010.; 3) X. Li, K. Inthavong, and J. Tu, “Particle inhalation and deposition in a human nasal cavity from the external surrounding environment,” Building and Environment, vol. 47, pp. 32–39, 2012. 4) S. Wang, K. Inthavong, J. Wen, J. Tu, and C. Xue, “Comparison of micron-and nanoparticle deposition patterns in a realistic human nasal cavity,” Respiratory physiology & neurobiology, vol. 166, no. 3, pp. 142–151, 2009.; 5) X. B. Chen, H. P. Lee, V. F. H. Chong, and D. Y. Wang, “A computational fluid dynamics model for drug delivery in a nasal cavity with inferior turbinate hypertrophy,” Journal of aerosol medicine and pulmonary drug delivery, vol. 23, no. 5, pp. 329–338, 2010; 6) K. Inthavong, Z. F. Tian, J. Tu, W. Yang, and C. Xue, “Optimising nasal spray parameters for efficient drug delivery using computational fluid dynamics,” Computers in biology and medicine, vol. 38, no. 6, pp. 713–726, 2008; and 7) D. O. Frank-Ito, M. Wofford, J. D. Schroeter, and J. S. Kimbell, “Influence of mesh density on airflow and particle deposition in sinonasal airway modeling,” Journal of aerosol medicine and pulmonary drug delivery, vol. 29, no. 1, pp. 46–56, 2016.”). For instance, findings in Inthavong et al. showed the influence of line location in the line profile results. In this line, the system 100 has performed the mesh independence study to investigate the suitability of the defined mesh. Here, to generate a more refined mesh with prism layers, an OpenFoam utility cfMesh tool was implemented by the system 100 and method of the present disclosure. In addition to 0.48 million elements meshing, three additional meshes approaches were generated to evaluate the mesh independence. For generating different meshes, a few parameters, such as minimum and maximum element size, were varied. The three meshes referred to as Mesh-1, Mesh-2, Mesh-3, and Mesh-4 were with and without prism layers ranging from 1.8-6.5 million, as shown in FIG. 6. More specifically, FIG. 6, with reference to FIGS. 1 through 5B, depicts a representation of nasal model showing coronal slices (pattern hatching) in the left most panel, in accordance with an embodiment of the present disclosure. Other panels show the slice-1 of mesh 1 - 4 and the zoomed-in views of their tip regions.
The statistics used for generating the four mesh along with mesh quality are summarized in Table 1, which includes minimum and maximum element size, number of prism layers, Height of first prism layer, Total Mesh size, Maximum Skewness, and Maximum Aspect Ratio. The thickness of the prism layer was so decided that the boundary layer thickness observed in velocity contours is well within the prism layer height. For all three meshes, the first cell height and number of prism layers were kept constant so that the boundary layer effect is not compromised.
Table 1
Discretization parameters (e.g., quality parameters) for the mesh independence study/analysis.
Parameters Mesh 1 Mesh 2 Mesh 3 Mesh 4
Minimum Element Size (mm) 2 1.6 1.2 0.8
Maximum Element Size (mm) 20 20 10 8
Number of prism layers No prism 6 6 6
Height of 1st prism layer (mm) No 0.08 0.08 0.08
Number of elements (million) 0.48 1.8 3.9 6.5
Maximum Skewness 2.92 3.33 3.32 3.38
Maximum Aspect Ratio 108.6 74.8 41.6 36.3

FIG. 13, with reference to FIGS. 1 through 12, depicts a velocity contour representation in the coronal cross-sectional view for different meshes at (a) slice-1 (for left and right nasal cavities) and (b) slice-2 (for the shared region of both cavities), in accordance with an embodiment of the present disclosure.
At step 206d of the method of the present disclosure, the one or more hardware processors 104 regenerate a nasal cavity mesh based on the one or more determined quality parameters to obtain a regenerated nasal cavity mesh. The above description of meshing the nasal cavity model described in step 206a, including Table 1 and FIGS. 5A and 5B may be referred for step 206d.
At step 208 of the method of the present disclosure, the one or more hardware processors 104 simulate, until a convergence is met, the regenerated nasal cavity mesh by solving one or more associated governing equations based on one or more setup parameters to obtain a first simulated nasal model. In the present disclosure, the convergence is obtained by a Machine Learning (ML) technique (e.g., a ML based regression as known in the art). However, such techniques shall not be construed as limiting the scope of the present disclosure. The one or more setup parameters comprise, but are not limited to, at least one of air flow rate at one or more inlets of the nasal cavity, and a selection of a turbulence model based on a Reynolds numbers, a nasal wall, an outlet boundary condition, a selection of one or more discretization schemes/parameters. The above step of 208 may be better understood by way of following description:
The governing equations for the flow modeling include the time-independent continuity equation, momentum balance equations, energy equation and species transport equation of the mass fraction of water as given below:
continuity equation:
(?(?u))/?x+(?(?v))/?y+(?(?w))/?z=0 (1)
momentum balance equation:
(?(?u^2))/?x+(?(?uv))/?y+(?(?uw))/?z=-?(p)/?x+?(t_xx )/?x+?(t_xy )/?y+?(t_xz )/?z+?f_x (2a)
(?(?uv))/?x+(?(?v^2))/?y+(?(?vw))/?z=-?(p)/?y+?(t_xy )/?x+?(t_yy )/?y+?(t_yz )/?z+?f_y (2b)
(?(?uw))/?x+(?(?vw))/?y+(?(?w^2))/?z=-?(p)/?z+?(t_xz )/?x+?(t_yz )/?y+?(t_zz )/?z+?f_z (2c)
where ? is the density, u, v, and w are the velocities in x, y, and z - directions, respectively. p and f are the pressure and body force on the fluid, respectively. For the analysis in the turbulent regime, the system 100 considered the k-epsilon turbulence model, which consists of the turbulent kinetic energy equation and its dissipation equation as:
(?(?ku_j))/(?x_j )=?/(?x_j ) [(µ+µ_t/s_k ) ?k/(?x_j )]+G_k+G_b-?e-Y_M+S_k (3a)
(?(?eu_j))/(?x_j )=?/(?x_j ) [(µ+µ_t/s_e ) ?e/(?x_j )]+?C_1 S_e-?C_2 e^2/(k+v?e)+C_1e e/k C_1e G_b+S_e (3b)
respectively, where C_1=max?[0.43,?/((?+5) )], ?=Sk/e, and S=v(2S_ij S_ij ). In equations (3a) and (3b) G_k and G_b are the turbulent kinetic energy generation terms due to the mean velocity gradients and buoyancy, respectively; Y_M is the contribution to the overall dissipation rate due to the fluctuating dilatation in compressible turbulence. The s_k and s_e are the turbulent Prandtl numbers for k and e, respectively; S_k and S_e are user-defined source terms, and C_1e and C_2 are constants. The eddy viscosity is defined as µ_t=(?C_µ k^2)/e, where C_µ is the coefficient for the turbulent viscosity.
For the estimation of the temperature profile and the heat flux inside nasal cavity, the energy balance equation is solved. The conversation form of the energy equation in terms of total energy (e+V^2/2) is given as:
?.[?(e+V^2/2)V]=?q+?/?x (k (?(T))/?x)+?/?y (k (?(T))/?y)+?/?z (k (?(T))/?z)-?(up)/?x-?(vp)/?y-?(wp)/?z+?(ut_xx )/?x+?(ut_yx )/?y+?(ut_zx )/?z+?(vt_xy )/?x+?(vt_yy )/?y+?(vt_zy )/?z+?(wt_xz )/?z+?(wt_yz )/?y+?(wt_zz )/?z+?f.V, (4)
where e is the internal energy and V^2/2 is the kinetic energy, k is the thermal conductivity and T is the temperature. The estimation of humidity inside the nasal cavity requires solving mass transport equation, which is given as:
?(?uY_i )/?x+?(?vY_i )/?y+?(?wY_i )/?z=-?(J_i )/?x-?(J_i )/?y-?(J_i )/?z (5)
where,
J_i=-?D_(i,m) ?Y_i-D_(T,i) ((?T))/T (6)
Y_i is the mass fraction of species i, which is water in this case. J_i is the species i diffusion flux, which arises due to gradient of temperature and concentration. Here D_(i,m) and D_(T,i) are the mass diffusion coefficient and the thermal diffusion coefficient of species i.

