METHOD AND SYSTEM FOR IMPROVING ACCURACY IN PERFUSION-DIFFUSION MISMATCH ASSESSMENT

BACKGROUND

[0001] Embodiments of the present disclosure relate generally to magnetic resonance imaging (MRI), and more particularly to a method and a system for customizing MRI data for enhanced characterization of a lesion.

[0002] Stroke has been a leading cause of death and disability in recent times, with more than 15 million people suffering from strokes each year globally. Typically, a stroke may be hemorrhagic or ischemic. A hemorrhagic stroke occurs when a blood vessel ruptures, thus flooding a portion of the brain with blood. A majority of strokes, however, are ischemic strokes that occur when a blood vessel is blocked, for example, due to a clot. The blocked blood vessel causes oxygen deprivation in cerebral tissues, which if left untreated for more than a few hours leads to necrosis of the cerebral tissues.

[0003] Ischemic strokes may be treated using thrombolytic agents that are designed to dissolve an obstructive clot and restore blood flow to hypoperfused and/or depolarized areas of the cerebral tissues. Rapid restoration of blood flow may potentially salvage portions of the affected cerebral tissues that have not yet been irreversibly damaged. Such portions of the cerebral tissues are commonly referred to as "ischemic penumbra," while portions of the cerebral tissues that have been irreversibly damaged due to oxygen deprivation are referred to as "core ischemic zones."

[0004] Generally, cerebral tissues cease to function if deprived of oxygen for more than sixty to ninety seconds. However, after approximately three hours, the tissues may suffer irreversible injury possibly leading to cerebral infarction or death of the tissues. Accordingly, only a narrow window of time after the onset of the stroke is available to a medical practitioner for administration of a thrombolytic agent for salvaging the ischemic penumbra.

[0005] A decision to administer the thrombolytic agent, however, is not automatic because reperfusion of severely hypoperfused areas can result in hemorrhage and associated complications. Accordingly, the thrombolytic agent is administered only if an estimation of the ischemic penumbra in the patient is large enough to justify pharmacological treatment and a corresponding risk of hemorrhage. In case of a patient with insignificant salvageable cerebral tissue, however, administering the thrombolytic agent may unnecessarily aggravate a risk to patient health and mortality. Accurate and rapid assessment of risk and/or benefit of thrombolytic administration in the early minutes of treating the patient with stroke symptoms, therefore, is key to a successful patient recovery.

[0006] Generally, assessment of the risk associated with thrombolytic administration entails estimation of a size of the ischemic penumbra and/or core ischemic zones using diagnostic images. These diagnostic images may be generated using an MRI system. Particularly, in acute ischemic stroke, diffusion-based MRI and/or perfusion-based MRI may be employed for clinical assessment of infarcted core ischemic zones. Further, a perfusion-diffusion mismatch (PDM) may be determined based on lesion volumes determined from perfusion-weighted (PW) and/or diffusion-weighted (DW) images. The determined PDM, in turn, may be used to estimate the salvageable cerebral tissue that is at risk of infarction following the stroke.

[0007] To that end, automated lesion segmentation techniques may be employed for assessing an extent and a type of the stroke. Conventionally, these techniques are known to use apparent diffusion coefficient (ADC) and/or diffusion-based contrast, both of which depend on accuracy of a b-factor or a q-factor used during diffusion-based MR imaging. Generally, the b-factor or q-factor are representative of a diffusion sensitivity of the MRI system and may aid in ascertaining an influence of gradients on the diffusion-based images. The b-factor and/or the q-factor used for diffusion-based MRI, however, may vary due to system-specific factors such as gradient non-linearity and concomitant field effects. These system-specific factors may themselves differ between different vendors and MRI systems, thus, resulting in inconsistent b-factor or q-factor values. Inconsistency in the b-factor or q-factor values, in turn, may result in an erroneous decision regarding suitability of the thrombolysis treatment for a stroke patient.

BRIEF DESCRIPTION

[0008] In accordance with certain aspects of the present disclosure, a method for magnetic resonance imaging is disclosed. The method includes receiving magnetic resonance imaging data corresponding to a target volume of a subject, where the received magnetic resonance imaging data includes one or more diffusion-based magnetic resonance images and/or one or more perfusion-based magnetic resonance images. An optimal value of at least one customization parameter that customizes the received magnetic resonance imaging data based on one or more customization preferences is determined to synthesize customized magnetic resonance imaging data. Diffusion-based lesion segmentation is performed using the customized magnetic resonance imaging data. A pathological condition of the subject is determined at least based on the diffusion-based lesion segmentation.

[0009] In accordance with certain aspects of the present disclosure, magnetic resonance imaging system is presented. The system includes a scanner configured to scan a target volume of a subject to acquire magnetic resonance imaging data. Additionally, the system includes a processing subsystem operationally coupled to the scanner and configured to receive the magnetic resonance imaging data corresponding to the target volume, where the received magnetic resonance imaging data includes one or more diffusion-based magnetic resonance images and/or one or more perfusion-based magnetic resonance images. The processing subsystem is configured to identify an optimal value of at least one customization parameter that customizes the received magnetic resonance imaging data based on one or more customization preferences to synthesize customized magnetic resonance imaging data. Moreover, the processing subsystem is configured to perform diffusion-based lesion segmentation using the customized magnetic resonance imaging data. Furthermore, the processing subsystem is configured to determine a pathological condition of the subject at least based on the diffusion-based lesion segmentation.

DRAWINGS

[0010] These and other features, aspects, and advantages of the present invention will become better understood when the following detailed description is read with reference to the accompanying drawings in which like characters represent like parts throughout the drawings, wherein:

[0011] FIG. 1 is a schematic representation of an exemplary MRI system, in accordance with aspects of the present disclosure;

[0012] FIG. 2 is a flowchart depicting an exemplary method for magnetic resonance (MR) imaging, in accordance with aspects of present disclosure;

[0013] FIG. 3 is a graphical representation depicting a comparison of exemplary lesion volumes computed using ADC-only lesion segmentation and exemplary lesion volumes computed using a combined diffusion-based and ADC-based lesion segmentation, in accordance with aspects of present disclosure;

[0014] FIG. 4 is a graphical representation depicting a comparison of segmentation results corresponding to a plurality of measurements at different customization parameters, in accordance with aspects of present disclosure; and

[0015] FIG. 5 is a graphical representation depicting a determined agreement between ground truths with an ADC-only segmentation and a combined diffusion-based and ADC-based segmentation, in accordance with aspects of present disclosure.

DETAILED DESCRIPTION

[0016] The following description presents exemplary systems and methods for enhancing accuracy of measurements corresponding to one or more lesion characteristics observed following an acute ischemic stroke. Typically, diagnosis and treatment of an ischemic stroke entails identification of regions of reduced perfusion and diffusion using diffusion-based and/or perfusion-based images. However, as previously noted, system-specific factors such as gradient non-linearity and concomitant field effects may result in errors in the diffusion and/or the perfusion measurements.

[0017] Accordingly, embodiments described hereinafter disclose a method and a system for customizing MRI data to allow for enhanced lesion segmentation in diffusion- based images. As used herein, the term "MRI data" may correspond to, for example, diffusion-based images, perfusion-based images, k-space imaging data, surface coil intensity profiles, user-defined data, and/or system-specific imaging parameters. The enhanced diffusion-based lesion segmentation using the customized MRI data may result in more accurate diffusion-based measurements, which in turn, may aid in accurate quantification of the lesion characteristics. A radiologist may use these accurately quantified lesion characteristics to determine an appropriate treatment for a stroke patient.

[0018] Although exemplary embodiments of the present systems and methods are described with reference to ischemic stroke assessment, it will be appreciated that use of embodiments of the present systems and methods in various other imaging applications is also contemplated. For example, embodiments of the present systems and methods may find use in allowing for robust and reproducible segmentation of diffusion and/or perfusion lesions. Particularly, embodiments of the present systems and methods may allow for robust segmentation of lesions caused by different pathologies, for example tumors, and occurring in different anatomical regions such as abdomen, breast, liver, kidney, and/or brain. An exemplary environment that is suitable for practising various implementations of the present system is discussed in the following sections with reference to FIG. 1.

