WO2025106981A1 - Systèmes et procédés de pronostic localisé de lésions cérébrales - Google Patents

Systèmes et procédés de pronostic localisé de lésions cérébrales Download PDF

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WO2025106981A1
WO2025106981A1 PCT/US2024/056401 US2024056401W WO2025106981A1 WO 2025106981 A1 WO2025106981 A1 WO 2025106981A1 US 2024056401 W US2024056401 W US 2024056401W WO 2025106981 A1 WO2025106981 A1 WO 2025106981A1
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data
brain
lesion
patient
volumetric
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Aaron BOES
Hans Johnson
Joel BRUSS
Michal BRZUS
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University of Iowa Research Foundation UIRF
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Definitions

  • method may include obtaining imaging data of the brain of the patient from a scanner and classifying the data by acquisition plane and modality to select a data series of interest.
  • Method may also include processing the data series of interest to generate brain masks and corresponding volumetric data.
  • the method may furthermore include coregistering the brain masks and corresponding volumetric data to a common brain space and generating registered volumetric data.
  • the method may in addition include performing lesion segmentation to generate a lesion mask in the common brain space.
  • the method may moreover include applying the lesion mask data to a trained prognostic model, the prognostic model having a machine learning model configured to output a probability -based prognosis of the patient’ s brain lesion from lesion mask data, where the prognostic model has been trained with a plurality of lesion masks from a plurality of brain lesion subjects that are not the patient, and outcome data from each of the plurality of brain lesion subjects.
  • the method may also include outputting a probability-based report of prognosis of the patient.
  • Other embodiments of this aspect include corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the methods.
  • Implementations may include one or more of the following features.
  • the method where the classification step is performed via a classification algorithm.
  • the method where the processing step further may include a volumetric data conversion step, where the data series of interest is converted to volumetric data of interest.
  • the method where the processing step further may include a quality assurance step, where the volumetric data of interest may include a plurality of images and where the plurality of images is input into a quality assurance deep learning model, where the quality assurance deep learning model assigns a quality score to each of the plurality of images and where a set of images having a quality score above a predetermined threshold are selected and synthesized into one or more anatomical volumetric sequences.
  • the method where the processing step further may include performing brain masking and skull stripping on the anatomical volumetric sequences.
  • the method where the co-registration step further may include generating transform files from the volumetric sequence data and brain mask and resampling the volumetric data and transform files to generate co-registered volumetric data.
  • image intensity is normalized based on intensity of the image foreground, where image intensity is normalized without clipping.
  • the method where the patient prognosis is determined through comparison of lesion location to outcome data from other data, where the other data may include outcome data embedded in lesion-symptom maps.
  • the method where the step of outputting a probability-based report of prognosis further may include outputting therapeutic guidance, where the therapeutic guidance is based on the prognosis output.
  • Implementations of the described techniques may include hardware, a method or process, or a computer tangible medium.
  • a system for assessing prognosis of a brain lesion patient comprising one or more processors configured to: obtain imaging data of the brain of the patient from a scanner and classify the data by acquisition plane and modality to select a data series of interest; process the data series of interest to generate brain masks and corresponding volumetric data; co-register the brain masks and corresponding volumetric data to a common brain space and generate registered volumetric data; perform lesion segmentation to generate a lesion mask in the common brain space; and apply the lesion mask data to a trained prognostic model, the prognostic model comprising a machine learning model configured to output a probability-based prognosis of the patient’s brain lesion from lesion mask data, wherein the prognostic model has been trained with a plurality of lesion masks from a plurality of brain lesion subjects that are not the patient and outcome data from each of the plurality of brain lesion subjects; and output a probability-based report of prognosis of the patient.
  • a non-transitory computer-readable medium storing a set of instructions for assessing prognosis of a brain lesion patient, the set of instructions comprising one or more instructions that, when executed by one or more processors of a device, cause the device to: obtain imaging data of the brain of the patient from a scanner and classify the data by acquisition plane and modality to select a data series of interest; process the data series of interest to generate brain masks and corresponding volumetric data; co-register the brain masks and corresponding volumetric data to a common brain space and generate registered volumetric data; perform lesion segmentation to generate a lesion mask in the common brain space; and apply the lesion mask data to a trained prognostic model, the prognostic model comprising a machine learning model configured to output a probability -based prognosis of the patient’s brain lesion from lesion mask data, wherein the prognostic model has been trained with a plurality of lesion masks from a plurality of brain lesion subjects
  • FIG. 1A is a flow diagram of the automated prediction tool, according to one implementation.
  • FIG. IB is a flow diagram of the automated prediction tool, according to one implementation.
