METHOD AND SYSTEM FOR SEMI-AUTOMATED PERFUSION-DIFFUSION MISMATCH ASSESSMENT

BACKGROUND

[0001 ] Embodiments of the present disclosure relate generally to magnetic resonance (MR) imaging. More particularly, the present disclosure relates to a method and a system for semi-automated assessment of abnormalities in brain tissues in an ischemic stroke using postprocessing and analysis of MR images.

(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 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 may cause oxygen deprivation in brain tissue, which if left untreated for more than a few hours, leads to necrosis of the brain tissue.

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

[0004] Generally, the brain tissue ceases to function if deprived of oxygen for more than sixty to ninety seconds. However, after approximately three hours, the brain tissue may suffer irreversible damage possibly leading to cerebral infarction or death of the brain tissue. Accordingly, only a narrow window of time after an 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, may not be automatic because reperfusion of severely hypoperfused areas may 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 brain 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 for successful patient recovery.

[0006] Typically, an 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. The diagnostic images may be generated using a magnetic resonance imaging (MRI) system. Particularly, in the case of acute ischemic stroke, diffusion weighted imaging (DWI) and/or perfusion weighted imaging (PWI) may be employed for clinical assessment of infarcted core ischemic zones. Further, a perfusion-diffusion mismatch (PDM) may be determined based on lesion volumes computed from diffusion weighted (DW) and/or perfusion weighted (PW) images. The PDM, in turn, may be used to estimate the salvageable brain tissue that is at risk of infarction following the stroke.

[0007] To that end, certain MRI methods employ automated lesion segmentation methods for assessing the extent and type of the stroke. Conventionally, these methods are known to use apparent diffusion coefficient (ADC) and/or DWI contrast for assessing the lesion. Histogram distributions of ADC and DWI of normal and infarcted tissues, however, are known to overlap, thus resulting in over or under-estimation of the lesion volumes. Accordingly, in certain scenarios, manual intervention may be employed in the lesion assessment. Manual lesion assessment, however, depends upon the skill and experience of the medical practitioner. Use of manual intervention by itself, thus, may lead to inconsistent assessments that may not be robust and/or reproducible for a large group of acute ischemic stroke patients.

BRIEF DESCRIPTION

[0008] In accordance with certain aspects of the present disclosure, a method for MRI is disclosed. To that end, one or more perfusion weighted images and one or more diffusion weighted images corresponding to a volume of interest (VOI) of a subject may be generated. Further, one or more seed points may be received from a user. Additionally, one or more diffusion weighted parametric maps corresponding to the one or more diffusion weighted images and one or more PW parametric maps corresponding to the one or more perfusion weighted images may be generated. The one or more diffusion weighted parametric maps may include one or more apparent diffusion coefficient maps. Moreover, one or more masks corresponding to one or more regions of interest corresponding to the VOI may be generated. Further, lesion segmentation in the one or more diffusion weighted images may be performed based on iterative and adaptive multilevel thresholding.

Particularly, the multilevel thresholding may be applied in different regions of the one or more diffusion weighted images using feedback from the one or more apparent diffusion coefficient maps, the one or more perfusion weighted parametric maps, one or more of the masks, the seed points, and/or the reference data. Additionally, lesion segmentation in the one or more perfusion weighted images may be performed based on the lesion segmentation in the one or more diffusion weighted images, the one or more seed points, contralateral analysis based on the one or more diffusion weighted parametric maps, the one or more perfusion weighted parametric maps, and/or the reference data. Subsequently, one or more characteristics corresponding to the VOI affected by an ischemic stroke may be determined based on the lesion segmentation in the one or more diffusion weighted images and the one or more perfusion weighted images.

[0009] In accordance with another aspect of the present disclosure, an MRI system may be presented. The MRI system may include a scanner configured to scan a volume of interest in a brain region of a subject to acquire imaging data and one or more input-output devices configured to receive one or more seed points from a user. The MRI system may further include a processing subsystem operationally coupled to one or more of the scanner and the input-output devices. The processing subsystem may be configured to generate one or more perfusion weighted images and one or more diffusion weighted images corresponding to a VOI of a subject. The processing subsystem may also be configured to generate one or more diffusion weighted parametric maps corresponding to the one or more diffusion weighted images and one or more perfusion weighted parametric maps corresponding to the one or more perfusion weighted images.

The one or more diffusion weighted parametric maps may include one or more apparent diffusion coefficient maps. Additionally, the processing subsystem may be configured to generate one or more masks corresponding to one or more regions of interest corresponding to the VOI. Moreover, the processing subsystem may be configured to perform lesion segmentation in the one or more diffusion weighted images based on iterative and adaptive multilevel thresholding as applied to in different regions of the one or more diffusion weighted images. Particularly, the processing subsystem may be configured to apply the multilevel thresholding to different regions of the one or more diffusion weighted images using feedback from the one or more apparent diffusion coefficient maps, one or more of the masks, the seed points, and/or reference data. Furthermore, the processing subsystem may be configured to perform lesion segmentation in the one or more perfusion weighted images based on one or more of the lesion segmentation in the one or more diffusion weighted images, the one or more seed points, contralateral analysis based on the one or more diffusion weighted parametric maps, the one or more perfusion weighted parametric maps, and/or the reference data. Subsequently, the processing subsystem may be configured to determine one or more characteristics corresponding to the brain tissue affected by an ischemic stroke based on the lesion segmentation in the one or more diffusion weighted images and the one or more perfusion weighted images.

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:

[00111 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 exemplary seed points input by a user, in accordance with aspects of present disclosure;

|0014] FIG. 4 is a flowchart depicting an exemplary method for performing lesion segmentation in DW images described with reference to FIG. 2, in accordance with aspects of present disclosure;

[0015| FIG. 5 is a flowchart depicting an exemplary method for performing lesion segmentation in PW images described with reference to FIG. 2, in accordance with aspects of present disclosure;

[0016] FIG. 6 is a graphical representation depicting reproducibility of an exemplary lesion segmentation in DW images performed using the method described with reference to FIG. 4, in accordance with aspects of present disclosure;

|0017] FIG. 7 is a graphical representation depicting reproducibility of an exemplary lesion segmentation in PW images performed using the method described with reference to FIG. 5, in accordance with aspects of present disclosure;

[0018] FIG. 8 illustrates exemplary images depicting a comparison between an exemplary lesion segmentation in a DW image performed using the method of FIG. 4 with a corresponding ground-truth (GT) lesion segmentation, in accordance with aspects of present disclosure; and

[0019] FIG. 9 illustrates exemplary images depicting a comparison between an exemplary lesion segmentation in a PW image performed using the method of FIG. 5 with a corresponding GT lesion segmentation, in accordance with aspects of present disclosure.

DETAILED DESCRIPTION

[0020| The following description presents exemplary systems and methods for providing a semi-automated assessment of abnormalities in brain tissue in an ischemic stroke. Particularly, embodiments illustrated hereinafter disclose an MRI system that may be configured to provide robust and reproducible lesion segmentation in MRI images. The lesion segmentation, in turn, aids in determining medically relevant brain tissue characteristics for use in diagnosing and managing ischemic strokes.

|0021] 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 perfusion based 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 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.

[0022] FIG. 1 illustrates an MRI system 100 configured for MR imaging. Particularly, in certain embodiments, the MRI system 100 may be configured for use in a semi-automated assessment of brain tissue in ischemic strokes. In certain other embodiments, however, an independent system or workstation may perform the semi-automated assessment based on one or more images received from the MRI system 100. Alternatively, the MRI system 100 and the independent system may be configured to perform the semi-automated assessment jointly and/or in a distributed manner.