Boundary Conditions:
For the simulations, the system 100 and method considered steady-state, incompressible, and single-phase flow to study/analyse the dynamics of flow in the nasal cavity. The simulations were performed at different breathing conditions, as shown in Table 2 below. More specifically, Table 2 depicts airflow rates at different breathing levels.
Table 2
Airflow rates at different breathing levels
Breathing level Airflow rates (L/min) Reynolds number Flow regime
Calm 10 694 Laminar
Physical exercise 40 2782 Turbulent
Extreme case 150 5214 Turbulent

For an adult, at normal breathing conditions, the inspiratory nasal airflow falls in the range of 5-12 L/min; while in physical exercise, it is ~ 40 L/min; and in an extreme case, the airflow rate is ~ 150 L/min (e.g., refer “G. B. R. Wang De Yun, Lee Heow Peuh, “Impacts of fluid dynamics simulation in study of nasal airflow physiology and pathophysiology in realistic human three-dimensional nose models,” Clin Exp Otorhinolaryngology, vol. 5, no. 4, pp. 181–187, 2012.”). For these flow rates, at the inlet, the Reynolds number is in the range of 694-5214. Therefore, for the flow rate of 10 L/min, the laminar model is used, and the system 100 used k-? turbulence model for the flow rates of 40 L/min (e.g., refer “K. Inthavong, J. Wen, J. Tu, and Z. Tian, “From CT scans to CFD modelling – fluid and heat transfer in a realistic human nasal cavity,” Engineering Applications of Computational Fluid Mechanics, vol. 3, no. 3, pp. 321–335, 2009.”).
In this present disclosure, the system 100 and the method described herein assume air as an incompressible, single-phase fluid medium with the density of 1.225 kg/m3 and the viscosity of 1.78 × 105 kg/(m s). A uniform flow normal to the inlet surface is applied at the inlets for the flow rates as mentioned in Table 2. The volumetric flow rate at the inlet is maintained at 1.667 × 10-4 m3/s, the outlet is set to a pressure boundary condition of zero Pa, and the no-slip condition is imposed on the remaining walls.
Temperature and humidity of the air inside the nose play important roles in physiological processes. The inhaled air passing through the nasal cavity becomes humidified and warm before reaching the lungs. To model nasal air conditioning and understand the effect of inhaled air temperature and humidity two climatic conditions were considered. These two climatic conditions are chosen such that the validation can be made with the reported results in the literature (e.g., refer “K. Inthavong, J. Wen, J. Tu, and Z. Tian, “From CT scans to CFD modelling – fluid and heat transfer in a realistic human nasal cavity,” Engineering Applications of Computational Fluid Mechanics, vol. 3, no. 3, pp. 321–335, 2009.”).
In the first condition, i.e., normal climatic condition, the inhaled air temperature is 25oC and the Relative Humidity (RH) is 35 %. At the RH of 35 % and 25oC, the mass fraction of water in the air is 0.684 %. In this study, we assumed the temperature at the nasal wall is equal to body temperature, i.e., 33.5oC. The nasal mucosal wall is assumed to be fully saturated; as reported by Inthavong et al, 2009(e.g., refer “K. Inthavong, J. Wen, J. Tu, and Z. Tian, “From CT scans to CFD modelling – fluid and heat transfer in a realistic human nasal cavity,” Engineering Applications of Computational Fluid Mechanics, vol. 3, no. 3, pp. 321–335, 2009.”). At the RH of 100 % and 33.5°C, the mass fraction of water in air is 3.24 %.
In the second condition, a cold climatic condition, the system 100 and method examined the effect of ambient Cold Dry Air (CDA) on the properties of air inside nose. In this case, the temperature of inhaled air was 12oC and the RH was 13 %, and the corresponding mass fraction of water was 0.112 %. The nasal cavity wall temperature was assumed to be 32.1oC with RH 100 %.