  • FIG. 2 is a Decision Tree for Acquisition Plane Classification used in the system, according to one implementation.
  • FIG. 3 is a Confusion Matrix for Modality Classification used in the system, according to one implementation.
  • FIG. 4 Shows Axial slice of a Trace-Weighted MRI in the left posterior quadrant, transformed to MNI space.
  • FIG. 5 is a flow chart showing one disclosed process, according to certain embodiments.
  • FIG. 6 is a flow chart showing one disclosed process, according to certain embodiments.
  • FIG. 7 is a graph plotting the relationship between the predicted and observed scores of a disclosed process, according to certain embodiments.
  • Ranges can be expressed herein as from “about” one particular value, and/or to “about” another particular value. When such a range is expressed, a further aspect includes from the one particular value and/or to the other particular value. Similarly, when values are expressed as approximations, by use of the antecedent “about,” it will be understood that the particular value forms a further aspect. It will be further understood that the endpoints of each of the ranges are significant both in relation to the other endpoint, and independently of the other endpoint. It is also understood that there arc a number of values disclosed herein, and that each value is also herein disclosed as “about” that particular value in addition to the value itself. For example, if the value “10” is disclosed, then “about 10” is also disclosed. It is also understood that each unit between two particular units is also disclosed. For example, if 10 and 15 are disclosed, then 11, 12, 13, and 14 are also disclosed.
  • brain lesion patient refers to an individual diagnosed with, or suspected to have, one or more abnormal areas of tissue within the brain that differ in structure or function from the surrounding healthy brain tissue.
  • diseases can arise from various pathological processes, including but not limited to: stroke or cerebrovascular disease; ischemic or hemorrhagic events leading to tissue death or damage in specific brain regions; tumors or neoplasms: abnormal growths, whether benign or malignant, that alter normal brain structure and function; neuroinflammatory disorders: conditions like multiple sclerosis (MS), where demyelination or inflammation causes lesions visible in neuroimaging; infections: such as brain abscesses or encephalitis, which can produce localized damage to brain tissue; congenital malformations or acquired abnormalities: including cysts or other structural anomalies visible as lesions on imaging; Traumatic Brain Injury (TBI): damage resulting from external physical force causing localized brain tissue disruption.
  • MS multiple sclerosis
  • a software module is implemented with a computer program product comprising a computer-readable medium containing computer program code, which can be executed by a computer processor for performing any or all of the steps, operations, or processes described.
  • a method for analyzing brain imaging data such as MRI, fMRI, CT, or PET scans, from a brain lesion patient and generating prognostic information includes obtaining imaging data of the brain of a patient from a scanner and classifying the data by acquisition plane and modality to select a data series of interest.
  • the method further involves processing the data series of interest to generate brain masks and corresponding volumetric data.
  • the brain masks and corresponding volumetric data are then coregistered to a common brain space, and registered volumetric data is generated.
  • lesion segmentation is performed to generate a lesion mask in the common brain space.
  • lesion segmentation is performed in native brain space.
  • the lesion mask data is applied to a trained prognostic model, which comprises a machine learning model configured to output a probability-based prognosis of the patient's brain lesion from lesion mask data.
  • the prognostic model has been trained with a plurality of lesion masks from a plurality of brain lesion subjects that are not the patient and behavioral outcome data from each of the plurality of brain lesion subjects.
  • the method further involves outputting a probability -based report of prognosis of the patient.
  • the co-registration step further comprises generating transform files from the volumetric sequence data and brain mask. In certain implementations, this step includes resampling the volumetric data and transform files to generate co-registered volumetric data.
  • Fig. 5 is a flowchart of an example process 500.
  • one or more process blocks of Fig. 5 may be performed by a system.
  • process 500 may include obtaining imaging data of the brain of the patient from a scanner and classifying the data by acquisition plane and modality to select a data series of interest (block 502).
  • the system may obtain imaging data of the brain of the patient from a scanner and classifying the data by acquisition plane and modality to select a data series of interest, as described above.
  • process 500 may include processing the data series of interest to generate brain masks and corresponding volumetric data (block 504).
  • the system may process the data series of interest to generate brain masks and corresponding volumetric data, as described above.
  • process 500 may include co-registering the brain masks and corresponding volumetric data to a common brain space (c.g., Montreal Neurological Institute (MNI)) and generating registered volumetric data (block 506).
  • MNI Montreal Neurological Institute
  • process 500 may include performing lesion segmentation to generate a lesion mask in the common brain space (block 508).
  • the system may perform lesion segmentation to generate a lesion mask in the common brain space, as described above.