[0019] FIG. 1 illustrates an MRI system 100 configured for MR imaging. Particularly, in certain embodiments, the MRI system 100 may be configured to aid in improving accuracy of a semi-automated assessment of cerebral tissues following an ischemic stroke. To that end, in one embodiment, the MRI system 100 may include a scanner 102, a system controller 104, and an operator interface 106. The scanner 102 may further include a patient bore 108 into which a table 110 may be positioned for disposing a patient 112 in a desired position for scanning.

[0020] Further, in certain embodiments, the scanner 102 may also include a series of associated coils for imaging the patient 112. In one embodiment, for example, the scanner 102 may include a primary magnet coil 114 energized via a power supply 116 for generating a primary magnetic field generally aligned with the patient bore 108. The scanner 102 may further include a series of gradient coils 118, 120 and 122 grouped in a coil assembly for generating accurately controlled magnetic fields, the strength of which may vary over a designated field of view (FOV) of the scanner 102.

[0021] Additionally, in one embodiment, the scanner 102 may include a radiofrequency (RF) coil 124 configured to generate RF pulses for exciting a gyromagnetic material, typically bound in tissues of the patient 112. In certain embodiments, the RF coil 124 may also serve as a receiving coil. Accordingly, the RF coil 124 may be operationally coupled to transmit-receive circuitry 126 in passive and/or active modes for receiving emissions from the gyromagnetic material and for applying RF excitation pulses, respectively. Alternatively, the MRI system 100 may include various configurations of receiving coils different from the RF coil 124.

[0022] In certain embodiments, the system controller 104 may be configured to control operation of the MR coils 118, 120, 122, and 124 for generating a desired magnetic field and RF pulses. To that end, in one embodiment, the system controller 104 may include a pulse sequence generator 128, timing circuitry 130, and a processing subsystem 132 that may be configured to generate and/or control imaging gradient waveforms and RF pulse sequences employed during a medical procedure. Further, the system controller 104 may also include amplification circuitry 134 and interface circuitry 136 configured to control and/or interface between the pulse sequence generator 128 and the coils of scanner 102. For example, the amplification circuitry 134 and/or the interface circuitry 136 may be configured to drive the RF coil 124 and amplify corresponding response signals for further processing. The amplified response signals, in turn, may be transmitted to the processing subsystem 132 for determining information for use in image reconstruction.

[0023] To that end, the processing subsystem 132, for example, may include one or more application-specific processors, graphical processing units (GPUs), digital signal processors (DSPs), microcomputers, microcontrollers, Application Specific Integrated Circuits (ASICs) and/or Field Programmable Gate Arrays (FPGAs). In certain embodiments, the processing subsystem 132 may be configured to process the response signals emitted by excited patient nuclei in response to the RF pulses. Specifically, the processing subsystem 132 may be configured to demodulate, filter, and/or digitize the response signals for determining the image reconstruction information. Additionally, the processing subsystem 132 may be configured to transmit the digitized information to an image processing unit 138 to allow reconstruction of desired images of a target volume of interest (VOI) in the patient 112.

[0024] Particularly, in one embodiment, the image processing unit 138 may be configured to use the digitized information to generate diffusion-based and/or perfusion-based images using the received MRI data. In a presently contemplated embodiment, the image processing unit 138 may be configured to use diffusion weighted imaging (DWI) and perfusion weighted imaging (PWI) to generate the diffusion-based and/or perfusion-based images. However, in alternative embodiments, the image processing unit 138 may be configured to use other diffusion-based and perfusion-based imaging protocols such as diffusion tensor imaging (DTI), diffusion spectral imaging (DSI), q-space imaging, intravoxel incoherent motion MRI (IVIM), dynamic contrast enhancement MRI, arterial spin labeling (ASL), and/or ASL-variants.

[0025] Further, the image processing unit 138 and/or the processing subsystem 132 may also be configured to identify a region corresponding to a probable lesion (probable lesion region) or a region of infarct in the diffusion-based and perfusion-based images. Particularly, the image processing unit 138 may be configured to identify regions of perfusion change, namely the ischemic penumbra, the core ischemic zones, and/or the probable lesion region in the diffusion-based and/or perfusion-based images with greater accuracy. In certain embodiments, the regions of perfusion change may be determined based on measurements obtained using diffusion-based and/or perfusion-based lesion segmentation. These measurements, however, may vary due to differences in the customization preferences employed by different radiologists, clinical sites, and/or imaging systems.

[0026] In accordance with exemplary aspects of the present disclosure, the processing subsystem 132 may be configured to customize the MRI data acquired by the scanner 102 based on the customization preferences being employed. In one example, the customization preferences may include determining whether to correct for one or more system imperfections. Upon receiving user-defined or application-specific input to correct the system imperfections, the processing subsystem 132 may be configured to identify a data correction factor (DCF). The processing subsystem 132 may also be configured to customize the MRI data using the DCF to mitigate data corruption in the diffusion-based images due to the system imperfections.

[0027] In one embodiment, the system imperfections may include b-factor and/or q-factor variability that may be represented using a system imperfection factor. As used herein, the term "system imperfection factor" may be used to refer to a value that is representative of variations in the MRI system 100, for example, caused by gradient related non-linearity and/or concomitant field effects. Magnitude of the system imperfection factor (SIF), however, may vary according to a distance from the isocentre of the MRI system 100. Accordingly, in certain embodiments, the processing subsystem 132 may be configured to determine the SIF at a determined distance corresponding to the target volume. The determined distance, for example, may correspond to a location of a selected slice and/or voxel corresponding to the target volume relative to the isocentre of the MRI system 100.

[0028] Further, the magnitude of the SIF may also differ between different vendors, different field strengths and/or across the field of view of MRI system 100, thus, resulting in inconsistent b-factor or q-factor values. Moreover, the b-factor or q-factor values may vary as a function of the diameter of the patient bore 108. Particularly, the fa-factor or q-factor variations may be observed to be higher in MRI systems having smaller patient bores, for example, in head-only scanners used for imaging a central nervous system.

[0029] It may be noted that b-factor and/or q-factor values may be used in determining diffusion measurements corresponding to one or more voxels in a diffusion-based image corresponding to the target VOL In an exemplary implementation that uses IVIM based MRI imaging, the b-factor may be used in determining both the diffusion-based and perfusion-based measurements in one or more voxels. The diffusion-based measurements, in turn, may be used to determine certain lesion characteristics, such as PDM, corresponding to the target VOL Accordingly, any inconsistency in the b-factor and/or q-factor values may result in erroneous PDM measurements, which may render the PDM measurements unsuitable for determining suitability of administering a thrombolytic agent to the patient 112.

[0030] Accordingly, in one embodiment, the processing subsystem 132 may be configured to identify a suitable value of the DCF for reducing any negative effects of variations in the SIF on the diffusion-based MRI data. To that end, in one embodiment, the processing subsystem 132 may be configured to identify the DCF based on a priori information. The a priori information, for example, may include information obtained and/or learned from previous medical exams, user input, and/or information derived from a database or published medical literature.

[0031] According to certain exemplary aspects of the present disclosure, the processing subsystem 132 may be configured to use the DCF for synthesizing pre-processed MRI data for enhanced diffusion-based lesion segmentation. Specifically, the processing subsystem 132 may be configured to use the DCF for identifying or generating more suitable diffusion-based MRI data. For example, the processing subsystem 132 may be configured to use the DCF for correcting gradient values, which in turn, may aid in accurate quantification of ADC values for use in the diffusion-based lesion segmentation. To that end, the DCF may be a scalar value, a spatially varying value, a set of operations, or a mathematical function such as a spline-based polynomial function that may allow the processing subsystem 132 to pre-process or normalize the acquired MRI data to mitigate system imperfections. Although the present embodiment is described with reference to a method for customizing the MRI data using the DCF to mitigate the system imperfections, in other embodiments, certain other customization parameters may be used to satisfy one or more customization preferences.