Solver Settings:
The simulations were performed using Ansys Fluent 19.2 and OpenFOAM v1812 software packages. Table 3 gives the details of the simulation settings used for the simulations.
Table 3
Summary of simulation settings for Ansys Fluent and OpenFOAM
Simulation Settings ANSYS Fluent 19.2 OpenFoam v1812
Boundary Conditions
Inlets Mass Flow inlet FlowRateInletVelocity
Outlet Pressure Outlet InletOutlet
Nasal Cavity wall No slip No slip
Numerical Methods
Solver Pressure Based steady State simpleFoam
Pressure Velocity coupling SIMPLE SIMPLE
Under-Relaxation Factors Pressure – 0.3 Pressure – 0.3
Momentum- 0.3 Momentum- 0.7
Turbulence – 0.3 Turbulence – 0.3
Numerical Schemes
Gradient Least squares cell based LeastSquares
Pressure Second Order LinearUpwind
Momentum Second Order Upwind LinearUpwind
Energy Second Order Upwind LinearUpwind
Interpolation Second Order Upwind LinearUpwind
To allow the comparison of the simulated results, most of the simulation settings in these two software packages were maintained the same; only a few settings appear different (cf. Table 3) due to the constraints in this software. The solutions were assumed to attain convergence based on the residual convergence criteria of 10-6.
Once the simulation is done to output the simulated result, the one or more hardware processors 104 are further configured by the instructions to generate a Virtual Reality (VR) enabled visualization of the nasal cavity with a physical feedback using one or more haptic techniques. The one or more hardware processors 104 further determine (i) an aerodynamic (air – respiration), a hydrodynamic, a (fluid) flow distribution, (ii) a characteristic behavior of one or more surgical equipment, and one or more prosthetics using the Virtual Reality enabled visualization.
Further, the one or more hardware processors 104 are configured by the instructions to determine a requirement for a nasal procedure requirement based on at least one of (i) the aerodynamic (air – respiration), the hydrodynamic, the flow distribution, (ii) the characteristic behavior of one or more surgical equipment, and one or more prosthetics, and (iii) the training of the nasal procedure (e.g., secondary and territory training).
In one scenario, the nasal module functions as an Injury prevention tool: the system 100 recommends optimal parameters for each procedure in the pre-surgical phase, while using catheters, balloon catheters, tissue cutting, suction or irrigation and biopsy. For example, in the case of balloon sinus ostial dilation (BSOD) procedure to widen the nasal pathway, it could recommend the bust pressure and its impacts such as breathing pathway profile, velocity, and stress. As a virtual surgery platform, it aids the physician in choosing the best option for patient therapy. On the other hand, say, endoscopic sinus surgery; the system 100 facilitates testing of the nasal cavity in a dynamic nature by varying its parameters such as pressure, velocity, fluid media with and without barriers, which is impossible with a human.
In another scenario, nasal module function as early detection of surgical impact: In the post-surgical analysis, detect the impact of surgery such as bone fractures and so, using the radiology image. Thereby the system 100 improves the surgeon's confidence and serves as digital evidence. As a post-surgical platform, it determines the modification of structural or flow path or foreign particle and estimates the nasal outcome parameter.
In yet another scenario, to improve the precision of digital replica of respiration or airflow, the Nose module coupling or superimposed with nasal module to account for the transient that aid in assisting and evaluating the surgical outcome. To be more specific, for every change in the morphological structure, the respiration or breathing profile and characteristics are quantitative measured in the combination of Nose on top of the nasal cavity, a digital replica of respiration. Thereby DBT improves the surgeon's confidence and serves as digital evidence. As a post-surgical platform, it determines the modification of structural or flow path or foreign particle and estimates the nasal outcome parameter.
A further scenario is NPD in the ENT/Med Tech space. The system 100 reduces the production time cycle by providing a virtual test environment and digital evidence for regulatory bodies. During the V&V phase, the NPD functional and structural specification is integrated with sensor input. Thereby, it could quantitatively measure and visually appreciate the airflow pattern (laminar or turbulent), velocity, pressure, wall shear stress, particle deposition, and temperature changes at different flow rates in different parts of the nasal cavity.
Once the nasal procedure requirement is determined, then a specific region of the nasal cavity of the user is modified based on the determined requirement (e.g., refer comparative study/analysis of Pre-surgical and Post-surgical Models described below with FIG. 13 which depicts pressure drop as a function of flow rate for the pre-surgical and post-surgical models (a) left and (b) right nasal cavities, and Table 5 illustrating flow resistances for the pre-surgical and post-surgical nasal models.
Further, a local meshing of the first simulated nasal mesh model is performed during the nasal procedure based on the reduced region of the nasal cavity to obtain a second simulated nasal model. The local meshing comprises repeating the step of regenerating the nasal cavity mesh; and performing a comparison of the first simulated nasal model and the second simulated nasal model to obtain a performance report of the nasal procedure (e.g., refer meshing of the 3D Model, mesh independence validation, and the like). The performance report for example, may include, but not limited to determining how much of the nasal procedure or surgical device being operated is causing an inconvenience to the user (e.g., say air passing, blocking, use and flexibility and performance of the surgical device, and the like). FIG. 4 as described above which illustrates nasal cavity mesh (a) sagittal view and (b) a tilted sagittal view, showing boundary surfaces, and comparative study/analysis of Pre-surgical and Post-surgical Models described below).
Simulation Result Visualization
The post-processing of the method and system 100 of the present disclosure for the pressure drop, velocity components, and wall shear was carried out using the ParaView v5.10.1 or other kind of 3D visualization tool. Further, the postprocessed data was transferred for advanced visualization (AR) using Unity.
The above steps of AR or VR enabled visualization, determination, and nasal procedure requirement, are better understood by way of following description:
RESULTS
Various commercial software, such as Ansys Fluent, COMSOL Multiphysics, PowerFLOW, Autodesk CFD, Altair® HyperWorks®, SimScale, SimulationX, PIPESIM, and opensource software, e.g., OpenFOAM, are available for the fluid flow modeling. In the present disclosure, simulations were performed using Ansys Fluent and OpenFOAM for various flow rates as mentioned in Table 2, and the analysis has been reported for literature (e.g., refer 1) U.S.FDA. Paving the way for personalized medicine-fda’s role in a new era of medical product development; u.s. food and drug administration: Silver spring, MD, USA, 2013. Available online: https://www.fdanews.com/ext/resources/files/10/10-28-13-Personalized-Medicine.pdf (accessed on 2 July 2021).; and 2) K. Inthavong, J. Wen, J. Tu, and Z. Tian, “From CT scans to CFD modelling – fluid and heat transfer in a realistic human nasal cavity, “Engineering Applications of Computational Fluid Mechanics, vol. 3, no. 3, pp. 321–335, 2009)
Model Validation
For the validation of models of the system 100 and the method of the present disclosure, first, the pressure drop across the nasal cavity has been compared with the simulated results and with reported experimental results in the literature in FIG. 7. More specifically, FIG. 7, with reference to FIGS. 1 through 6, depicts a comparison of pressure drop across the nasal cavity at different flow rates among the simulated results and the experimental values reported in the literature, in accordance with an embodiment of the present disclosure. This result infers that the pressure drop increases with the increase in flow rate. The comparison of the Ansys Fluent and OpenFOAM simulated results has been observed that the relative difference in pressure drop remains below 6.3%, which is for the flow rate of 10 L/min. In the other case, when the flow is higher, i.e., at 40 L/min, the pressure drop difference between other methods further decreases towards ~ 5%. Secondly, the simulated outcomes have been compared with the reported experimental pressure drop by literature (e.g., refer “J. T. Kelly, B. Asgharian, J. S. Kimbell, and B. A. Wong, “Particle deposition in human nasal airway replicas manufactured by different methods. part i: Inertial regime particles,” Aerosol Science and Technology, vol. 38, no. 11, pp. 1063–1071, 2004.”) and that from the simulations of literatures (e.g., refer “I. Weinhold and G. Mlynski, “Numerical simulation of airflow in the human nose,” European Archives of Oto-Rhino-Laryngology and Head & Neck, vol. 261, no. 8, pp. 452–455, 2004.; and K. Inthavong, J. Wen, J. Tu, and Z. Tian, “From CT scans to CFD modelling – fluid and heat transfer in a realistic human nasal cavity,” Engineering Applications of Computational Fluid Mechanics, vol. 3, no. 3, pp. 321–335, 2009.”). From the comparison in FIG. 7, it can be observed that the results from the model of the present disclosure agree well with the reported experimental results (e.g., refer J. T. Kelly, et. al). At lower flow rates, the results of the present disclosure match well with the reported simulated pressure drop (e.g., refer “I. Weinhold et. al and K. Inthavong et. al”), but a significant difference can be observed at higher flow rates. From this comparison, it can be shows that the methods findings are significantly agreeing well with the experiment data reported (e.g., refer “I. Weinhold et. al and K. Inthavong et. al”), specifically at higher flow rates, the model of the present disclosure over predicts the pressure drop.
FIG. 9 shows the contour plots of flow velocity magnitude in coronal, axial, and sagittal views of the nasal cavity for a flow rate of 10L/min. More specifically, FIG. 9, with reference to FIGS. 1 through 8, depicts a velocity magnitude contour plot at 10L/min, in accordance with an embodiment of the present disclosure. Panels in FIG. 9 represent the airflow in (a) coronal view, (b) axial view, and (c) sagittal view. The observed complexity in the flow is mainly due to the intricate geometry of the nasal cavity. The available cross-sectional area for flow at the locations along the flow path plays a crucial role in defining the velocity profile. The cross-sectional area is minimum near the region of the nasal valve, and it is narrowest in the nasal valve. The cross-sectional area starts to expand in the anterior turbinate region and keeps increasing until reaching the choanae. Beyond choanae, the cross-sectional area starts to reduce, and the nasopharynx provides a narrower shared region for both the nasal cavities.
From the contour plots, it can be observed that the airflow attains the most significant velocity near the nasal valves due to the smallest available cross-sectional area, as represented in the leftmost part of FIG. 9 (b and c). At the end of this region, airflow takes two different routes to flow further: a significant fraction flowing upward and the other fraction moving downward. At the anterior turbinate region, a reduction of flow magnitude is observed due to the larger cross-sectional area for the flow FIG. 9 (a). As the major available area for the flow is localized around the central region of this slice, the resistance to the flow is most minor in this zone, and thereby the developed flow achieves a relatively larger magnitude. A significant difference in flow magnitude can be observed between the left (right to the reader) and right (left to the reader) nasal cavities, as seen in the coronal cut and the left ends of the axial cut sections. This difference is due to the distributed flow area with lower resistance for the left cavity, which spreads out from the bottom of the coronal cut FIG. 9 (a) and covers until the bottom of the olfactory region. This outcome is similar to the observation reported by Plasek et al., (e.g., refer “M. Pl´a?sek, M. Mas´arov´a, M. Bojko, P. Kom´inek, P. Matou?sek, and M. Form´anek,“Computational fluid dynamics could enable individualized surgical treatment of nasal obstruction (a preliminary study),” Diagnostics, vol. 12, no. 11, 2022.”), which was further supported by Zhao et al., (e.g., refer “K. Zhao and J. Jiang, “What is normal nasal airflow? a computational study of 22 healthy adults,” International Forum of Allergy & Rhinology, vol. 4, no. 6, pp. 435–446, 2014.”). Thus, it can be claimed that there is no general airflow pattern in various regions of the nasal cavity; in fact, significant inter-individual variations can be observed due to the dissimilarity in the geometry of the nasal cavity of individuals. Further, a study by Simmen et al., (e.g., refer “D. Simmen, J. L. Scherrer, K. Moe, and B. Heinz, “A Dynamic and Direct Visualization Model for the Study of Nasal Airflow,” Archives of Otolaryngology–Head & Neck Surgery, vol. 125, no. 9, pp. 1015–1021, 09 1999.”) suggested that the middle meatus and common meatus regions allow most of the airflow.