[0023] 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. 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)ofthe scanner 102.

[0024] 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.

|0025| 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.

[00261 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.

[0027] Particularly, in one embodiment, the image processing unit 138 may be configured to use the digitized information to generate PW and DW images from the acquired MRI data. Further, the image processing unit 138 may also be configured to identify a lesion or a region of infarct in the DW and PW images. In certain embodiments, the image processing unit 138 may also be configured to identify and/or segment the lesion based on one or more seed points input by a medical practitioner 140 through the operator interface 106.

[0028] To that end, the operator interface 106 may further 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 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 view multiple images generated as part of a determined stroke imaging protocol and/or identify a VOI corresponding to the lesion in the PWI and/or the DW 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.

[0029] 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. To that end, 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.

|0030] In one embodiment, the image processing unit 138 may be configured to transmit the requested information to the display 148 to aid the medical practitioner 140 in deciding whether to administer a thrombolytic agent to the patient 112. In certain embodiments, the requested information may also include other medically relevant information, for example, features of interest, structural characteristics, and/or functional parameters such as blood flow in the target VOL The image processing unit 138 may be configured to use the structural characteristics such as location and size of the lesion and functional information such as tissue perfusion parameters, lesion volumes, and/or PDM for ascertaining the pathological condition of the target VOI in the brain tissue.

[0031] To that end, the image processing unit 138 may be configured to generate a plurality of diagnostic images for ascertaining the pathological condition of the target VOI. In one embodiment, for example, the image processing unit 138 may be configured to generate the diagnostic images, for example, using PWI, magnetic resonance angiography (MRA), DWI and/or DWI with fluid attenuation at different b-factor values. By way of example, the b-factor values may range from about 0 second/millimetre2 (s/mm2) to greater than 1000 s/mm2.

[0032] In another embodiment, the image processing unit 138 may be configured to generate the diagnostic images, for example, using fluid-attenuated inversion recovery (FLAIR), susceptibility weighted imaging (SWI), magnetic resonance spectroscopic imaging (MRSI) and/or chemical exchange saturation transfer (CEST)). Particularly, the image processing unit 138 may be configured to generate the diagnostic images such that a selected point in a first image, for example a FLAIR image, corresponding to the VOI is rendered in the same frame of reference as a corresponding point in a second image, for example a DW image corresponding to the VOI. To that end, the image processing unit 138 may be configured to employ image geometry and/or image registration methods to render different diagnostic images in the same frame of reference. Rendering the different diagnostic images in the same frame of reference may allow the medical practitioner 140 to input the seed points in any of the diagnostic images, for example, a MRA image. The image processing unit 138 may be configured to render the seed point in a corresponding location in a DW image for further evaluation.

[0033] In one embodiment, for example, the image processing unit 138 may be configured to use the seed point rendered in the DW image to perform a semi-automated segmentation of the lesion in the DW and PW images. More specifically, the image processing unit 138 may be configured to perform the semi-automated lesion segmentation guided by one or more seed points received from the user, one or more anatomical masks, one or more parametric maps such as ADC maps, PWI parametric maps, and/or reference data. To that end, the anatomical masks, for example, may correspond to different regions in the brain such as cerebrum, cerebellum, and brain ventricles. The anatomical masks may aid in dividing an image corresponding to the brain into different regions for more efficient lesion segmentation.

[0034] Further, the reference data, for example, may include previously acquired patient information, atlas-based anatomical information, and/or reference images corresponding to different pathological conditions such as an acute ischemic stroke and/or a chronic stroke to provide better guidance for the lesion segmentation. The previously acquired patient information, in turn, may include electrocardiogram (ECG) signals, resting state MRI data, functional MRI data, vascular information, metabolic information, arterial spin labeling (ASL) information, MR relaxation mechanisms, and/or spectroscopic information. In certain embodiments, the image processing unit 138 may be configured to use the reference data to filter patient-specific pathology data and remove false positive data from a region identified as the lesion.

[0035] 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 reconstructed 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.

[0036] 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 DW images. Particularly, multilevel thresholding employs a plurality of thresholds to segment the DW image into a plurality of segments. To that end, 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 DW 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 D W image.

(0037] In a presently contemplated embodiment, multilevel thresholding may be used to segment the DW image into a plurality of segments based on one or more determined ranges of intensities of voxels in the DW image. By way of example, the DW 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 segmented and 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.

|0038] Accuracy of lesion segmentation using multilevel thresholding, however, may depend upon an accuracy of DWI data acquisition by the MRI system 100. In certain scenarios, the DWI data acquisition may be corrupted by patient motion and/or system imperfections. DWI trace maps, DW images and/or ADC maps generated from DWI data acquired during such a corrupt DWI acquisition, thus, may exhibit anisotropy, thereby resulting in false positives during multilevel thresholding.

|0039] Conventional lesion segmentation methods fail to address anisotropy in the DWI data due to corruption of the DWI acquisition, and thus, continue to use the corrupted DWI data for lesion segmentation and subsequent lesion characteristic analysis. Embodiments of the present disclosure, however, allow for mitigation of the anisotropy in the DWI data prior to use in lesion segmentation and/or the subsequent lesion characteristic analysis. By way of example, in one embodiment, the anisotropy in the DWI data may be identified manually by the medical practitioner 140 and/or determined automatically by the image processing unit 138 using a DWI anisotropy atlas, a diffusion tensor imaging (DTI) anisotropy atlas, and/or using texture classification based methods.

[0040| In accordance with exemplary aspects of the present disclosure, the image processing unit 138 may be configured to adapt the multilevel thresholding parameters so as to mitigate the identified anisotropy in the DWI data. To that end, in one embodiment, the image processing unit 138 may be configured to adapt parameter settings used in performing multilevel thresholding in the DW images. 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 such as distance-based parameters for robust lesion segmentation

[0041] Moreover, in certain embodiments, one or more additional metrics may be used in conjunction with multilevel thresholding and user provided seed inputs to aid in isolating and/or collating regions that represent objects of interest in the DW images. By way of example, distance based functions and/or size based region merging may be used to aid in isolating and/or collating the objects of interest in the DW images. To that end, the distance-based functions and/or the size-based region merging, for example, may include functions that provide the additional metrics based on Euclidean distances, local variances, and/or information derived from the user-provided seed points. Use of such additional metrics to isolate and/or collate the objects of interest from the D W image may allow for accurate estimation of a location and extent of the lesion.

[0042] Accordingly, in one embodiment, the image processing unit 138 may be configured to use the additional metrics for implementing adaptive and iterative multilevel thresholding in the DW images. Particularly, the image processing unit 138 may be configured to perform the multilevel thresholding based on multi-scale ADC values corresponding to a region identified as a probable lesion (probable lesion region), the anatomical masks, the seed points, and/or the reference data. In particular, the image processing unit 138 may be configured to apply multilevel thresholding to the DW images iteratively. To that end, the multi-scale ADC values may refer to a plurality of ADC threshold values that may be employed to segment the probable lesion region during different iterations of multilevel thresholding in the DW image. 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 regions that have been erroneously identified (false positive regions) as corresponding to the probable lesion region. Removal of these false positive regions, thus aiding in determining the lesion characteristics. Furthermore, as previously noted, one or more parameters, such as, voxel intensity thresholds, number of threshold levels, and/or use of distance-based threshold parameters may be adapted to mitigate anisotropy in DWI data.