[0032] Generally, imaging protocols for generating the diffusion-based and perfusion-based images that may be used to measure lesion characteristics such as PDM may differ owing to variations in one or more system-specific and/or user-defined customization preferences for lesion characterization. By way of example, a first radiologist may employ a rectangular region delineated on the diffusion-based images as seed point input for lesion segmentation in the diffusion-based images. A second radiologist, however, may employ a circular region as the seed point input for the lesion segmentation. Accordingly, use of the same lesion segmentation method by both the first and second radiologists may not be optimal due to differences in corresponding customization preferences. Similarly, imaging protocols may also differ between different MRI systems and/or clinical sites. Conventional lesion segmentation methods, however, may not provide customization capabilities for satisfying different imaging and user-specific customization preferences for characterizing a lesion.

[0033] However, embodiments of the present disclosure allow customization of the lesion segmentation method based on the one or more customization preferences received from the user and/or those defined by system or clinical site-specific imaging protocols. Particularly, certain embodiments allow customization of the diffusion-based MRI data acquired in a particular acquisition scan using an optimal value of at least one customization parameter. The customization parameter, for example, may include b-factor values, q-factor values, transitional slopes, voxel intensities, and/or mathematical functions used for subsequent processing of the diffusion-based MRI data to satisfy the customization preferences. The image processing unit 138 may be configured to select the optimal values of the customization parameter so as to allow customization of the diffusion-based MRI data such that there is greater agreement between lesion characteristic measurements determined using the lesion segmentation method and a corresponding reference value.

[0034] In certain embodiments, the image processing unit 138 may also be configured to determine the reference value using a priori information determined from diffusion-based images acquired using the one or more desired customization preferences. As previously noted, the a priori information, for example, may include information obtained from previous medical exams, user input, and/or information derived from a database or published medical literature. In one embodiment, for example, the a priori information may include previously acquired diffusion MRI data and a corresponding lesion segmentation performed using the acquired diffusion MRI data.

[0035] In another embodiment, the reference value may include a "golden set" or a "ground truth" (GT) lesion segmentation available in the database. The GT lesion segmentation may be generated by a skilled and/or experienced radiologist by manually delineating a lesion in the diffusion-based images such that the GT lesion segmentation satisfies the desired customization preferences. The image processing unit 138 may be configured to compute a volume corresponding to the delineated lesion and use the computed volume as the GT value of a lesion characteristic. Further, the image processing unit 138 may be configured to determine one or more values of the lesion characteristic using the lesion segmentation method at different values of the customization parameter. The image processing unit 138 may also be configured to compute corresponding differences between the GT value and each of these one or more lesion characteristic values determined at different values of the customization parameter.

[0036] A lower value of the computed difference is representative of greater agreement between the GT value and lesion characteristic values determined using the lesion segmentation method. An extent of agreement between the GT value and lesion characteristic values determined using the lesion segmentation method may be indicative of a robustness of the lesion segmentation method. Accordingly, the image processing unit 138 may be configured to select the value of the customization parameter corresponding to the smallest computed difference as the optimal value of the customization parameter.

[0037] In one embodiment, the image processing unit 138 may be configured to use the optimal value of the customization parameter for customizing the diffusion-based MRI data. Alternatively, the image processing unit 138 may be configured to use one or more user-defined values received in real-time or prior to imaging as the optimal value of the customization parameter. In this example, the user, such as a radiologist, may determine the optimal value of the customization parameter for synthesizing customized MRI data, for example, based on the a priori information and/or previous experience.

[0038] Moreover, in certain embodiments, the image processing unit 138 may be configured to use the optimal values of more than one customization parameter to synthesize customized MRI data that satisfies the desired customization preferences. For example, the image processing unit 138 may be configured to use an optimal value of a first customization parameter, for example, the DCF for pre-processing the MRI data to mitigate system imperfections. Additionally, the image processing unit 138 may be configured to use an optimal value of a second customization parameter to customize the acquired MRI data and/or the pre-processed MRI data, for example, to improve performance of a diffusion-based lesion segmentation that employs a user-defined shape as a seed point input.

[0039] Additionally, in one embodiment, the image processing unit 138 may be configured to compute a product of the optimal values of the first and second customization parameters and the originally acquired and/or pre-processed MRI data corrected using the DCF to synthesize the customized MRI data. Alternatively, if corrections to the system imperfections are not requested, the image processing unit 138 may be configured to compute a product of the optimal value of the second customization parameter and the originally acquired diffusion-based MRI data. The diffusion-based MRI data, thus customized, may satisfy the desired customization preferences as specified by clinical site protocols, user preferences, and/or capabilities of the MRI system 100. An exemplary method for pre-processing and customizing the MRI data for use in efficient MR imaging will be described in greater detail with reference to FIG. 2.

[0040] Furthermore, in certain embodiments, the image processing unit 138 may be configured to use the customized MRI data for performing lesion segmentation, for example, in diffusion-based images. Use of the customized MRI data in the lesion segmentation may allow for accurate estimation of diffusion-based lesion volumes. Accurate estimation of the diffusion-based lesion volumes, in turn, may aid in determining lesion characteristics such as PDM, which may be used in stroke management.

[0041] Accordingly, in one embodiment, the image processing unit 138 may be configured to use the pre-processed and/or customized MRI data for performing a semi-automated lesion segmentation in the diffusion-based images. Alternatively, if there are no customization preferences, the processing subsystem 132 may be configured to perform the semi-automated lesion segmentation using the acquired MRI data. Moreover, in certain embodiments, the image processing unit 138 may be configured to perform the semi-automated lesion segmentation to identify the lesion and/or segment the lesion in the diffusion-based images. Particularly, the image processing unit 138 may be configured to perform the semi-automated lesion segmentation based on one or more seed points input by a medical practitioner 140 through the operator interface 106.

[0042] To that end, the operator interface 106 may include one or more input devices 142 that are operationally connected to the MRI system 100 via a communications link 144, such as a backplane or Internet. The input devices 142, for example, may include a keyboard, a mouse, a trackball, a joystick, a touch-activated screen, a light wand, a control panel, and/or an audio input device such as a microphone associated with corresponding speech recognition circuitry. In one embodiment, the input devices 142 may include an interactive graphical user interface (GUI) that may allow the medical practitioner 140 to input the seed points and/or identify a VOI corresponding to the lesion in the perfusion-based and/or the diffusion-based images. The input devices 142 may also allow the medical practitioner 140 to request for image-derived information such as lesion characteristics for evaluating stroke parameters corresponding to the patient 112.

[0043] In certain embodiments, the image processing unit 138 may be configured to provide the medical practitioner 140 with the requested information in real-time through one or more output devices 146. The output devices 146, for example, may include a display 148, a printer 150, and/or an audio output device 152. In one embodiment, the display 148 may be integrated into wearable eyeglasses, or may be ceiling or cart mounted to allow the medical practitioner 140 to observe the reconstructed images and other medically relevant information during imaging. Accordingly, in certain embodiments, the image processing unit 138 may be configured to transmit the requested information to the medical practitioner 140 as a visual report on the display 148 and/or the printer 150. Additionally, the image processing unit 138 may be configured to convey the requested information to the medical practitioner 140 audibly through the audio output device 152.

[0044] Furthermore, in one embodiment, the requested information may include estimated structural and functional characteristics such as lesion volumes and PDM. The image processing unit 138 may be configured to derive the requested information via use of the semi-automated lesion segmentation. In one example, the semi-automated lesion segmentation may be guided by one or more seed points received from the user, one or more anatomical masks, ADC maps, functional maps, and/or reference data. The anatomical masks, for example, may correspond to different regions in the brain such as cerebrum, cerebellum, and brain ventricles. These masks may aid in dividing an image corresponding to the brain into different regions for more efficient lesion segmentation. Additionally, the functional maps may include, for example, a time-to-maximum of a tissue residue function (Tmax) map, a cerebral blood flow (CBF) map, cerebral blood volume (CBV) map, a mean transit time (MTT) map, and a time to peak (TTP) map.

[0045] Moreover, the reference data may include, for example, previously acquired patient information, atlas-based anatomical information, and/or reference images. The previously acquired patient information, in turn, may include electrocardiogram (ECG) signals, resting state MRI data, functional MRI data, IVIM data, vascular information, metabolic information, ASL information, and/or spectroscopic information. In certain embodiments, the image processing unit 138 may be configured to use the reference data to remove any false positive data from a region identified as the probable lesion region.