Towards the posterior turbinate region, due to the larger available cross-sectional area, a lower magnitude of the velocity profile is seen to be almost uniformly distributed across the flow area (towards the right of FIG. 9(b) and the middle part of FIG. 9(c). Later, due to a reduction in the available cross-sectional area for flow, a slight positive gradient in fluid velocity is observed in the nasopharynx region (see the lower right part in FIG. 9(c)). The magnitude of the flow in this region is intermediate to the higher magnitude near the nasal valve and the lower magnitude at the turbinate region.
The pressure profile for the flow inside the nasal cavity is shown in the same coronal, axial, and sagittal views by a contour plot in FIG. 8. More specifically, FIG. 8, with reference to FIGS. 1 through 7, depicts a pressure contour plot at 10L/min, in accordance with an embodiment of the present disclosure. In FIG. 8, panels represent pressure in (a) coronal view, (b) axial view, and (c) sagittal view. The presence of a nasal wall poses a solid resistance to the flow; therefore, a large pressure gradient builds up between the ambient and the nostril. Further, the sudden increase in the cross-sectional area causes a significant pressure drop in the anterior turbinate region. FIG. 8 represents grey-colored regions towards the front of the sagittal view, axial cut section’s view, and the central part of the coronal cut section view. Except for the anterior turbinate region, a slight positive gradient in the pressure profile is observed due to the negative velocity gradient. Beyond the posterior turbinate, a significantly sizeable positive velocity gradient causes a negative gradient in the pressure profile. The increasing intensity of the shade (e.g., say grey color) in the nasopharynx region shows this gradual decrease in the pressure profile.
Further the model of the present disclosure is used to simulate the temperature and humidity profiles inside nose at the climatic conditions as described above; these conditions are namely the normal climatic condition and the cold dry air condition. The average temperature and average relative humidity over different cross-sections in coronal view along the equi-spaced normalized nasal flow path are estimated and plotted in FIG. 10A and 10B, respectively. More specifically, FIG. 10A and 10B, with reference to FIG. 1 through 9, depict Average (a) temperature and (b) relative humidity profiles across the nasal cavity at different cross-sections, in accordance with an embodiment of the present disclosure. The distance along the nasal path is normalized by the length of the full path, i.e., 97 mm. In these plots, the reported results for these two parameters are plotted for comparison.
The inhaled air temperature increases rapidly within 35 mm from the inlet in the anterior region of the nasal cavity. The temperature changes by 4.2oC within the nasal valve area, i.e., a short distance of 16 mm from the inlets. Beyond the distance of ~ 35 mm the temperature almost remains steady and the converges to 31.8oC for the air with the normal climatic condition. This observation indicates a high rate of heat transfer between inhaled air and the nasal wall in the frontal region where the cross-sectional area is smaller; while in the posterior region, the rate of heat transfer decreases as the temperature difference between the air and the nasal wall reduces. A similar trend is observed in the literature through simulation and experiment (e.g., refer “K. Inthavong, J. Wen, J. Tu, and Z. Tian, “From CT scans to CFD modelling – fluid and heat transfer in a realistic human nasal cavity,” Engineering Applications of Computational Fluid Mechanics, vol. 3, no. 3, pp. 321–335, 2009.”).
The relative humidity profile follows the similar trend as the temperature profile. In this case, the relative humidity increased rapidly within the distance of ~ 35 mm from the inlet, and then attains a steady value. The inhaled air sufficiently humidifies to ~ 85 % before reaching pharynx. Similar trends are followed for the temperature and the relative humidity for the incoming air with cold dry air condition.
From the comparison in FIG. 10A and 10B, it can be observed that the simulated results from the proposed CFD method matches well with the experimental results reported in the literature (e.g., refer “K. Inthavong, J. Wen, J. Tu, and Z. Tian, “From CT scans to CFD modelling – fluid and heat transfer in a realistic human nasal cavity,” Engineering Applications of Computational Fluid Mechanics, vol. 3, no. 3, pp. 321–335, 2009.”) for both the temperature and relative humidity. However, a substantial deviation can be observed between the simulated results in proposed CFD model and the simulated results in the literature (e.g., refer “K. Inthavong, J. Wen, J. Tu, and Z. Tian, “From CT scans to CFD modelling – fluid and heat transfer in a realistic human nasal cavity,” Engineering Applications of Computational Fluid Mechanics, vol. 3, no. 3, pp. 321–335, 2009.) for the relative humidity in the posterior region of the nasal cavity.
In the cold climatic condition, the temperature and relative humidity profiles are estimated separately for the left and right cavities and plotted in FIGS. 10A and 10B, respectively. In the anterior region of the nasal cavity, the left cavity shows a relatively higher temperature in comparison to the right cavity except at the region close to the entrance, ~ 7 mm inside from inlets. Whereas, in the posterior region, the temperature of the right cavity is higher than that of the left cavity; a similar trend is reported in the literature (e.g., refer “K. Inthavong, J. Wen, J. Tu, and Z. Tian, “From CT scans to CFD modelling – fluid and heat transfer in a realistic human nasal cavity,” Engineering Applications of Computational Fluid Mechanics, vol. 3, no. 3, pp. 321–335, 2009.”). In the posterior region, a temperature difference is observed between the left and right cavities as well. Though, the reported results (e.g., refer “K. Inthavong, J. Wen, J. Tu, and Z. Tian, “From CT scans to CFD modelling – fluid and heat transfer in a realistic human nasal cavity,” Engineering Applications of Computational Fluid Mechanics, vol. 3, no. 3, pp. 321–335, 2009.”) does not show any difference in temperature between the cavities; this is possibly due to inter-individual variation. A similar trend was observed for the relative humidity of the air inside the nasal cavity. These observations show that even in cold dry climatic condition the inhaled air undergoes temperature and humidity conditioning before it reaching pharynx, and in this process, the nasal mucosal wall plays an important role.
FIG. 11 shows the contour plots of the temperature field for the air with cold climatic conditions at the different sections in the coronal view along the nasal cavity. More specifically, FIG. 11, with reference to FIG. 1 through 10B, depicts temperature contour plots at different cross-sections across the nasal cavity for the air with cold climatic condition, in accordance with an embodiment of the present disclosure. From the contour plots, it can be observed that the temperature is higher near the nasal wall and lower at the central region of the cross-sections. In the uppermost region of the sections (b) and (c), the temperature is higher relative to that of the rest of the sections. The air becomes hot with increasing distance from the nostrils by absorbing the heat from the nasal mucosal wall, and the temperature changes primarily takes place in the anterior region of the nasal cavity. Near the pharynx region, the cold inhaled air heats up and reaches close to the nasal mucosal wall temperature and a significant uniformity in the temperature across a section can be seen.
To understand the heat transfer in nasal cavity, the heat flux distribution is mapped on the surface of the nasal cavity as shown in FIG. 12. More specifically, FIG. 12, with reference to FIG. 1 through 11, depicts contour plots of wall heat flux on the surface of nasal cavity for the air with cold climatic condition, in accordance with an embodiment of the present disclosure. In the anterior region, higher heat flux is observed due to larger temperature difference between the inhaled air and nasal wall. In the posterior region, as the temperature difference reduces substantially, the heat flux is very less that leads to a negligible temperature rise in that region.
Mesh Independence Validation
The simulation was performed using the same settings with a laminar flow model for all four meshes. Furthermore, to understand the sensitivity of meshing, the velocity profile at different cut sections and pressure drop across the nasal cavity have been compared. FIG. 13, with reference to FIG. 1 through 13, depicts velocity contour representation in the coronal cross-sectional view for different meshes at (a) slice-1 (for left and right nasal cavities) and (b) slice-2 (for the shared region of both cavities), in accordance with an embodiment of the present disclosure. More specifically, FIG. 13(a) shows the velocity contour at coronal cut section-1. At the bottom of the right nasal, it is observed that a recirculation flow is captured in mesh 3 and 4, while the flows are dispersed at the exact location in mesh 1 and 2. This recirculation flow could be an outcome of turbulent flow regimes characterized by the narrowing wall. Without prism meshing, our findings are well aligned with Inthavong et al., (e.g., refer “K. Inthavong, A. Chetty, Y. Shang, and J. Tu, “Examining mesh independence for flow dynamics in the human nasal cavity,” Computers in Biology and Medicine, vol. 102, 09 2018.”). However, it contradicts when the system 100 considers the prism layer as shown in FIG. 12(a (mesh-3 and mesh-4)). Specifically, Inthavong et al. remark that the velocity line profile and contour planes with narrow passage did not vary significantly between all meshed methods; in the method of the present disclosure, it shows significant change and induces local turbulent flow. Also, in the region of maximum flow, i.e., the central area of coronal cut section—1, the velocity gradient near the wall is sharply captured with prism layers for mesh 2-4 compared to mesh-1 without prism layers. At the coronal cut section-2, as shown in FIG. 13(b), where the left and right nasal cavities are combined, variation in velocity profile is observed for all four mesh. This finding is well aligned with Inthavong et al.’s outcome, remarking that larger passage cross-section regions are prone to more significant variation in flow behavior, irrespective of meshed models. The Inthavong et al. also mentioned that this variation could be due to the increased mixing of two streams of fluids, i.e., flow from the left and right nasal cavities. Thus, with high confidence, a refined mesh is required for capturing the velocity profile accurately, specifically at the larger cross-sectional region.
Further, a pressure drop parameter has been used as a metric to evaluate the mesh independence analysis. The pressure drop across the nasal cavity has been measured for each mesh and compared with reference mesh-4 (6.5 million mesh size) as represented in Table 4 below.
Table 4
Pressure drops as a parameter for mesh independence analysis
Mesh type Mesh Size (million) ?Pressure drop (%) w.r.t mesh 4
Mesh 1 0.48 10.2 %
Mesh 2 1.8 2.36 %
Mesh 3 3.9 0.25 %
Mesh 4 (Ground Truth) 6.5 N/A