[0043J Conventionally, both the ischemic lesion and the hemorrhagic region correspond to low ADC map values. Accordingly, use of conventional ADC-only lesion segmentation may not allow differentiation between the ischemic lesion and the hemorrhagic region. Accordingly, embodiments of the present disclosure use a combination of such adaptive and iterative multilevel thresholding with multi-scale ADC values not only to identify an ischemic lesion but also to exclude and/or independently identify one or more regions corresponding to probable hemorrhages. More particularly, use of a combined ADC and DWI-based lesion segmentation, such as the method described with reference to FIG. 2, may allow identification of the ischemic lesion. More specifically, the combined ADC and DWI-based lesion segmentation may identify the probable lesion region as not to be of ischemic origin if the probable lesion region in the DW image corresponds to low intensity values even though corresponding ADC map values are also low.

[00441 Additionally, the image processing unit 138 may be configured to perform lesion segmentation in the PW images based on the lesion segmentation in the DW images, deterministic and probabilistic data statistics from the one or more PW parametric maps, the seed points, contralateral analysis of the brain tissue based on the parametric maps corresponding to the PW images, and/or the reference data.

[0045] The image processing unit 138 may also be configured to determine one or more characteristics corresponding to the brain tissue affected by an ischemic stroke based on the lesion segmentation in the DW and PW 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. The medical practitioner 140 may also 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.

[0046] Embodiments of the present disclosure, thus, allow for robust and reproducible lesion segmentation in DW and/or PW images using a semi-automated method guided by user-defined seed point inputs. Particularly, use of multilevel thresholding, the reference data, and/or the ADC maps, combined with knowledge of the anatomy, and/or mitigation of MR image artifacts through preprocessing and/or postprocessing in the present lesion segmentation method allows 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. An exemplary method for MR imaging for use in accurately determining various stroke parameters based on lesion segmentation in PW and DW images, in accordance with certain aspects of the present disclosure, will be described in greater detail with reference to FIG. 2.

[0047] FIG. 2 illustrates a flow chart 200 depicting an exemplary method for MR imaging. 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.

[0048] 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.

[0049] 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 DW and PW image generation 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.

[0050J 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 FIG. 1.

[0051J Successful recovery of a patient suffering from an acute ischemic stroke may depend upon accurate and swift quantification of lesion characteristics. To that end, the lesion characteristics may be determined using images generated using PWI, DWI and/or other complementary scans such as FLAIR and MRA. Particularly, lesion characteristics may be accurately determined by identifying and segmenting a probable lesion region in the DW and PW images using seed points input by a user in the images generated using PW1, DWI and/or the other complimentary scans such as FLAIR and MRA. The lesion segmentation in the DW and PW images may aid in determining a mismatch between DW and PW lesion volumes. The mismatch between the DW and PW lesion volumes, in turn, may be used to determine PDM that may be indicative of salvageable brain tissue that is at risk of infarction following the ischemic stroke.

[0052J Conventionally, during lesion segmentation, histogram distributions of ADC or DW image data corresponding to normal and infarcted tissues may be used for quantifying the mismatch between the DW and PW lesion volumes. These histograms, however, are known to overlap, thus resulting in over-estimation or under-estimation of the lesion volumes. Such inaccurate lesion volume estimations may negatively affect PDM computation, which in turn, may adversely affect a decision regarding administration of a thrombolytic agent.

[0053] Accordingly, embodiments of the present method describe a robust and reproducible method for lesion segmentation that allows for an accurate estimation of brain tissue characteristics following an ischemic stroke. The estimated brain tissue characteristics may be used, for example, to compute the PDM corresponding to the DW and PW images for assessing a need for administration of the thrombolytic agent. To that end, in one embodiment, a patient, such as the patient 112 of FIG. 1, is suitably positioned on an examination table associated with an MRI system, such as the MR1 system 100 of FIG. 1, for imaging. Particularly, the patient may be positioned such that a desired portion of brain, for example, a brain region of interest, is positioned within a field of view (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.

[0054J Further, at step 202, one or more PW and DW images corresponding to a VOI of a subject, for example the patient 112 of FIG. 1, may be generated using the acquired imaging data. In one embodiment, the DW images may be generated such that an intensity of each image element or voxel in the DW images may reflect an estimate of the 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 DW images may be indicative of changes to a pathological state of the brain tissue corresponding to the VOL By way of example, the DW images may capture early changes after an ischemic stroke as opposed to more traditional MRI measurements such as spin-lattice relaxation time (Tl) and spin-spin relaxation time (T2) rates. Further, DW 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 DW image reconstruction will appear to be substantially similar when measured along any axis.

[0055] Typically, DWI may be performed by applying diffusion sensitivity, for example based on a b-factor value, along three orthogonal axes (for example x, y and z axes), and then combining corresponding scans to generate DWI trace maps for use in clinical diagnosis. Sensitivity of the DW images to ischemic regions may be varied by varying a value of diffusion weighting, for example, the b-factor value corresponding to the DW images. Typically, for clinical diagnosis, b-factor values may correspond to about 1000 s/mm2. However, in accordance with aspects of the present method, the DW images having higher or lower b-factor values may also be used to improve conspicuity of ischemic lesions.

[0056| Further, the PW images may be obtained by performing computations on a time series of, for example, raw images that are reconstructed subsequent to administration of a contrast agent. Alternatively, PW images may be obtained using endogenous contrast, such as that corresponding to blood, using arterial spin labeling (ASL) based methods. The PW images, thus obtained, may provide information regarding a variety of hemodynamic parameters corresponding to the VOL The hemodynamic parameters, for example, may include cerebral blood volume (CBV), cerebral blood flow (CBF), mean transit time (MTT), arterial transit time (ATT), and time to peak (TTP). These hemodynamic parameters may be used to assess a rate of perfusion in the brain tissue, which in turn, may be used to determine lesion characteristics.

[0057] In accordance with exemplary aspects of the present disclosure, the PW images, in conjunction with the DW images may be used to determine an approximate location and type of the lesion in the brain tissue of the patient. To that end, in certain embodiments, the DW and PW images may be preprocessed for correcting MRI image non-uniformity artifacts. The MRI non-uniformity artifacts in the DW and PW images may be caused due to dielectric effects, RF coil transmit and receive sensitivity, and/or susceptibility to magnetic materials. In one embodiment, the MRI inhomogeneity artifacts may be corrected, for example, using a phased array uniformity enhancement (PURE) method.

[0058] Further, at step 204, one or more seed points may be received from a user. As previously noted, the seed points may be determined or received on images generated using MRI scans corresponding to a determine stroke evaluation protocol, for example, including DWI, PWI, FLAIR and/or MRA. These seed points may be propagated to the DW and/or the PW images to identify a lesion in the DW and PW images. Conventional MRI systems typically employ automated methods for identifying and delineating the lesion in MRI images. Specifically, the automated methods may perform lesion segmentation by identifying regions that satisfy specific homogeneity criteria. However, such automated methods may not be optimal for lesion segmentation in images corresponding to a brain region.

[0059] Brain tissue is typically anisotropic and may exhibit variance in structure and function. Accordingly, an infarcted brain tissue may fail to satisfy the homogeneity criteria employed by the automated methods. Additionally, conventional DW and PW image reconstruction may result in artifacts that mimic structural and functional parameters corresponding to the lesion during an automated evaluation. Automated lesion segmentation methods, thus, are often unable to differentiate between these artifacts and the actual lesion. However, a subjective evaluation of a location and extent of lesion in the brain tissue provided by the user such as an experienced radiologist may allow for enhanced lesion segmentation. To that end, in one embodiment, the user may input one or more seed points to identify one or more regions corresponding to the lesion in the DW and PW images.