[0046] In one embodiment, the image processing unit 138 may be configured to receive the seed points, the anatomical masks, and/or the reference data from a storage repository 154 for identifying and segmenting the lesion in the reconstructed MRI images. The storage repository 154 may further store the acquired data, reconstructed images, and/or related information derived from the diffusion-based MRI images for use during the lesion segmentation and/or analysis in real-time. Accordingly, the storage repository 154 may include devices such as a hard disk drive, a floppy disk drive, a compact disk-read/write (CD-R/W) drive, a Digital Versatile Disc (DVD) drive, a flash drive, and/or a solid-state storage device.

[0047] Moreover, in accordance with aspects of the present disclosure, the image processing unit 138 may be configured to perform the lesion segmentation using iterative multilevel thresholding in the different regions of the diffusion-based images. Multilevel thresholding employs a plurality of thresholds to segment the diffusion-based image into a plurality of segments. Accordingly, in one example, a number and/or range of thresholds may be selected by optimizing an objective function. For example, the objective function may entail maximizing variance or minimizing entropy between selected regions in the diffusion-based image corresponding to anatomical areas of interest in the brain such as the cerebrum and the cerebellum. Furthermore, in certain embodiments, multilevel thresholding may aid in identifying and extracting a feature of interest such as a probable lesion from background data, for example, based on a distribution of gray levels or texture in a raw or filtered diffusion-based image.

[0048] In a presently contemplated embodiment, multilevel thresholding may be used to segment the diffusion-based image into a determined number of segments based on one or more determined ranges of intensities of voxels in the diffusion-based image. By way of example, the diffusion-based image may be segmented into four segments. In particular, a first segment corresponding to a first range that includes the highest voxel intensities may be identified as the probable lesion. Similarly, a second segment corresponding to a second range that includes the lowest voxel intensities may be identified as background regions. Further, third and fourth segments corresponding to third and fourth ranges of voxel intensities, respectively, may be identified as corresponding to certain structures in the brain tissue.

[0049] Accuracy of lesion segmentation using multilevel thresholding, however, may depend upon an accuracy of the diffusion-based MRI data. In certain scenarios, the diffusion-based MRI data may be corrupted by patient motion and/or system imperfections. Diffusion-based trace maps, diffusion-based images and/or ADC maps generated from the corrupted diffusion-based MRI data, thus, may exhibit anisotropy, thereby resulting in false positives during multilevel thresholding. Conventional lesion segmentation methods fail to address anisotropy in the diffusion-based data due to corruption of the diffusion-based MRI data, and thus, continue to use the corrupted diffusion-based MRI data for lesion segmentation and subsequent lesion characteristic analysis.

[0050] Embodiments of the present disclosure, however, allow for mitigation of the anisotropy in the diffusion-based MRI data prior to use in lesion segmentation and/or the subsequent lesion characteristic analysis. Particularly, the embodiments described herein allow for mitigation of ill effects of the system imperfections, such as b-factor variability, on the lesion segmentation by pre-processing the MRI data using a determined DCF. As previously noted, the image processing unit 138 may be configured to use the DCF for generating the pre-processed MRI data that may be more robust to the b-factor variability.

[0051] In accordance with exemplary aspects of the present disclosure, the image processing unit 138 may be configured to use the pre-processed MRI data for performing diffusion-based lesion segmentation with greater accuracy. As previously noted, in one embodiment, the image processing unit 13 8 may be configured to use the pre-processed MRI data for performing diffusion-based lesion segmentation using multilevel thresholding. Accordingly, the image processing unit 138 may be configured to adapt one or more multilevel thresholding parameters to mitigate the anisotropy in the diffusion-based MRI data. For example, the image processing unit 138 may be configured to vary one or more ranges corresponding to voxel intensities in each threshold level, the number of threshold levels, and/or use of different parameter settings for robust lesion segmentation.

[0052] In one embodiment, the image processing unit 138 may be configured to apply multilevel thresholding to the diffusion-based images iteratively using the different parameter settings. During each iteration, the image processing unit 138 may be configured to use feedback from the ADC maps, the anatomical masks, the seed points, and/or the reference data to remove regions that have been erroneously identified (false positive regions) as corresponding to the probable lesion region. Removal of these false positive regions from the diffusion-based images may aid in more accurate diffusion-based lesion segmentation.

[0053] Additionally, in one embodiment, the image processing unit 138 may be configured to use the lesion segmentation in the diffusion-based images for performing lesion segmentation in the perfusion-based images. Moreover, the image processing unit 138 may be configured to use the user-defined seed points, contralateral analysis of a target VOI based on the perfusion-based parametric maps, and/or the reference data for performing the perfusion-based lesion segmentation.

[0054] Further, the image processing unit 138 may be configured to determine one or more characteristics corresponding to tissues affected by an ischemic stroke based on the lesion segmentation in the diffusion-based and/or perfusion-based images. In one embodiment, for example, the image processing unit 138 may be configured to identify the ischemic penumbra and core ischemic regions in the brain tissue based on the determined lesion characteristics. The medical practitioner 140 may rely on this information for determining whether to administer the thrombolytic agent to the patient 112. Additionally, the medical practitioner 140 may use this information for evaluating an effect of the thrombolytic agent in real-time and further for determining whether to terminate or continue therapy based on the evaluated effect. In case of a longitudinal and/or serial scan performed on the patient 112, the information may be used to determine a status of patient recovery to evaluate effectiveness of therapy and/or to aid in determining appropriate treatment for rehabilitation and/or early discharge of the patient 112.

[0055] Embodiments of the present disclosure, thus, allow for robust and reproducible lesion segmentation in diffusion-based and/or perfusion-based images using a semi-automated method guided by user-defined seed points. Particularly, use of MRI data pre-processed using a determined DCF mitigates erroneous effects of system- specific b-factor and/or q-factor variability, thus allowing for greater accuracy and consistency in assessing stroke parameters. Accurate assessment of the stroke parameters may aid in determining an extent of infarction in the brain tissue, which in turn, may aid in determining appropriate treatment for the patient 112.

[0056] Additionally, embodiments of the present disclosure allow appropriate customization of the diffusion-based MRI data for satisfying one or more desired customization preferences that may vary due to differences between radiologists, clinical sites, and/or MRI systems. Use of the customized MRI data may optimize diffusion lesion segmentation performed using different imaging protocols, thus providing greater flexibility in assessing stroke parameters. An exemplary method for improving the accuracy of lesion segmentation using pre-processed and/or customized MRI data, in accordance with exemplary aspects of the present disclosure, will be described in greater detail with reference to FIG. 2.

[0057] FIG. 2 illustrates a flow chart 200 depicting an exemplary MR imaging method for improved assessment of lesion characteristics following ischemic strokes. Embodiments of the exemplary method may be described in a general context of computer executable instructions on a computing system or a processor. Generally, computer executable instructions may include routines, programs, objects, components, data structures, procedures, modules, functions, and the like that perform particular functions or implement particular abstract data types.

[0058] Embodiments of the exemplary method may also be practised in a distributed computing environment where optimization functions are performed by remote processing devices that are linked through a wired and/or wireless communication network. In the distributed computing environment, the computer executable instructions may be located in both local and remote computer storage media, including memory storage devices.

[0059] Further, in FIG. 2, the exemplary method is illustrated as a collection of blocks in a logical flow chart, which represents operations that may be implemented in hardware, software, or combinations thereof. The various operations are depicted in the blocks to illustrate the functions that are performed, for example, during diffusion-based image generation, pre-processing, and lesion segmentation phases of the exemplary method. In the context of software, the blocks represent computer instructions that, when executed by one or more processing subsystems, perform the recited operations.

[0060] The order in which the exemplary method is described is not intended to be construed as a limitation, and any number of the described blocks may be combined in any order to implement the exemplary method disclosed herein, or an equivalent alternative method. Additionally, certain blocks may be deleted from the exemplary method or augmented by additional blocks with added functionality without departing from the spirit and scope of the subject matter described herein. For discussion purposes, the exemplary method will be described with reference to the elements of FIG. 1.