These findings show the relative change in pressure drop of 10.2%, 2.4%, and 0.25% for mesh-1, mesh-2, and mesh-3 compared to mesh-4. As it is evident that the pressure drop between mesh-3 and 4 is 0.25%, this study adopted mesh-3 (3.9 million element size) with 6 prism layers to reduce the computational time. Finally, the mesh independence analysis findings could be summarized as (i) to capture the velocity gradients accurately in the narrow region; a refined mesh with prism layers near the wall is recommended, (ii) in the larger cross-sectional area, mesh element size plays an important role for mesh refinement, and (iii) in the case of finer mesh, the change in the pressure drop value was minimal and insufficient to justify the increase in computational time.
Comparative analysis of Pre-surgical and Post-surgical Models:
The system and method of the present disclosure aim to offer a promising tool for pre-surgical planning, enabling the surgeon to access the outcome of the surgery and plan accordingly. In rhinology, a common complaint by patients is nasal obstruction, which requires the removal of the mucosal or enlarged tissues. The surgeons generally practice surgery for this treatment to increase the cross-sectional area for flow through the nasal cavity. Analogous to this procedure, the existing nasal cavity model was modified by increasing the region between the starting of the nasal valve area and the end of choanae by 10% and studying the flow.
The same end-to-end workflow, as conducted earlier for the flow analysis in the pre-surgical model, is followed for the modified nasal cavity model, i.e., the post-surgical model. A comparison of results for the flow rate of 10 L/min using the post-surgical nasal cavity model and the pre-surgical model is presented below.
A comparison is made for the velocity at the small area, just below the beginning of the olfactory region, as shown in FIG. 8(a), of larger velocity magnitude, for the base and modified models. It is found that the velocity for the base model is ~ 3.52 m/s, while, for the modified model, it is 2.9 m/s. This observation suggests that the reduced resistance due to the increase in the cross-sectional area allows the fluid to flow at a lower velocity.
The pressure drop for the post-surgical model is compared with the results from the presurgical model at different breathing flow rates for both left (in FIG. 14a) and right (in FIG. 14b), nasal passages (collectively referred as FIG. 14). More specifically, FIG. 14, with reference to FIG. 1 through 13, depicts pressure drop as a function of flow rate for the pre-surgical and post-surgical models (a) left and (b) right nasal cavities, in accordance with an embodiment of the present disclosure. Also, below Table 5 shows the calculated nasal flow resistances for the pre-surgical and post-surgical models.
Table 5
Flow resistances for the pre-surgical and post-surgical nasal models
Models Surface Area (cm2) Volume (cm3) Flow Rate (L/min) Pressure Drop (pa) Nasal Resistance (Pa-min/L)
Pre-surgical 223.67 64.234 10 8.9 0.89
Post-surgical 251.17 77.047 10 7.7 0.77