[0060] FIG. 3 illustrates a graphical representation 300 depicting exemplary seed points input by a user for guiding lesion segmentation in DW images as indicated by step 204 of FIG. 2. In FIG. 3, reference numeral 302 is representative of a diagrammatic representation of the seed point in the form of a line. In one example, this example of the seed point may be representative of a line marked on a central slice (CSL) of a DW image corresponding to an infarct in the brain tissue of a patient. Further, reference numeral 304 is representative of a diagrammatic representation of the seed point in the form of a point marked on the central slice (CSP). Additionally, reference numeral 306 is representative of a diagrammatic representation of the seed point in the form of a point marked on an extremity slice (ESP) of the DW image corresponding to the infarct. Although, FIG. 3 illustrates seed points for DW lesion segmentation, in certain embodiments, similar seed points may be received for providing subjective guidance for PW lesion segmentation as well. In certain embodiments, the user may improve specificity of the seed point corresponding to a probable lesion based on synchronized data from other MRI scans such as FLAIR, MRA, and/or DWI performed at low b-factor values (for example, b-factor=0 s/mm2) and high b-factor values (for example, b-factor>1000 s/mm2).

[0061] With returning reference to FIG. 2, lesion segmentation may proceed at sites in the DW and PW images using the seed points received at step 204. To that end, at step 206, one or more parametric maps corresponding to the DW and PW images may be generated. The parametric maps may be used to assess a change in functional, vascular, spectroscopic and/or structural parameters corresponding to the brain owing to the ischemic lesion. Accordingly, in one embodiment, the parametric maps generated using the DW images may include at least ADC maps.

[0062] Generally, the DW images are sensitive to MRI relaxation mechanisms and their differential rates. Accordingly, in certain embodiments, quantitative ADC maps may be generated to discriminate between diffusion and relaxation effects on image contrast. Particularly, the ADC maps may be generated with ADC as a sole source of contrast. These ADC maps may be constructed by collecting images with at least two different b-factor values. In one embodiment, the b-factor may correspond to a factor of DW sequences and may provide an indication of an influence of gradients on the DW images. In another embodiment, the b-factor may correspond to a change in time spacing between lobes of diffusion gradients.

[0063J In certain embodiments, the ADC maps generated based on at least two b- factor values may then be used for further assessment of medically relevant parameters, such as PDM, Tl, and/or T2 relaxation rates. The medically relevant parameters may be used to differentiate between acute, sub-acute, and chronic ischemic lesions for administering appropriate treatment to the patient. In one embodiment, for example, hypointense signals corresponding to the ADC maps and hyperintense signals corresponding to the DW images may be indicative of an acute lesion in the brain tissue.

[0064] Similarly, parametric maps generated from the PW images (PW parametric maps) may be used to assess a change in functional, vascular and/or structural parameters corresponding to the brain owing to the ischemic lesion. To that end, the PW parametric maps may include a time-to-maximum of a tissue residue function (Tmax) map, a CBF map, a CBV map, an MTT map, a TTP map, a peak concentration map, a processed peak subtraction map, MRA processed data, and/or an ATT map. The change in the functional, vascular and/or structural parameters ascertained through the PW parametric maps may provide indications regarding a nature and extent of the ischemic lesion.

|0065] Additionally, at step 208, one or more masks corresponding to one or more regions of interest (ROI) in the brain tissue may be generated. In one embodiment, the masks may correspond to different ROI of the brain. By way of example, the masks may include a cerebrum mask, a cerebellum mask, and/or a brain ventricle mask. In certain embodiments, the masks may be generated in both a native space and a symmetry space for use in PW lesion segmentation. As used herein, the term "native space" may be used to refer to an original coordinate system of the MRI scanner 102 that was employed to acquire the data used in construction of the PW image. Additionally, the term "symmetry space" may be used to refer to a space in which PW images have symmetry about the mid-sagittal plane corresponding to the MRI scanner 102. In accordance with aspects of the present disclosure, the generated masks may be used to divide the DW images and the PW images into sub-images that correspond to different regions of the brain such as the cerebrum, the cerebellum, and brain ventricle for performing focused lesion segmentation in each region. Thus, use of the masks may allow removal of regions where probability of occurrence of a lesion is negligible, such as the brain ventricles, from the probable lesion region. Additionally, use of the masks aids in learning one or more characteristics corresponding to false-positive lesion regions.

[0066] Subsequently, at step 210, lesion segmentation may be performed using the DW images by applying an adaptive and iterative multilevel thresholding method to the different regions of the DW images. More specifically, the lesion segmentation may be performed using different parameter settings corresponding to the different regions of the brain, such as the cerebrum or the cerebellum, identified at step 208. In one example, the different parameter settings, for example, may include different voxel intensity levels. The lesion segmentation may be further guided by values in the ADC maps, the PW parametric maps, the masks, the seed points, and/or reference data. In particular, the ADC maps, the masks, the seed points, and/or the reference data may allow removal of any false positive regions, thus aiding in determining the lesion characteristics with greater accuracy. An exemplary DW lesion segmentation method employing adaptive and iterative multilevel thresholding guided by feedback from the ADC maps, the masks, the seed points, and/or the reference data will be described in greater detail with reference to FIG. 4.

(0067J Further, at step 212, lesion segmentation may be performed using the PW images based on the lesion segmentation in the DW images, the seed points, contralateral analysis based on the PW parametric maps, and/or the reference data. To that end, in one embodiment, native space PW image data may be transformed into symmetry space PW image data. Similar to removal of false positive regions in DW images at step 210, one or more false positive regions corresponding to the probable lesion region in the PW images may also be identified based on the one or more masks and/or the PW parametric maps. Such false positive information may be removed during the PW lesion segmentation to aid in more efficient quantification of the lesion characteristics. An exemplary PW lesion segmentation method for improved lesion segmentation, in accordance with aspects of the present disclosure, will be described in greater detail with reference to FIG. 5.

[0068J Subsequently, at step 214, one or more characteristics corresponding to regions of the brain affected by an ischemic stroke may be determined based on the lesion segmentation in the DW images and the PW images. The one or more characteristics, for example, may include a voxel-by-voxel mismatch between DW and PW images and/or a mismatch between the lesion volumes determined from the DW and PW images. Accordingly, in one embodiment, the DW lesion and the PW lesion may be combined or co-registered on a common coordinate system. Additionally, the combined lesion may be rendered on a display using a volume rendering algorithm to allow for user evaluation. In certain embodiments, the PW lesion, the DW lesion, and/or the combined lesion may be used to determine a voxel-by-voxel mismatch between the DW and PW images. Alternatively, a mismatch between lesion volumes may be computed based on information derived from the DW and PW segmentation and/or co-registration. The mismatch between the lesion volumes, in turn, may aid in identifying the brain tissue that may be salvaged with reperfusion therapies following an ischemic stroke. Particularly, embodiments of the present method provide an efficient method for the DW and PW lesion segmentation that may aid in accurate and swift quantification of the mismatch volume for timely treatment decision in case of an acute ischemic stroke.