[0061] As previously noted, successful recovery of a patient suffering from an acute ischemic stroke may depend upon accurate and rapid quantification of lesion characteristics computed from diffusion-based and perfusion-based images. Particularly, lesion characteristics may be accurately determined by identifying and segmenting probable lesion regions in the diffusion-based and perfusion-based images. By way of example, lesion segmentation in the diffusion-based and perfusion-based images may aid in determining a mismatch between diffusion-based and perfusion-based lesion volumes. The mismatch in the lesion volumes, in turn, may be used to determine PDM, which may be indicative of salvageable cerebral tissue that is at risk of infarction following the ischemic stroke.

[0062] To that end, in one embodiment, the probable lesion region in the diffusion-based images may be segmented using ADC maps corresponding to the probable lesion region. However, as previously noted, both ADC and diffusion-based lesion segmentation may depend on accuracy of a b-factor and/or q-factor representative of a diffusion sensitivity of the MRI system. Specifically, the b-factor and/or q-factor may vary due to system-specific characteristics such as gradient non-linearity and concomitant field effects. As previously noted, these system-specific characteristics may differ between different vendors and MRI systems, thus, resulting in inconsistent b-factor and/or q-factor values. Inconsistency in the b-factor and/or q-factor values, in turn, may render an estimated PDM value to be an unsuitable criterion for determining an appropriate treatment for the patient.

[0063] Accordingly, embodiments of the present disclosure describe a robust and reproducible method for improving lesion segmentation in diffusion-based images even in presence of b-factor and/or q-factor variability. To that end, in one embodiment, a patient is suitably positioned on an examination table associated with an MRJ system, such as the MRI system 100 of FIG. 1, for imaging. Particularly, the patient may be positioned such that a desired portion of brain, for example, a cerebral tissue of interest, is positioned within an FOV of the MRI system. Subsequently, the patient may be scanned to acquire imaging data corresponding to the desired portion in presence and/or absence of a contrast agent.

[0064] Further, at step 202, MRI data corresponding to a target volume of a subject may be received. In one example, the subject may be the patient 112 of FIG. 1. In certain embodiments, the MRJ data may be received from the MRI system that may be configured to scan the target volume positioned at a determined distance from an isocentre of the MRI system. The received MRI data, for example, may include diffusion-based images, perfusion-based images, k-space imaging data, surface coil intensity profiles, user-defined data, and/or system-specific imaging parameters used for scanning the target volume of the subject.

[0065] In a presently contemplated embodiment, the MRI data including the perfusion-based and diffusion-based images may be used to determine a lesion characteristic, such as PDM, for determining presence and/or extent of a lesion in the target volume. To that end, the perfusion-based images may be generated, for example, by performing computations on a time series of, for example, raw images subsequent to administration of a contrast agent. The perfusion-based images, thus obtained, may provide information regarding a variety of hemodynamic parameters corresponding to the target volume. The hemodynamic parameters, for example, may include CBV, CBF, MTT, and TTP. These hemodynamic parameters may be used to assess a rate of perfusion in the cerebral tissues, which in turn, may be used to determine lesion characteristics.

[0066] Moreover, the diffusion-based images may be generated such that an intensity of each image element or voxel in the diffusion-based images may reflect an accurate estimate of a rate of water diffusion at that location. Generally, mobility of water is driven by thermal agitation and is highly dependent on water's cellular environment. Accordingly, the diffusion-based images may be indicative of changes to a pathological state of the brain tissue corresponding to the target volume.

[0067] By way of example, diffusion-based images may capture early changes after an ischemic stroke, while more traditional MRI measurements such as spin-lattice relaxation time (T,) and spin-spin relaxation time (T2) rates may fail to capture these early changes. Further, diffusion-based images may be particularly useful when imaging tissues with isotropic water movement, such as grey matter in the cerebral cortex and major brain nuclei, because a rate of diffusion used in diffusion-based image reconstruction will appear to be substantially similar when measured along any axis.

[0068] Typically, diffusion-based imaging may be performed by applying diffusion sensitivity, for example, based on a b-factor and/or q-factor value. Sensitivity of the diffusion-based images to ischemic regions may be varied by varying the b and/or q-factor values corresponding to the diffusion-based images.

[0069] Accordingly, a signal intensity (S) of a voxel in a diffusion-based image such as a diffusion weighted image may be defined, for example, using equation (1):

(1) where M corresponds to magnetization, TE corresponds to echo-time, T2 corresponds to transverse relaxation time, Da corresponds to ADC and b corresponds to the diffusion sensitivity factor.

[0070] In one embodiment, the diffusion sensitivity b-factor, b, may be defined, for example, using equation (2):

(2) where y corresponds to gyromagnetic ratio, G corresponds to pulsed diffusion gradient amplitude, 8 corresponds to pulsed diffusion gradient duration, and A corresponds to a time separation between leading edges of the pulsed diffusion gradients.

[0071] Similarly, the diffusion sensitivity factor q may be defined, for example, using equation (3): where m corresponds to the length unit, meter.

[0072] Although, the present method may be used for mitigating various system imperfections, for clarity, the present method will be described with reference to mitigating effects of the b-factor variability on the diffusion-based MRI data. To that end, in one embodiment, if the diffusion sensitivity factor b is zero, a signal So, corresponding to a T2 -weighted image may be obtained. Additionally, a dimensionless exponential decay factor/may be defined, for example, using equation (4):

[0073] The estimated ADC (ADCe), in such an embodiment, may be computed, for example, as defined in equation (5):

[0074] Typically, a value of the exemplary decay factor / may vary because of system-specific factors such as gradient nonlinearities. In one embodiment, these variations may be represented by a system imperfection factor, s . Accordingly, the exponential decay factor/may now be represented as (exf). A percentage change in ADCe, thus, may be represented, for example, using equation (6):

[0075] Further, a percentage change in a measured diffusion-based imaging signal may be defined, for example, using equation (7):

[0076] Thus, for a given variation in the exemplary decay factor/, the percentage change in the measured diffusion-based imaging signal may often be lower than the percentage change in the estimated ADC, ADCe. Accordingly, a variation in the exemplary decay factor / may have different impacts on diffusion-based lesion segmentation methods that are based on ADC and/or those based on diffusion-based imaging intensities. Assessment of a PDM value using ADC-based lesion segmentation techniques or diffusion-based lesion segmentation techniques, thus, may not provide the most accurate PDM calculations in view of the b-factor variability.

[0077] Accordingly, conventional PDM calculations that entail determining a difference or a ratio between probable lesion regions identified from the diffusion-based images and corresponding perfusion-based images may result in discrepancies. Additionally, a lesion may include a range of tissue types with varying levels of ischemia and cellular damage owing to differences in stroke mechanism, anatomical location of the lesion, supplying vascular apparatus and/or time to onset of an ischemic stroke. Moreover, conventional PDM calculations using only ADC-based information, however, may not account for regional heterogeneity of the lesion, thus, failing to provide accurate information for diagnosis and treatment planning.

[0078] In contrast to such conventional PDM calculations that employ only ADC-based information, embodiments of the present disclosure determine PDM and/or regional lesion information using a combined ADC and diffusion-based lesion segmentation, such as the method described with reference to FIG. 1. The regional lesion information may be more accurately determined by using MRI data that is customized to satisfy one or more desired customization preferences. However, the desired customization preferences may vary due to differences in imaging protocols used by different radiologists, clinical sites, and/or MRI systems.

[0079] Accordingly, at step 204, a check may be performed to determine if there are one or more customization preferences. The customization preferences, for example, may include mitigating ill effects of system imperfections such as the b-factor and q-factor variability on the diffusion-based MRI data, a selection of particular scanning protocol, a shape of a region of interest for the diffusion-based lesion segmentation, and/or functions for post-processing the diffusion-based and/or perfusion-based images. If it is determined at step 204 that there are no customization preferences, diffusion- based lesion segmentation may be performed using the received MRI data, as depicted by step 206. Particularly, in one embodiment, the diffusion-based lesion segmentation may be performed using the combined diffusion-based and ADC-based lesion segmentation method described with reference to FIG. 1. The diffusion-based lesion segmentation may then be used for determining a pathological condition of the subject. A method for determining the pathological condition of the subject based on the diffusion-based lesion segmentation will be described in greater detail with reference to step 220.