At a particular flow rate, the pressure drop is lower for the post-surgical model relative to that of the pre-surgical model due to the reduced resistance to flow and velocity. Therefore, the resistance to flow can be measured in terms of the pressure drop. The nasal resistance is defined as the ratio of pressure drop across the nasal cavity and the corresponding volumetric flow rate.
The temperature and relative humidity profiles for the post-surgical model has been compared with the results from the pre-surgical model and reported experimental results (in FIG. 15). More specifically, FIG. 15, with reference to FIG. 1 through 14, depicts average (a) temperature and (b) relative humidity profiles for the pre-surgical and post-surgical models, in accordance with an embodiment of the present disclosure. It was observed that the measured temperature and relative humidity at different cross section were slightly higher in the front region for the post operative condition, this could be in preoperative condition the front region had a smaller cross-sectional area compared to postoperative condition which allowed greater heat transfer between inhaled air and nasal wall as the bulk airflow region was closer to the nasal wall.
The system 100 and the method of the present disclosure offers an efficient mechanism for understanding the fluid flow and heat transfer in the nasal cavity. 3D Slicer has been used to develop the nasal cavity model in three dimensions from a CT image of the patient. Further, the system 100 performs the simulation on the discretized 3D nasal cavity model. The system 100 is also equipped with AR-Visualization mechanism AR-Visualization tool to understand the internal flow pattern in the nasal cavity easily.
As the primary aim of the system 100 is to plan surgery, the nasal geometry is modified accordingly provided. Further, a provision for comparing the outcome of the modified model with the base model is also provided by the system 100 and the method of the present disclosure. The flow behavior in the modified nasal cavity model should help efficient surgery planning. Further, the system 100 and the method (including the simulation as described herein) can be integrated to a real-world data/scenario or evidence such as respirate rate, temperature, air humidity, which can be extracted from a wearable device (e.g., smartwatch, or a mobile device, worn by the user such as subject/patient). Such integration can be enabled during pre- and post- surgical procedures (e.g., nasal procedures).
Further, the turbulent airflow in the nasal cavity occurs in two scenarios: (i) nasal obstacle caused by mucosa or congestant environment, and (ii) due to the septal deviation. Further, the system 100 and the method of the present disclosure have been validated by comparing the outcome with reported results in the literature to show the performance.
The written description describes the subject matter herein to enable any person skilled in the art to make and use the embodiments. The scope of the subject matter embodiments is defined by the claims and may include other modifications that occur to those skilled in the art. Such other modifications are intended to be within the scope of the claims if they have similar elements that do not differ from the literal language of the claims or if they include equivalent elements with insubstantial differences from the literal language of the claims.
It is to be understood that the scope of the protection is extended to such a program and in addition to a computer-readable means having a message therein; such computer-readable storage means contain program-code means for implementation of one or more steps of the method, when the program runs on a server or mobile device or any suitable programmable device. The hardware device can be any kind of device which can be programmed including e.g., any kind of computer like a server or a personal computer, or the like, or any combination thereof. The device may also include means which could be e.g., hardware means like e.g., an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or a combination of hardware and software means, e.g., an ASIC and an FPGA, or at least one microprocessor and at least one memory with software processing components located therein. Thus, the means can include both hardware means and software means. The method embodiments described herein could be implemented in hardware and software. The device may also include software means. Alternatively, the embodiments may be implemented on different hardware devices, e.g., using a plurality of CPUs/Graphic Processing Units (GPUs).
The embodiments herein can comprise hardware and software elements. The embodiments that are implemented in software include but are not limited to, firmware, resident software, microcode, etc. The functions performed by various components described herein may be implemented in other components or combinations of other components. For the purposes of this description, a computer-usable or computer readable medium can be any apparatus that can comprise, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
The illustrated steps are set out to explain the exemplary embodiments shown, and it should be anticipated that ongoing technological development will change the manner in which particular functions are performed. These examples are presented herein for purposes of illustration, and not limitation. Further, the boundaries of the functional building blocks have been arbitrarily defined herein for the convenience of the description. Alternative boundaries can be defined so long as the specified functions and relationships thereof are appropriately performed. Alternatives (including equivalents, extensions, variations, deviations, etc., of those described herein) will be apparent to persons skilled in the relevant art(s) based on the teachings contained herein. Such alternatives fall within the scope of the disclosed embodiments. Also, the words “comprising,” “having,” “containing,” and “including,” and other similar forms are intended to be equivalent in meaning and be open ended in that an item or items following any one of these words is not meant to be an exhaustive listing of such item or items or meant to be limited to only the listed item or items. It must also be noted that as used herein and in the appended claims, the singular forms “a,” “an,” and “the” include plural references unless the context clearly dictates otherwise.
Furthermore, one or more computer-readable storage media may be utilized in implementing embodiments consistent with the present disclosure. A computer-readable storage medium refers to any type of physical memory on which information or data readable by a processor may be stored. Thus, a computer-readable storage medium may store instructions for execution by one or more processors, including instructions for causing the processor(s) to perform steps or stages consistent with the embodiments described herein. The term “computer-readable medium” should be understood to include tangible items and exclude carrier waves and transient signals, i.e., be non-transitory. Examples include random access memory (RAM), read-only memory (ROM), volatile memory, nonvolatile memory, hard drives, CD ROMs, DVDs, flash drives, disks, and any other known physical storage media.
It is intended that the disclosure and examples be considered as exemplary only, with a true scope of disclosed embodiments being indicated by the following claims.