[0069J FIG- 4 illustrates a flowchart 400 depicting an exemplary method for performing lesion segmentation in DW images described with reference to step 210 of FIG 2. To that end, at step 402, ADC maps may be generated by preprocessing the DW images. To that end, in one embodiment, the DW images may be acquired at b-factor values of about 0 s/mm2 (bO) and about 1000 s/mm2 (bl) along different directions (x, y and z). The DW images acquired at bl may be registered to the DW images acquired at bO using rigid registration, affine registration and/or non-rigid registration (NRR).

[0070] Typically, a distortion generated due to diffusion gradients and echo-planar imaging (EPI) will be different along x, y and z diffusion encoding axes. Accordingly, rigid, affine and/or NRR techniques may be used to combine the DW images obtained along different diffusion encoding axes to form DW trace images, from which the ADC maps may be generated. Additionally, the ADC maps may be preprocessed using gradient anisotropic diffusion filtering. The gradient anisotropic filtering may rectify variations in the ADC values due to noise in the DW images. The gradient anisotropic filtering, thus, may improve accuracy of structural and architectural characterization of the brain tissue in the DW images by reducing noise, while preserving image detail.

[0071] Further, at step 404, a histogram matching filter may be applied to the DW images using at least one reference image corresponding to a volume of interest in the brain tissue. To that end, histograms depicting a number of voxels in a DW image (DW histogram) and in the reference image (reference histogram) at different intensity levels corresponding to that DW image and the reference image may be obtained. Application of the histogram matching filter to the DW images in view of the reference histogram may distribute voxel intensities more efficiently across the resulting DW histogram. Particularly, the histogram matching filter may distribute the voxel intensities in the DW histogram such that the VOl, for example the probable lesion region, in the DW image gains more contrast, thus providing a greater level of useful detail. In an embodiment where seed point inputs are available, the DW image may be preprocessed only using the histogram matching filter. However, if the seed points are not available, the DW images may undergo additional spatial filtering in addition to applying the histogram matching filter for smoothening the DW image for further segmentation.

[0072] Additionally, at step 406, the DW images may be divided into one or more sub-images corresponding to regions such as the cerebrum, the cerebellum, and/or the brain ventricle of the subject using one or more region of interest masks, such as the masks generated at step 208 of FIG. 2. In certain embodiments, an atlas-based approach may be employed to divide the DW images into one or more sub-images corresponding to regions such as the cerebrum, the cerebellum, and/or the brain ventricle. Dividing the images corresponding to the brain into sub-images corresponding to different brain regions may allow for more efficient lesion segmentation. Such division may also eliminate regions such as brain ventricles from the images where false positive data points may occur frequently.

(0073J Subsequently, at step 408, lesion segmentation may be performed in the DW images based on a plurality of intensities of voxels corresponding to the cerebrum and/or the cerebellum using the multilevel thresholding method. To that end, different parameter settings, for example, determined ranges of intensities of voxels corresponding to the cerebrum and/or the cerebellum may be identified. The lesion segmentation may then proceed independently in the cerebrum and the cerebellum.

[0074] In accordance with aspects of the present disclosure, sub-images corresponding to the cerebrum and the cerebellum may be further segmented based on the identified ranges of intensities of corresponding voxels. In one example, the sub-images may be segmented into four segments based on the identified ranges of voxel intensities. As previously noted, the sub-images corresponding to the cerebrum and/or the cerebellum may be segmented into a first segment including voxels having highest range of intensities. Further segments of the cerebrum and/or the cerebellum may include a background region and other regions having lower voxel intensities.

[0075] As regions corresponding to the lesion or an infarct in the DW images generally exhibit lower attenuation, the probable lesion region may include voxels having high intensities. Accordingly, the first segment may be identified as a region corresponding to the lesion. By way of example, in one embodiment, regions with values less than a threshold value of about 0.65xl(P millimeter2/second (mm2/s) for the cerebrum in a corresponding ADC map may be selected as a probable DW lesion region. Similarly, regions with values less than a threshold value of about 0.6><10~3 mm2/s for the cerebellum in a corresponding ADC map may be selected as the probable DW lesion region. As previously noted, the threshold value may be adaptively varied using multi-scale ADC values during the iterative multilevel thresholding to mitigate any anisotropy identified in a DW1 dataset prior to the lesion segmentation.

[0076| Further, at step 410, a first diffusion weighted lesion mask may be generated using iterative multilevel thresholding to delineate the lesion from the DW images. In one embodiment, the first diffusion weighted lesion mask may correspond to the probable lesion region identified using the multilevel thresholding method, as described with reference to step 408. In certain embodiments, the first diffusion weighted lesion mask may be refined by eliminating one or more regions from the first diffusion weighted lesion mask that do not correspond to the seed points received from the user.

[0077] Further, the iterative multilevel thresholding method may be applied to regions of the cerebrum and/or the cerebellum excluding the first diffusion weighted lesion mask to identify a second diffusion weighted lesion mask. The first diffusion weighted lesion mask may then be combined with the second diffusion weighted lesion mask to form a combined diffusion weighted lesion mask. It may be noted that using the first diffusion weighted lesion mask based only on voxel intensities in the DW images may not provide optimal lesion segmentation as system level imperfections may confound the DWI intensity measurements.

[0078] To that end, at step 412, the first diffusion weighted lesion mask may be modified. In certain embodiments, one or more voxels proximate a region corresponding to the first diffusion weighted lesion mask may be identified based on corresponding ADC values. Particularly, voxels whose ADC values are indicative of an abnormality such as the lesion or infarct in the brain tissue may be identified, for example, based on historical information correlating the ADC values with GT values corresponding to the lesion. Accordingly, in one embodiment, voxels proximate the first diffusion weighted lesion mask that have the highest range of intensities in a DW image, but have lower values in a corresponding ADC map may be identified as a region corresponding to the lesion. By way of example, voxels proximate the lesion and having an ADC threshold of less than about 0.59><10~J mm2/s for the cerebrum and less than about 0.51xl0~J mm2/s for the cerebellum be identified as probable DW lesion regions.

[0079] Subsequently, at step 414, the voxels identified at step 412 may be added to the first diffusion weighted lesion mask to generate a combined diffusion weighted lesion mask for lesion segmentation. The combined diffusion weighted lesion mask may be used to further delineate a boundary of the lesion in the cerebrum and/or the cerebellum using the multilevel thresholding method.

|0080] Further, at step 416, the one or more DW images may be postprocessed to remove any remaining false positive data from the probable lesion region. To that end, in one embodiment, the cerebrum and the cerebellum may be combined into a single region. Additionally, regions corresponding to the lesion in the cerebrum and/or the cerebellum may be combined and identified as a probable region corresponding to the lesion in the DW image. The probable lesion region may be further refined using the ADC maps and/or the reference data.

[00811 In one embodiment, multi-scale ADC thresholds may be used to identify the probable lesion region. Moreover, these regions may be used to automatically generate seed points corresponding to one or more slices of the DW image. The automatically generated seed points for each DW image slice may be iteratively compared with the seed points received from the user. Further, regions corresponding to the automatically generated seed points that do not have a corresponding seed point received from the user may be removed from the probable lesion region and may be relabeled as background regions. However, regions corresponding to the automatically generated seed points that have a corresponding seed point received from the user may be confirmed as corresponding to the lesion in the DW image. The DW lesion segmentation, thus performed, may be used to aid in segmenting the PW lesion.