[0080] However, at step 204, if it is determined that there are one or more customization preferences, the received MRI data may be customized to satisfy the one or more customization preferences. By way of example, if the customization preferences include mitigating ill effects of system imperfections such as the b-factor and q-factor variability on the diffusion-based MRI data, control may pass to step 208. Further, a check may be carried out to verify if one or more system imperfections are present. Upon determining the presence of the system imperfections, a further check may be performed to verify whether to correct the MRI data for mitigating the ill effects of the system imperfections, as depicted by step 208. In one embodiment, verifying whether to correct for the system imperfections may be performed interactively based on user-input and/or automatically based on an application-specific requirement.

[0081] Upon verifying that system imperfections are to be corrected at step 208, a DCF that may be used to reduce effect of the one or more system imperfections on the received MRI data may be identified, as depicted by step 210. As previously noted, the system imperfections, such as those caused by gradient related non-linearity and/or concomitant field effects as represented by the SIF may cause variations in b-factor and/or q-factor values used for computations during diffusion lesion segmentation. Magnitude of the SIF, however, may vary between different vendors, different field strengths, and/or across the FOV of MRI system, thus, resulting in inconsistent b-factor or q-factor values.

[0082] As previously noted, the b-factor and/or q-factor values may be used in determining diffusion-based measurements corresponding to one or more voxels in a diffusion-based image corresponding to the target volume. The diffusion-based measurements, in turn, may be used to determine certain lesion characteristics, such as PDM, corresponding to the target volume. Accordingly, any inconsistency in the fa-factor and/or q-factor values may result in erroneous PDM measurements, which may render the PDM measurements unsuitable for use in determining suitability of administering a thrombolytic agent to the patient.

[0083] Accordingly, in one embodiment, the DCF may be used for reducing any effects of variations in the SIF on the diffusion-based MRJ data. In one example, the DCF may be identified based on a priori information. The a priori information, for example, may include information obtained from previous diffusion-based and perfusion-based MR imaging exams, user input, and/or information derived from a database or published medical literature. Generally, a magnitude of the SIF at different distances from the isocentre of the MRI system may be known. However, tools to correct effects of the SIF variations may be unavailable in conventional systems. Accordingly, in certain embodiments of the present method, a value of SIF at a determined location of a desired slice and voxel of the target volume relative to the isocentre may be determined from the known values. Subsequently, a value that modifies the magnitude of the SIF such that the modified SIF magnitude is closer to a determined optimal value may be identified as the DCF. In one example, the DCF may be a value that drives the SIF at the determined location towards zero, which may be representative of absence of system imperfections.

[0084] At step 212, the determined DCF may be used to pre-process the received MRI data to generate pre-processed MRI data. The pre-processed MRI data may aid in enhancing diffusion lesion segmentation. It may be noted that the DCF may be a scalar or spatially varying value used to pre-process the acquired MRI data to mitigate effect of the system imperfections on the received MRI data.

[0085] Further, at step 214, an optimal value of at least one customization parameter that may be used to customize the pre-processed MRI data may be determined. In certain embodiments, the optimal value of the customization parameter may be determined based on a comparison with GT values and/or user-defined input. The optimal value of the customization parameter may be used to synthesize customized MRI data from the pre-processed MRI data based on the one or more customization preferences.

[0086] As previously noted, diffusion-based imaging protocols may differ owing to variations in one or more system-specific and/or user-defined customization preferences. By way of example, the customization preferences may vary based on an imaging protocol that may be used, such as DTI or DWI, number and type coils, magnetic field strength, and/or post-processing procedures used for mitigating inhomogeneity and imaging artifacts. Similarly, imaging protocols may also differ between different MRI systems and/or clinical sites. In one embodiment, these customization preferences may be received from the user and/or may be defined based on system or clinical site-specific imaging protocols. Accordingly, satisfying the different customization preferences may entail customization of the lesion segmentation method for optimal imaging.

[0087] In accordance with exemplary aspects of the present disclosure, the lesion segmentation method may be customized by customizing the pre-processed diffusion-based MRI data acquired in a particular acquisition scan using an optimal value of the customization parameter. To that end, the optimal value of the customization parameter may be selected such that there is greater agreement between lesion characteristic measurements determined using the lesion segmentation method and a corresponding reference value.

[0088] In certain embodiments, the reference value may be determined using a priori information obtained from diffusion-based images acquired using the one or more desired customization preferences. As previously noted, the a priori information, for example, may include information obtained from previous medical exams, user input, and/or information derived from a database or published medical literature. In one embodiment, for example, the a priori information may include previously acquired diffusion-based MRI data and the corresponding lesion segmentation performed using the previously acquired diffusion-based MRI data.

[0089] In another embodiment, the reference value may include a GT lesion segmentation available in the database. The GT lesion segmentation may be generated by a skilled and/or experienced radiologist by manually delineating a lesion in the diffusion-based images such that the GT lesion segmentation satisfies the desired customization preferences. Further, a lesion volume measured using the GT lesion segmentation may be used as the GT value. Additionally, one or more values of the lesion characteristic may be determined using the lesion segmentation method at different values of one or more customization parameters. To that end, the customization parameters used for optimizing the lesion segmentation method, for example, may include different DCF values, voxel intensities, transitional slopes, and/or mathematical functions.

[0090] In one embodiment, the lesion segmentation method may correspond to the combined diffusion-based and ADC-based lesion segmentation, such as the method described with reference to FIG. 1. As previously noted, the combined diffusion-based and ADC-based lesion segmentation may employ multilevel thresholding in different regions of the target volume along with anatomical masks, ADC maps, functional maps, reference data and/or user-defined seed points for accurate diffusion-based lesion segmentation. The diffusion-based lesion segmentation, in turn, may provide measurements of the lesion characteristic values at the different values of the customization parameter, for example, at different DCF values and/or different voxel intensities.

[0091] Subsequently, corresponding differences between the GT value and each of these one or more lesion characteristic values determined at different values of the customization parameter may be computed. In accordance with exemplary aspects of the present disclosure, the values of the customization parameter corresponding to the smallest computed difference may be selected as the optimal value of the customization parameter. As previously noted, lower the computed difference, greater is an agreement between the GT value and lesion characteristic values determined using the lesion segmentation method. Accordingly, in one embodiment, a value of the customization parameter that provides the greatest agreement between the GT value and lesion characteristic values determined using the lesion segmentation method may be identified as the optimal value for use in customizing the diffusion-based MRI data.

[0092] In an alternative embodiment, however, the optimal value of the customization parameter may be determined based on user-input received in real-time or prior to imaging. To that end, the user, such as a radiologist, may determine the optimal value of the customization parameter for synthesizing customized MRI data, for example, based on the a priori information and/or previous experience.

[0093] Furthermore, in one embodiment, the customized MRI data may be synthesized by computing a product of the customization parameter and the pre-processed MRI data. In certain other embodiments, other customization functions may be used to process the pre-processed MRI data with the customization parameter to synthesize the customized MRI data. Particularly, the customization parameter may customize the pre-processed MRI data such that the customized MRI data satisfies the desired customization preferences as defined by clinical site protocols, user preferences, and/or capabilities of the MRI system. Control may subsequently be passed to step 218.

[0094] With returning reference to step 208, if it is determined that the system imperfections need not be corrected, an optimal value of at least one customization parameter that may be used to customize the received MRI data may be determined, as depicted by step 216. The optimal value of the customization parameter may be used to synthesize customized MRI data from the received MRI data based on the one or more customization preferences. In one example, the optimal value of the customization parameter may be determined at step 216, for example, using the method described with reference to step 214. Control may subsequently be passed to step 218.

[0095] Referring now to step 218, the customized MRI data may be used to perform improved diffusion-based lesion segmentation. In one example, improved diffusion-based lesion segmentation may be performed using the combined diffusion-based and ADC-based lesion segmentation described with reference to FIG. 1. Customizing the pre-processed and/or received MRI data using the optimal values of the customization parameters improves accuracy of the ADC maps. Further, use of the optimal value of the customization parameters optimizes the contrast of the diffusion-based signal acquired in presence of system imperfections to match the contrast in diffusion-based MRI data acquired in absence of system imperfections, thereby facilitating more accurate lesion characteristic measurements.