, Claims:
1. A processor implemented method, comprising:
obtaining, via one or more hardware processors, a Computed Tomography (CT) image of a nasal cavity of a user (202);
rendering, via the one or more hardware processors, a three-dimensional (3D) model of the nasal cavity from the CT image based on a segmentation of an air passage region in the CT image of the nasal cavity based on (a) one or more user inputs, and (b) an Artificial Intelligence (AI) based technique (204);
iteratively performing, until a nasal cavity mesh is generated (206):
meshing the rendered 3D model of the nasal cavity to obtain a meshed geometry of the nasal cavity, wherein the meshed geometry of the nasal cavity comprises a plurality of cell identifiers (IDs) associated with a plurality of grids, (206a);
performing a mesh independent analysis on the meshed geometry of the nasal cavity to obtain a first simulated nasal mesh model (206b), wherein the mesh independent analysis comprises:
performing a velocity gradient optimization technique of the plurality of grids, with a dynamic velocity gradient threshold determination to obtain one or more optimized gradient values (206b-1);
splitting one or more grids from the plurality of grids based on the one or more optimized gradient values to obtain a refined mesh (206b-2);
smoothing the refined mesh using one or more smoothing techniques to obtain a smoothened mesh (206b-3); and
simulating the smoothened mesh of the nasal cavity to obtain the first simulated nasal mesh model (206b-4);
determining one or more quality parameters of the first simulated nasal mesh model (206c); and
regenerating a nasal cavity mesh based on the one or more determined quality parameters to obtain a regenerated nasal cavity mesh (206d); and
simulating, until a convergence is met, the regenerated nasal cavity mesh by solving one or more associated governing equations based on one or more setup parameters to obtain a first simulated nasal model (208).