[0082] FIG. 5 illustrates a flowchart 500 depicting an exemplary method for performing lesion segmentation in PW images described with reference to step 212 of FIG 2. To that end, at step 502, the PW images may be preprocessed to rectify non-uniformity artifacts corresponding to magnetic resonance image intensity. Typically, the non-uniformity artifacts may be caused due to dielectric effects, RF coil transmit and receive sensitivity, and/or susceptibility to magnetic materials. Accordingly, as previously noted, in one embodiment, the non-uniformity artifacts may be corrected, for example, using the PURE method.

[0083] Further, at step 504, one or more regional masks corresponding to one or more regions of interest in the PW image may be generated in a native space and a symmetry space. As previously noted, the native space may correspond to the original coordinate system used to acquire data from an MRI scanner, whereas the symmetry space may correspond to a space in which PW images have symmetry about the mid-sagittal plane corresponding to the MRI scanner. Further, the regional masks may correspond to regions such as the cerebrum, cerebellum, and ventricles in the brain.

[0084] Additionally, at step 506, a native space PW parametric map corresponding to the one or more PW images may be generated. In one embodiment, the native space PW parametric map, for example, may include a Tmax map, a CBV map, a CBF map, a TTP map, an ATT map and/or and MTT map. In a presently contemplated embodiment, a

native space Tmax map may be generated for use in PW lesion segmentation. Particularly, the native space Tmax map may be used to define a threshold that may be used to determine PDM for stroke evaluation.

[0085] Moreover, at step 508, symmetry correction may be performed by transforming the PW parametric map from the native space to the symmetry space. In certain scenarios, a region of interest, for example, the head of a patient may be tilted during PWJ data acquisition. In such scenarios, contralateral analysis that may be generally used in lesion segmentation may not be possible due to an inability to perform a voxel-by voxel comparison. Accordingly, the native space PW parametric map may be transformed into the symmetry space PW parametric map.

[0086] Subsequently, at step 510, one or more regions may be removed from the symmetry space PW parametric map using the one or more symmetry space regional masks generated at step 504. The one or more regions may include structures that may be eliminated from further processing such as brain ventricles. As previously noted, a probability of occurrence of a lesion in the brain ventricles is negligible, and therefore, the brain ventricle region may be eliminated from further processing.

|0087] Moreover, at step 512, a deficit region in the symmetry space PW parametric map may be identified based on the lesion segmentation in the DW images, PW map characteristics, for example, including deterministic and probabilistic data statistics from one or more of parametric maps, ventricle masks, and/or the one or more seed points received from the user. The deficit region may correspond to hypoperfused areas of brain tissue. These hypoperfused areas, in turn, may correspond to probable penumbra regions that may be reperfused and salvaged through thrombolytic administration.

[0088] Accordingly, in one embodiment, regions of the symmetry space PW parametric map that exhibit values greater than a determined PW parametric threshold may be identified as the deficit region. In an embodiment that employs Tmax map, a Tmax threshold may be determined to be about 6.5 seconds. Accordingly, regions in the Tmax map that yield Tmax values greater than about 6.5 seconds may be indicative of regions having no or delayed blood flow, and thus, may be identified as potential penumbra regions. The Tmax threshold value may also be dependent on a time-resolution of a corresponding PWI scan. Accordingly, the Tmax threshold value may be adapted based on a step size of a PWI data acquisition. By way of example, in one embodiment, for PWI temporal resolution of about 1 second or less, the Tmax threshold value may be adapted to be about 5.5 seconds, where the threshold value may be indicative of regions with no-blood flow or delayed blood flow.

[0089] Generally, an ischemic stroke affects functioning of regions in the brain that are located on an ipsilateral side of the penumbra regions. Accordingly, at step 514, a contralateral region corresponding to the deficit region (contralateral deficit region) may be identified for improving the accuracy in lesion assessment. As used herein, the term "contralateral deficit region" is used to refer to a region located on a side that is opposite to the identified deficit region. For example, if the deficit region corresponds to the left hemisphere of the brain, a comparable region located substantially on the opposite side, that is, in the right hemisphere of the brain may be identified as the contralateral deficit region. The contralateral deficit region may be used to perform a comparative analysis between opposing regions, such as the left and right brain hemispheres for assessing impact of the ischemic stroke on the functional aspects of the brain tissue.

(0090] Further, at step 516, a metric may be determined using PW parametric map values corresponding to one or more voxels in the deficit region and corresponding voxels in the contralateral region. The deficit region may correspond to the ipsilateral hemisphere of the brain, whereas the contralateral region may correspond to the contralateral hemisphere of the brain.

[0091] Moreover, at step 518, it may be ascertained if the metric determined at step 516 satisfies a determined PW deficit threshold. In an embodiment employing a Tmax map, for example, the metric may correspond to a difference in the Tmax values corresponding to one or more voxels in the deficit region and corresponding voxels in the contralateral region. Further, the PW deficit threshold, for example, may have a value of about 2 seconds. Also, the PW deficit threshold may be satisfied if the difference in the Tmax values between voxels in the ipsilateral and contralateral hemispheres exceeds 2 seconds. In another embodiment employing an MTT map, the metric may correspond to a ratio of the MTT values between voxels corresponding to the ipsilateral and contralateral hemispheres. In the embodiment employing the MTT map, the PW deficit threshold may have a value of about 1.8 seconds. In this example, the PW deficit threshold may be satisfied if the ratio of the MTT values between the voxels corresponding to the ipsilateral and contralateral hemispheres exceeds 1.8 seconds.

[0092] Accordingly, at step 518, if it is determined that the metric satisfies the PW deficit threshold, then the one or more voxels may be retained in the deficit region, as depicted by step 520. However, if at step 518, it is ascertained that the determined metric fails to satisfy the PW deficit threshold, then the one or more voxels may be removed from the identified deficit region to generate a symmetry space lesion mask, as depicted by step 522. In one embodiment, for example, if the difference in the Tmax values between the ipsilateral and contralateral hemispheres is ascertained to be greater than 2 seconds, the voxels in the deficit region may be considered to be corresponding to the probable penumbra region. Accordingly, the voxels corresponding to the probable penumbra region may be retained in the deficit region. The control then passes to step 524. However, if the determined difference is ascertained to be less than 2 seconds, it may be assumed that the voxels are representative of false positive data and may be removed from the deficit region.

[0093] Subsequently, at step 524, the symmetry space PW parametric map may be transformed back into the native space PW parametric map to revert the data to the original coordinate system of the MRI system. Further, at step 526, any remaining false positive data may be removed from the deficit region in the native space PW parametric map. To that end, the symmetry space lesion mask may be registered to the native space and to generate a native space lesion mask. The false positive data may then be removed from the deficit region native space lesion mask and/or the one or more PW parametric maps to generate a perfusion weighted lesion mask. In certain embodiments, for example, the false positive data may be removed based on reference data and/or values of voxels in a CBV map corresponding to the PW image data. By way of example, in one embodiment, voxels having a Tmax value of greater than 6 seconds and a regional CBV of greater than 12 milliliters/100 grams of the brain tissue may be removed as false positive data. Use of the reference data and the PW parametric maps, thus, may allow for generation of the perfusion weighted lesion mask that aids in further removal of non- penumbra regions from the probable lesion region. The perfusion weighted lesion mask may then be used to perform lesion segmentation in the PW images more efficiently.