[0096] Additionally, in certain embodiments, use of the customized MRI data may result in lesion characteristic measurements that are quantitatively closer to a desired GT lesion characteristic value as opposed to lesion characteristic measurements determined using the received MRI data.

[0097] Accordingly, at step 220, a pathological condition of the subject may be determined based at least on the improved diffusion-based lesion segmentation. By way of example, the determined lesion characteristic measurements may allow for identification of ischemic penumbra and/or a core ischemic zone in the cerebral tissues. Subsequently, an appropriate treatment decision may be determined based on the identified zones. Although the present embodiment only describes use of the improved diffusion-based lesion segmentation, in certain embodiments, a decision to administer a thrombolytic agent to a stroke patient may be further based on certain additional information and/or additional criteria. The additional criteria, for example, may be derived from previous medical exams, user input, and/or information derived from a database or published medical literature.

[0098] Further, in one embodiment, the additional criteria for determining a treatment decision may correspond to the Diffusion Weighted Imaging Evaluation For Understanding Stroke Evaluation 2 (DEFUSE 2) criteria. It may be noted that the DEFUSE 2 criteria are based on lesion volumes determined using PWI (PWIlv) and DWI (DWIIlv). Accordingly, in one exemplary implementation, a dichotomous agreement may be determined between a GT PDM value and PDM values computed from lesion volumes determined using a conventional ADC-only lesion segmentation method and a combined diffusion-based and ADC-based lesion segmentation method. Particularly, the dichotomous agreement may be achieved on determining a suitable PDM value. A PDM value may be considered suitable for treatment when the DEFUSE 2 criteria are satisfied. In one embodiment, the DEFUSE 2 criteria may be satisfied when (PWIlv (with Tmax > 6s)/DWIIlv) > 1.8, (DWIlv < 70 ml), (PWIlv (with Traax > 10s) < 100 ml), and (PWIlv (with Tmax > 6s) - DWIlv > 15 ml).

[0099] More specifically, satisfying the DEFUSE 2 criteria may entail a condition that a lesion volume computed using perfusion-based images is at least twice as large as a diffusion lesion volume computed using diffusion-based images. Further, satisfying the DEFUSE 2 criteria may also entail another condition that a lesion volume computed using diffusion-based images is less than, for example, 70 cubic centimeters (cc) and/or that a minimum difference between the lesion volumes computed using diffusion-based images and the perfusion-based images is at least 15 cc. In certain embodiments, the thrombolytic agent may be administered to the patient only if all of the criteria are satisfied. In alternative embodiments, however, the thrombolytic agent may be administered to the patient if at least some of the criteria are satisfied. Accuracy of the treatment decision, thus, may depend upon accuracy of lesion characteristic measurements obtained in presence of the system-specific imperfections.

[0100] Embodiments of the present method allow for pre-processing and/or customizing the diffusion-based MRI data for obtaining accurate lesion characteristic measurements. Particularly, the MRI data may be pre-processed using the DCF to mitigate ill effects caused by the b-factor and/or q-factor inconsistencies on the lesion characteristic measurements, and in turn, stroke assessment. Additionally, embodiments of the present method may also allow for lesion characteristic measurements that are closer to the GT lesion characteristic value when compared to the values determined using the originally acquired MRI data. FIGs. 3-5 depict exemplary lesion characteristic measurements that are representative of the robustness of the method described with reference to FIG. 2 for use in stroke evaluation.

[0101] FIG. 3 illustrates a graphical representation 300 of exemplary lesion volumes computed in an exemplary implementation of the method described with reference to FIG. 2. Particularly, reference numeral 302 represents images that may be used to compute lesion volumes using a conventional ADC-only lesion segmentation method. Further, reference numeral 304 represents images that may be used to compute lesion volumes using the combined diffusion-based and ADC-based lesion segmentation method, such as the method described with reference to FIG. 1. In accordance with exemplary aspects of the present disclosure, an MRI system such as the MRI system 100 of FIG. 1 may be configured to perform the combined diffusion-based and ADC-based lesion segmentation method. Specifically, the MRI system may be configured to perform the combined diffusion-based and ADC-based lesion segmentation method using pre-processed and/or customized MRI data synthesized using the method described with reference to FIG. 2.

[0102] To that end, in the exemplary implementation, diffusion-based imaging of a VOI may be performed using a first b-factor value of about 0 second/millimetre2 (s/mm2) to generate a first signal, S0. Further, diffusion-based imaging may be also performed using a second b-factor value of about 1000 s/mm2 to generate a second signal, Si. Additionally, an ADC map may be generated using the first and the second b-factor values. The ADC map and the first signal, S0,, may be used to synthesize a plurality of diffusion-based MRI data sets (bk). The diffusion-based MRI data sets, bk, may be represented, for example, using equation (8): where k may correspond to numerical values differing by an exemplary value of about 50.

[0103] In one exemplary implementation, values corresponding to k, for example, may range from about 800 to about 1200 in increments of about 50. Accordingly, a customization parameter, S, corresponding to these diffusion-based MRI data sets, bk generated at the second b-factor value of 1000 s/mm2 may range from about 0.8 to about 1.2.

[0104] Further, an estimated ADC, ADCe, may be computed for segmenting a probable lesion region in diffusion-based images using bk and S0 . Subsequently, the MRI system may be configured to perform the exemplary combined diffusion-based and ADC-based lesion segmentation method using multilevel thresholding in different regions of the VOL To that end, the MRI system may use one or more anatomical masks, ADC maps, reference data and/or user-defined seed points for accurate diffusion-based lesion segmentation using the multilevel thresholding.

[0105] As previously described with reference to FIG. 1, the MRI system may be configured to perform lesion segmentation in the perfusion-based images based on the lesion segmentation in the diffusion-based images, user-defined seed points, contralateral analysis of the cerebral tissues based on one or more functional maps, and/or the reference data. Further, the MRI system may be configured to compute lesion volumes corresponding to lesion regions identified in the diffusion-based and perfusion-based images at different values of the customization parameter, £. Additionally, a GT lesion volume may be computed based on a probable lesion region in the diffusion-based images identified by a skilled and/or experienced radiologist. To that end, the radiologist may manually delineate the probable lesion region on the diffusion-based images using ADC, while also delineating an ischemic hypo-perfused area on a Tmax map corresponding to the perfusion-based images.

[0106] The graphical representation 300 depicts a comparative analysis of exemplary lesion volumes computed at different values of the customization parameter (£) using a conventional ADC-only lesion segmentation and the exemplary combined diffusion-based and ADC-based lesion segmentation. Particularly, the graphical representation 300 depicts the comparative analysis of exemplary lesion volumes with reference to the GT lesion volume. In one example, the GT lesion volume may be about 91 cc.

[0107] As depicted in FIG. 3, error between the GT lesion volume and lesion volumes computed using the ADC-only segmentation at the different values of customization parameter may be typically higher than the error between the GT lesion volume and the lesion volumes computed using the combined diffusion-based and ADC-based segmentation. For example, in FIG. 3, the lesion volume computed at £"=0.8 using the ADC-only segmentation corresponds to about 405 cc, thus indicating more than half of the brain as being infarcted. Such overestimation of the infarction may lead to the radiologist erroneously determining the cerebral tissues as unsalvageable. Accordingly, the radiologist may rule out administration of thrombolytic treatment, thus risking patient health.

[0108] However, the lesion volume computed at the customization parameter value £■=0.8 using the exemplary combined diffusion-based and ADC-based segmentation corresponds to about 170 cc. It may be noted that although the lesion volume determined using the exemplary method described with reference to FIG. 2 is higher than the GT lesion volume of 91 cc, the determined lesion volume may still satisfy the DEFUSE 2 criteria used for making the treatment decision. Accordingly, a corresponding estimation of the infarcted region based on the lesion volume measured using the exemplary method may not result in a change in the treatment decision.

[0109] Further, FIG. 4 illustrates a graphical representation 400 depicting a comparison of a GT lesion volume and exemplary lesion volumes computed at different values of customization parameter S, using a conventional ADC-only lesion segmentation and the combined diffusion-based and ADC-based lesion segmentation. As depicted in FIG. 4, the lesion volumes computed using the combined diffusion-based and ADC-based lesion segmentation are closer in value to the GT lesion volume than the lesion volumes computed using the conventional ADC-only lesion segmentation. Particularly, the combined diffusion-based and ADC-based lesion segmentation method performs better than the conventional ADC-only lesion segmentation method at each of the illustrated values of the customization parameter £.