2. The processor implemented method as claimed in claim 1, wherein the meshed geometry of the nasal cavity is based on a first edge length and a second edge length being imposed on a plurality of surfaces of the 3D model of the nasal cavity of the user.

3. The processor implemented method as claimed in claim 1, wherein the one or more quality parameters comprise at least one of a skewness, an aspect ratio, and a non-orthogonality of the first simulated nasal mesh model.

4. The processor implemented method as claimed in claim 1, wherein the one or more setup parameters comprise at least one of air flow rate at one or more inlets of the nasal cavity, a selection of a turbulence model based on a Reynolds numbers, a nasal wall, an outlet boundary condition, and a selection of one or more discretization schemes.

5. The processor implemented method as claimed in claim 1, wherein if the convergence is not met, the method comprises remeshing the first simulated nasal model or validating the one or more setup parameters.

6. The processor implemented method as claimed in claim 1, comprising generating a Virtual Reality (VR) enabled visualization of the nasal cavity with a physical feedback using one or more haptic techniques.
7. The processor implemented method as claimed in claim 6, comprising determining (i) an aerodynamic, a hydrodynamic, a flow distribution, (ii) a characteristic behavior of one or more surgical equipment, and one or more prosthetics using the Virtual Reality (VR) enabled visualization, and (iii) a training of a nasal procedure.

8. The processor implemented method as claimed in claim 7, comprising:
determining a requirement for the nasal procedure based on at least one of (i) the aerodynamic, the hydrodynamic, the flow distribution, (ii) the characteristic behavior of one or more surgical equipment, and one or more prosthetics, and (iii) the training of the nasal procedure;
modifying a specific region of the nasal cavity of the user based on the determined requirement;
performing a local meshing of the first simulated nasal mesh model during the nasal procedure based on the reduced region of the nasal cavity to obtain a second simulated nasal model, wherein the local meshing comprises:
repeating the step of regenerating the nasal cavity mesh; and
performing a comparison of the first simulated nasal model and the second simulated nasal model to obtain a performance report of the nasal procedure.

9. The processor implemented method as claimed in claim 1, wherein the convergence is obtained by using a Machine Learning (ML) technique.

10. A system (100), comprising:
a memory (102) storing instructions;
one or more communication interfaces (106); and
one or more hardware processors (104) coupled to the memory (102) via the one or more communication interfaces (106), wherein the one or more hardware processors (104) are configured by the instructions to:
obtain a Computed Tomography (CT) image of a nasal cavity of a user;
render a three-dimensional (3D) model of the nasal cavity from the CT image based on a segmentation of an air passage region in the CT image of the nasal cavity based on (a) one or more user inputs, and (b) an Artificial Intelligence (AI) based technique;
iteratively perform, until a nasal cavity mesh is generated:
meshing the rendered 3D model of the nasal cavity to obtain a meshed geometry of the nasal cavity, wherein the meshed geometry of the nasal cavity comprises a plurality of cell identifiers (IDs) associated with a plurality of grids;
performing a mesh independent analysis on the meshed geometry of the nasal cavity to obtain a first simulated nasal mesh model, wherein the mesh independent analysis comprises:
performing a velocity gradient optimization technique of the plurality of grids, with a dynamic velocity gradient threshold determination to obtain one or more optimized gradient values;
splitting one or more grids from the plurality of grids based on the one or more optimized gradient values to obtain a refined mesh (206b-2);
smoothing the refined mesh using one or more smoothing techniques to obtain a smoothened mesh (206b-3); and
simulating the smoothened mesh of the nasal cavity to obtain a first simulated nasal mesh model;
determining one or more quality parameters of the first simulated nasal mesh model; and
regenerating a nasal cavity mesh based on the one or more determined quality parameters to obtain a regenerated nasal cavity mesh; and
simulating, until a convergence is met, the regenerated nasal cavity mesh by solving one or more associated governing equations based on one or more setup parameters to obtain a first simulated nasal model.

11. The system as claimed in claim 10, wherein the meshed geometry of the nasal cavity is based on a first edge length and a second edge length being imposed on a plurality of surfaces of the 3D model of the nasal cavity of the user.

12. The system as claimed in claim 10, wherein the one or more quality parameters comprise at least one of a skewness, an aspect ratio, and a non-orthogonality of the first simulated nasal mesh model.

13. The system as claimed in claim 10, wherein the one or more setup parameters comprise at least one of air flow rate at one or more inlets of the nasal cavity, a selection of a turbulence model based on a Reynolds numbers, a nasal wall, an outlet boundary condition, and a selection of one or more discretization schemes.

14. The system as claimed in claim 10, wherein if the convergence is not met, the method comprises remeshing the first simulated nasal model or validating the one or more setup parameters.

15. The system as claimed in claim 10, wherein the one or more hardware processors are further configured by the instructions to generate a Virtual Reality (VR) enabled visualization of the nasal cavity with a physical feedback using one or more haptic techniques.

16. The system as claimed in claim 15, wherein the one or more hardware processors are further configured by the instructions to (i) an aerodynamic, a hydrodynamic, and a flow distribution, (ii) a characteristic behavior of one or more surgical equipment, and one or more prosthetics using the Virtual Reality (VR) enabled visualization, and (iii) a training of a nasal procedure.

17. The system as claimed in claim 16, wherein the one or more hardware processors are further configured by the instructions to:
determine a requirement for the nasal procedure based on at least one of (i) the aerodynamic, the hydrodynamic, the flow distribution,, (ii) the characteristic behavior of one or more surgical equipment, and one or more prosthetics, and (iii) the training of the nasal procedure.
modify a specific region of the nasal cavity of the user based on the determined requirement.
perform a local meshing of the first simulated nasal mesh model during the nasal procedure based on the reduced region of the nasal cavity to obtain a second simulated nasal model, wherein the local meshing comprises:
repeating the step of regenerating the nasal cavity mesh; and
performing a comparison of the first simulated nasal model and the second simulated nasal model to obtain a performance report of the nasal procedure.

18. The system as claimed in claim 10, wherein the convergence is obtained by using a Machine Learning (ML) technique.