[0094] As previously noted, in certain embodiments, the DW and PW lesions may be combined or co-registered for further evaluation. Additionally, the DW and PW lesions may be filtered to provide smooth lesion contours within a slice or an entire three-dimensional (3D) volume. To that end, in one embodiment, the lesion contours may be smoothened using an active contour based method or using energy functional parameters that penalizes roughness, for example, using double derivative norm or surface fitting. Alternatively, the lesion contours may be smoothened using a mathematical morphological CLOSE operation that employs a morphological structuring element with a particular dilation radius. In one example, the morphological CLOSE operation may employ a dilation radius of about two voxels and/or a disk-shaped structuring element. In certain embodiments, energy functional parameters along with the dilation radius and a shape of the structuring element may be adapted based on user input to obtain smoothened lesion contours that aid in subsequent lesion characteristic assessment.

[0095) Subsequently, information derived from the lesion with smoothened contours, and/or the combined lesion identified at step 414 of FIG. 4 may be used to determine a volume and/or a voxel-by-voxel mismatch between DW and PW images. Alternatively, the PWI, the DWI, and/or the combined lesion information may be used to determine other medically relevant parameters such as time to stroke, predict progression of the lesion and/or recovery status of the patient. Embodiments of the present disclosure, thus, allow for robust and reproducible DW and PW lesion segmentation that provides accurate information for an informed assessment of the ischemic stroke.

[0096] FIGs. 6-9 illustrate graphical representations that demonstrate the robustness and reproducibility of the exemplary method described with reference to FIGs. 2-5. The reproducibility analysis may be conducted on a group of stroke patients. By way of example, in one embodiment, the patients may be imaged using DW and PW imaging before treatment with a thrombolytic agent. Further, a skilled and/or experienced medical practitioner may generate GT values for reproducibility analysis. More specifically, the medical practitioner may generate the GT values by manually delineating a stroke infarct imaged in the DW images using ADC maps to avoid false-positive data, while also delineating an ischemic hypoperfused area on a Tmax map. The results of present semi-automated DW and PW lesion segmentation and PDM assessment with three different user inputs may then be compared with the GT values for the reproducibility analysis.

[0097] FIG. 6 illustrates a graphical representation 600 depicting reproducibility of the semi-automated lesion segmentation in DW images performed using the method described with reference to FIG. 2 and FIG. 4. Particularly, FIG. 6 illustrates the graphical representation 600 corresponding to an exemplary Box-and-Whisker plot that depicts excellent reproducibility of the DW lesion segmentation method using three different user inputs, for example, CSL 302, CSP 304, and ESP 306 of FIG. 3. The Box-and-Whisker plot may be generated based on a statistical analysis of reproducibility and mismatch agreement. Additionally, repeated measures Analysis of Variance (ANOVA) may be performed to analyze and illustrate a difference between segmentation results corresponding to the user inputs, CSL 302, CSP 304, and ESP 306.

[0098] As illustrated by the graphical representation 600, inputs corresponding to the GT, CSL 302, CSP 304, and ESP 306 result in substantially similar DW lesion volumes. Similarity in the DW lesion volumes computed for the different user inputs indicate that embodiments of the present method described with reference to FIG. 2 are robust to any differences in user inputs. Additionally, similarity in the DW lesion volumes computed using the present method and the lesion volume computed using the GT validates accuracy of the present method.

[0099] Further, FIG. 7 illustrates a graphical representation 700 corresponding to a Box-and-Whisker plot that depicts excellent reproducibility of exemplary PW lesion segmentation performed using the method described with reference to FIG. 2 and FIG. 5. As previously described with reference to FIG. 6, it may be noted that FIG. 7 also does not depict any statistically significant differences between lesion volumes obtained using different user inputs, and/or between GT lesion volumes and lesion volumes obtained using embodiments of the present method.

[0100] Further, FIG. 8 illustrates a diagrammatic representation 800 of a comparison between exemplary lesion segmentations in DW images 804 and 802 performed using the present method described with reference to FIG. 4 with corresponding GT lesion segmentations. As previously noted, a skilled and/or experienced medical practitioner may generate the GT lesion segmentations by manually delineating the lesion in the DW and PW images. Moreover, FIG. 8 illustrates exemplary DW lesion segmentation 808 obtained using an embodiment of the methods described with reference to FIG. 4 and a corresponding GT lesion marking 806 identified by the medical practitioner.

[0101] As depicted in FIG. 8, the DW lesion marking 808 identified using the method of FIG. 4 is substantially similar to the GT lesion marking 806, thus, indicating a substantial agreement between the present method and the GT lesion segmentation. The agreement between the DW lesion marking 808 and the GT lesion marking 806, in turn, validates the robustness of the present method.

[0102] Similarly, FIG. 9 illustrates a diagrammatic representation 900 of a comparison between exemplary lesion segmentations in PW images 904 and 902 performed using the method described with reference to FIG. 5 with corresponding GT lesion segmentations. Also, FIG. 9 illustrates exemplary PW lesion marking 908 obtained using an embodiment of the methods described with reference to FIG. 5 and a corresponding GT lesion marking 906 identified by the medical practitioner.

|0103] Moreover, as depicted in FIG. 9, the PW lesion marking 908 identified using the method of FIG. 5 is substantially similar to the GT lesion marking 906, thus, indicating a substantial agreement between the present method and the GT lesion segmentation. The agreement between the PW lesion marking 908 and the GT lesion marking 906, in turn, further validates the robustness of the present method.

[0104] Embodiments of the present disclosure, thus, provide systems and methods for robust, reproducible, and semi-automated assessment of abnormalities in brain tissue using user-defined seed points following an ischemic stroke. More particularly, embodiments of the present disclosure employ iterative and adaptive multilevel thresholding, feedback from the user-defined seed points, reference data, ADC maps, and/or PW parametric maps for the DW and PW lesion segmentations. Use of the iterative and adaptive multilevel thresholding and feedback from the user-defined seed points, the reference data, the ADC maps, and/or the PW parametric maps may allow for greater accuracy and consistency in determining lesion characteristics such as PDM. Additionally, embodiments of the present disclosure provide DW and PW lesion segmentation methods that are robust against variations in user inputs, thus allowing for accurate and swift quantification of the lesion characteristics. Accurate and swift quantification of lesion characteristics, in turn, may aid in an informed and timely decision-making regarding appropriate stroke treatment for the patient.

|0105| 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 of FIG. 1, may be implemented by suitable code on a processor-based system. To that end, the processor-based system, for example, may include 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.

[0106] Additionally, the functions may be implemented in a variety of programming languages, including but not limited to Ruby, Hypertext Preprocessor (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.

[0107] 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 various embodiments, for example, to construct additional assemblies and MRI methods.

[0108] 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: generating one or more perfusion weighted magnetic resonance images and one or more diffusion weighted magnetic resonance images corresponding to a volume of interest of a subject; receiving one or more seed points from a user generating one or more diffusion weighted parametric maps corresponding to the one or more diffusion weighted images and one or more perfusion weighted parametric maps corresponding to the one or more perfusion weighted images, wherein the one or more diffusion weighted parametric maps comprise one or more apparent diffusion coefficient maps; generating one or more masks corresponding to one or more regions of interest corresponding to the volume of interest; performing lesion segmentation in the one or more diffusion weighted images based on iterative and adaptive multilevel thresholding as applied to different regions of the one or more diffusion weighted images using feedback from the one or more apparent diffusion coefficient maps, the one or more perfusion weighted parametric maps, one or more of the masks, the seed points, reference data, or combinations thereof; performing lesion segmentation in the one or more perfusion weighted images based on one or more of the lesion segmentation in the one or more diffusion weighted images, the one or more seed points, contralateral analysis based on the one or more diffusion weighted parametric maps, the one or more perfusion weighted parametric maps, the reference data, or combinations thereof; and determining one or more characteristics corresponding to a region in the volume of interest affected by an ischemic stroke based on the lesion segmentation in the one or more diffusion weighted images and the one or more perfusion weighted images.