[0110] Moreover, FIG. 5 illustrates a graphical representation 500 depicting a determined agreement between a GT value corresponding to a lesion characteristic and exemplary values corresponding to the lesion characteristic computed at different values of the customization parameter £, using a conventional ADC-only lesion segmentation and the exemplary combined diffusion-based and ADC-based lesion segmentation. In a presently contemplated embodiment, DEFUSE 2 criteria may be used to determine the agreement between the GT lesion characteristic value and the lesion characteristic values computed using the conventional ADC-only lesion segmentation method and the combined diffusion-based and ADC-based lesion segmentation method.

[0111] As depicted in FIG. 5, for all values of the customization parameter £, better agreement is observed with the GT value for the combined diffusion-based and ADC-based lesion segmentation method compared to the conventional ADC-only lesion segmentation method. Particularly, it may be noted that in the embodiment illustrated in FIG. 5, a maximum value of agreement (K) is obtained for a customization parameter £ = 0.95, with the combined diffusion-based and ADC-based segmentation. Accordingly, as described with reference to step 210 of FIG. 2, 0.95 may be identified as the DCF.

[0112] As previously noted, the MRI data may be may be multiplied with the identified DCF to synthesize the pre-processed MRI data. The pre-processed data may be used to improve the performance of the diffusion-based and/or the perfusion-based lesion segmentations, and in turn, the lesion characteristic values computed based on the diffusion-based and/or the perfusion-based lesion segmentations. In accordance with exemplary aspects of the present disclosure, the lesion characteristic value, thus computed, may be advantageously used for rapidly and robustly assessing stroke parameters and for further treatment and planning.

[0113] Embodiments of the present disclosure, thus, provide systems and methods for improving assessment of lesion characteristics following acute ischemic strokes using customized MRI data. More particularly, the embodiments describe use of the customized MRI data in a combined diffusion-based and ADC-based lesion segmentation technique that is robust to variations in user-defined inputs and b-factor variability, thus allowing for accurate and fast quantification of the lesion volume and/or PDM. The accurate and fast quantification of the lesion volumes and/or PDM may allow an accurate estimation of an extent of infarction in the cerebral tissues for ascertaining suitability of administering thrombolytic treatment to a stroke patient.

[0114] It may be noted that the foregoing examples, demonstrations, and process steps that may be performed by certain components of the present systems, for example, by the system controller 104, the processing subsystem 132, and the image processing unit 138 may be implemented by suitable code on a processor-based system, such as a general-purpose or a special-purpose computer. It may also be noted that different implementations of the present disclosure may perform some or all of the steps described herein in different orders or substantially concurrently.

[0115] Additionally, the functions may be implemented in a variety of programming languages, including but not limited to Ruby, Hypertext Pre-processor (PHP), Perl, Delphi, Python, C, C++, or Java. Such code may be stored or adapted for storage on one or more tangible, machine-readable media, such as on data repository chips, local or remote hard disks, optical disks (that is, CDs or DVDs), solid-state drives, or other media, which may be accessed by the processor-based system to execute the stored code.

[0116] Although specific features of various embodiments of the present disclosure may be shown in and/or described with respect to some drawings and not in others, this is for convenience only. It is to be understood that the described features, structures, and/or characteristics may be combined and/or used interchangeably in any suitable manner in the various embodiments, for example, to construct additional assemblies and techniques for use in MRI.

[0117] While only certain features of the present disclosure have been illustrated and described herein, many modifications and changes will occur to those skilled in the art. It is, therefore, to be understood that the appended claims are intended to cover all such modifications and changes as fall within the true spirit of the invention.

We claim:

1. A method for magnetic resonance imaging, comprising: receiving magnetic resonance imaging data corresponding to a target volume of a subject, wherein the magnetic resonance imaging data comprises one or more diffusion-based magnetic resonance images, one or more perfusion-based magnetic resonance images, or a combination thereof; determining an optimal value of at least one customization parameter, wherein the at least one customization parameter is configured to customize the magnetic resonance imaging data based on one or more customization preferences to synthesize customized magnetic resonance imaging data; performing diffusion-based lesion segmentation using the customized magnetic resonance imaging data; and determining a pathological condition of the subject based on the diffusion-based lesion segmentation.

2. The method of claim 1, wherein determining the optimal value of the at least one customization parameter comprises determining a data correction factor that reduces effect of one or more system imperfections on the magnetic resonance imaging data based on the one or more customization preferences.

3. The method of claim 2, wherein determining the at least one customization parameter comprises determining the data correction factor based on a gradient non-linearity, concomitant magnetic field effects, b-factor variability, q-factor variability, or combinations thereof.

4. The method of claim 1, further comprising determining the at least one customization parameter based on a priori information, user input, or a combination thereof.

5. The method of claim 1, further comprising determining the one or more customization preferences based on a determination whether to correct for one or more system imperfections, user input, application-specific requirements, clinical site-specific requirements, system-specific requirements, or combinations thereof.

6. The method of claim 1, wherein determining the optimal value of the at least one customization parameter comprises: determining a reference value of a lesion characteristic based on a priori information, user input, or a combination thereof; determining one or more values of the lesion characteristic using a lesion segmentation method corresponding to different values of the at least one customization parameter; computing corresponding differences between the reference value and each of the one or more values of the lesion characteristic at the different values of the at least one customization parameter; and identifying the optimal value of the at least one customization parameter based on a corresponding value of the at least one customization parameter at which at least one of the computed differences is minimal.

7. The method of claim 1, wherein determining the optimal value of the at least one customization parameter comprises determining one or more scalar parameters, one or more spatially varying parameters, a set of operations, one or more mathematical functions, or combinations thereof, such that the optimal value of the at least one customization parameter customizes the received magnetic resonance imaging data based on the one or more customization preferences.

8. The method of claim 1, further comprising: determining a lesion characteristic based on the diffusion-based lesion segmentation, wherein the lesion characteristic corresponds to a lesion volume; using the determined lesion characteristic to determine the pathological condition of the subject.

9. The method of claim 1, wherein performing the diffusion-based lesion segmentation comprises: regenerating one or more diffusion-based magnetic resonance images corresponding to the target volume using the customized magnetic resonance imaging data; receiving one or more seed points from a user; generating one or more diffusion-based parametric maps corresponding to the one or more diffusion-based magnetic resonance images and one or more perfusion-based parametric maps corresponding to the one or more perfusion- based magnetic resonance images, wherein the one or more diffusion-based parametric maps comprise one or more apparent diffusion coefficient maps;

generating one or more masks corresponding to one or more regions of interest associated with the target volume; performing lesion segmentation in the one or more diffusion-based magnetic resonance images based on iterative and adaptive multilevel thresholding as applied to different regions of the one or more diffusion-based magnetic resonance images; performing lesion segmentation in the one or more perfusion-based magnetic resonance images based on one or more of the lesion segmentation in the one or more diffusion-based magnetic resonance images, the one or more seed points, contralateral analysis based on the one or more diffusion-based parametric maps, the one or more perfusion-based parametric maps, reference data, or combinations thereof; and determining one or more lesion characteristics based on the lesion segmentation in the one or more diffusion-based images and the one or more perfusion-based images.

10. A magnetic resonance imaging system, comprising: a scanner configured to scan a target volume of a subject to acquire magnetic resonance imaging data; a processing subsystem operationally coupled to the scanner and configured to: receive the magnetic resonance imaging data corresponding to the target volume, wherein the received magnetic resonance imaging data comprises one or more diffusion-based magnetic resonance images, one or more perfusion-based magnetic resonance images, or a combination thereof; identify an optimal value of at least one customization parameter, wherein the customization parameter is configured to customize the received magnetic resonance imaging data based on one or more customization preferences to synthesize customized magnetic resonance imaging data; perform diffusion-based lesion segmentation using the customized magnetic resonance imaging data; and determine a pathological condition of the subject at least based on the diffusion-based lesion segmentation.