2. The method of claim 1, wherein the volume of interest corresponds to a brain region of the subject.

3. The method of claim 1, further comprising: preprocessing the one or more diffusion weighted images by combining the one or more diffusion weighted images generated at a plurality of b-factor values and diffusion encoding directions using non-rigid registration to generate the one or more apparent diffusion coefficient maps; and preprocessing the one or more diffusion weighted images to rectify non-uniformity artifacts corresponding to magnetic resonance image intensity.

4. The method of claim 1, further comprising processing the one or more diffusion weighted images using a histogram matching filter using at least one reference image corresponding to the volume of interest.

5. The method of claim 1, further comprising: generating one or more magnetic resonance images corresponding to the volume of interest using diffusion weighted imaging at a plurality of b-factor values, diffusion weighted imaging with fluid attenuation at the plurality of b-factor values, magnetic resonance angiography (MRA), fluid-attenuated inversion recovery (FLAIR), susceptibility weighted imaging (SWI), magnetic resonance spectroscopic imaging (MRSI), chemical exchange saturation transfer (CEST), or combinations thereof; co-registering the one or more magnetic resonance images, the one or more diffusion weighted images and the one or more perfusion weighted images to a common coordinate system; receiving the one or more seed points corresponding to the one or more magnetic resonance images; and propagating the one or more seed points from a particular location in the one or more magnetic resonance images to a corresponding location in the one or more diffusion weighted images, the one or more perfusion weighted images, or a combination thereof.

6. The method of claim 1, wherein generating the one or more masks comprises generating a cerebrum mask, a cerebellum mask, a ventricle mask, or combinations thereof.

7. The method of claim 1, wherein performing lesion segmentation based on the iterative and adaptive multilevel thresholding comprises identifying a plurality of intensities of voxels corresponding to the cerebrum, the cerebellum, or a combination thereof.

8. The method of claim 1, wherein performing lesion segmentation based on the iterative and adaptive multilevel thresholding comprises: identifying a degree of anisotropy in a dataset corresponding to the one or more diffusion weighted images; and adapting one or more threshold parameters corresponding to the iterative and adaptive multilevel thresholding based on the identified degree of anisotropy to improve accuracy of the diffusion weighted lesion segmentation.

9. The method of claim 1, further comprising generating a first diffusion weighted lesion mask using the iterative and adaptive multilevel thresholding performed using the one or more seed points.

10. The method of claim 9, further comprising: modifying the first diffusion weighted lesion mask based on multi-scale apparent diffusion coefficient values corresponding to one or more voxels in the one or more diffusion weighted images, wherein the apparent diffusion coefficient values corresponding to the one or more voxels are indicative of an diffusion abnormality in the volume of interest, the one or more voxels are proximate to the first diffusion weighted lesion mask, or a combination thereof; and smoothening contours of the first diffusion weighted lesion mask based on user input.

11. The method of claim 9, further comprising removing false positive data from the first diffusion weighted lesion mask based on feedback from the one or more seed points received from the user, multi-scale apparent diffusion coefficient thresholds, reference data, or combinations thereof.

12. The method of claim 1, wherein generating the one or more perfusion weighted parametric maps comprises generating a time-to-maximum of a tissue residue function (Tmax) map, a cerebral blood flow map, a cerebral blood volume map, a mean transit time map, a time to peak map, an arterial transit time map, a peak concentration map, a processed peak subtraction map, processed MRA data, or combinations thereof.

13. The method of claim 1, wherein performing the lesion segmentation in the one or more perfusion weighted images comprises: generating one or more regional masks corresponding to one or more regions of interest in the one or more perfusion weighted images in a native space and a symmetry space; generating a native space perfusion weighted parametric map corresponding to the one or more perfusion weighted images, wherein the native space perfusion weighted parametric map corresponds to at least one of the one or more perfusion weighted parametric maps; performing symmetry correction by transforming the native space perfusion weighted parametric map from the native space into the symmetry space; removing one or more regions from the symmetry space perfusion weighted parametric map using the one or more regional masks corresponding to the symmetry space; identifying a deficit region in the symmetry space perfusion weighted parametric map based on the lesion segmentation in the one or more diffusion weighted images, the one or more seed points,

one or more characteristics corresponding to the perfusion weighted parametric map, or combinations thereof; removing one or more voxels corresponding to the deficit region to generate a symmetry space lesion mask based on a metric computed using values corresponding to the one or more voxels in the deficit region in the perfusion weighted parametric map and corresponding voxels in a contralateral deficit region; transforming the symmetry space perfusion weighted parametric map into the native space perfusion weighted parametric map; removing false positive data from the deficit region using a native space lesion mask generated from the symmetry space lesion mask, the one or more perfusion weighted parametric maps, or a combination thereof, to generate a perfusion weighted image mask; smoothening contours of the perfusion weighted image mask based on user input; and performing the lesion segmentation in the one or more perfusion weighted images using the smoothened perfusion weighted image mask.

14. The method of claim 1, wherein determining the one or more characteristics corresponding to the volume of interest affected by an ischemic stroke comprises determining a voxel-by-voxel mismatch between regions corresponding to a lesion determined from the one or more perfusion weighted images and the one or more diffusion weighted images, calculating a mismatch between lesion volumes determined from the one or more perfusion weighted images and the one or more diffusion weighted images, or a combination thereof.

15. The method of claim 1, further comprising identifying a region of abnormality in the volume of interest based on a voxel-by-voxel mismatch between regions corresponding to a lesion determined from the one or more perfusion weighted images and the one or more diffusion weighted images, a mismatch between lesion volumes determined from the one or more perfusion weighted images and the one or more diffusion weighted images, or a combination thereof.

16. A magnetic resonance imaging system, comprising: a scanner configured to scan a volume of interest in a brain region of a subject to acquire imaging data; one or more input-output devices configured to receive one or more seed points from a user; and a processing subsystem operationally coupled to one or more of the scanner and the input-output devices, wherein the processing subsystem is configured to: generate one or more perfusion weighted images and one or more diffusion weighted images corresponding to a volume of interest of a subject; generate one or more diffusion weighted parametric maps corresponding to the one or more diffusion weighted images and one or more perfusion weighted parametric maps corresponding to the one or more perfusion weighted images, wherein the one or more diffusion weighted parametric maps comprise one or more apparent diffusion coefficient maps; generate one or more masks corresponding to one or more regions of interest corresponding to the volume of interest;

perform lesion segmentation in the one or more diffusion weighted images based on iterative and adaptive multilevel thresholding as applied to different regions of the one or more diffusion weighted images using feedback from the one or more apparent diffusion coefficient maps, one or more of the masks, the seed points, reference data, or combinations thereof; perform lesion segmentation in the one or more perfusion weighted images based on one or more of the lesion segmentation in the one or more diffusion weighted images, the one or more seed points, contralateral analysis based on the one or more diffusion weighted parametric maps, the one or more perfusion weighted parametric maps, the reference data, or combinations thereof; and determine one or more characteristics corresponding to the brain tissue affected by an ischemic stroke based on the lesion segmentation in the one or more diffusion weighted images and the one or more perfusion weighted images.