  • lesion segmentation is performed in the patient’s native brain space.
  • image intensity is normalized based on intensity of the image foreground.
  • image intensity is normalized without clipping.
  • process 500 may include applying the lesion mask data to a trained prognostic model, the prognostic model having a machine learning model configured to output a probability-based prognosis of the patient’s brain lesion from lesion mask data, where the prognostic model has been trained with a plurality lesion masks from a plurality of brain lesion subjects that are not the patient and outcome data from each of the plurality of brain lesion subjects (block 510).
  • the system may apply the lesion mask data to a trained prognostic model, the prognostic model having a machine learning model configured to output a probability-based prognosis of the patient’s brain lesion from lesion mask data, where the prognostic model has been trained with a plurality lesion masks from a plurality of brain lesion subjects that are not the patient and outcome data from each of the plurality of brain lesion subjects, as described above.
  • process 500 may include outputting a probability-based report of prognosis of the patient (block 512).
  • the system may output a probability-based report of prognosis of the patient, as described above.
  • the method utilizes Lesion-symptom maps and lesion-network maps — techniques that provide crucial insights into brain-behavior relationships and the impact of focal brain damage.
  • Lesion symptom maps statistically illustrate the association between lesion locations and specific symptoms by analyzing normalized brain images from multiple patients, highlighting areas critical for particular functions.
  • Lesion network maps extend this concept by incorporating brain connectivity data, revealing how localized damage affects broader brain networks. These maps are created by identifying lesion locations associated with specific symptoms and using functional connectivity data to map the brain networks connected to these sites.
  • the regression model compares the new lesion mask to the predetermined symptom and network maps to predict personalized patient outcome.
  • Fig. 6 is a flowchart of another example process 600.
  • one or more process blocks of Fig. 6 may be performed by a system.
  • process 600 may include obtaining imaging data of the brain of the patient from a scanner and classifying the data by acquisition plane and modality to select a data series of interest (block 602).
  • the system may obtain imaging data of the brain of the patient from a scanner and classifying the data by acquisition plane and modality to select a data series of interest, as described above.
  • process 600 may include processing the data series of interest to generate brain masks and corresponding volumetric data (block 604).
  • the system may process the data series of interest to generate brain masks and corresponding volumetric data, as described above.
  • process 600 may include co-registering the brain masks and corresponding volumetric data to a brain space template and generating registered volumetric data (block 606).
  • the system may coregister the brain masks and corresponding volumetric data to a brain space template and generating registered volumetric data, as described above.
  • process 600 may include performing lesion segmentation to generate a lesion mask in the brain space template (block 608).
  • process 600 may include applying the lesion mask data to a trained prognostic model, the prognostic model having a machine learning model configured to output a probability-based prognosis of the patient’s brain lesion from lesion mask data, where the prognostic model has been trained with a plurality lesion masks from a plurality of brain lesion subjects that are not the patient and outcome data from each of the plurality of brain lesion subjects (block 610).
  • the system may apply the lesion mask data to a trained prognostic model, the prognostic model having a machine learning model configured to output a probability-based prognosis of the patient’s brain lesion from lesion mask data, where the prognostic model has been trained with a plurality lesion masks from a plurality of brain lesion subjects that are not the patient and outcome data from each of the plurality of brain lesion subjects, as described above.
  • process 600 may include outputting a probability-based report of prognosis of the patient (block 612).
  • the system may output a probability-based report of prognosis of the patient, as described above.
  • the method utilizes Lesion-symptom maps and lesion-network maps — techniques that provide crucial insights into brain-behavior relationships and the impact of focal brain damage.
  • Lesion symptom maps statistically illustrate the association between lesion locations and specific symptoms by analyzing normalized brain images from multiple patients, highlighting areas critical for particular functions.
  • Lesion network maps extend this concept by incorporating brain connectivity data, revealing how localized damage affects broader brain networks. These maps are created by identifying lesion locations associated with specific symptoms and using functional connectivity data to map the brain networks connected to these sites.
  • the regression model compares the new lesion mask to the predetermined symptom and network maps to predict personalized patient outcome.
  • the machine learning model may be a deep learning model that may analyze additional data.
  • deep learning model refers to an artificial neural network architecture designed with multiple layers of processing units (neurons) which has been trained to recognize patterns, features, or relationships within complex neuroimaging data. This type of model leverages vast amounts of imaging data, such as MRI, fMRI, CT, or PET scans, and in certain embodiments, the deep learning model analyzes the entire multi-channel image input. Deep learning models may be comprised of multiple layers — such as input, hidden, and output layers — allowing the model to transform raw imaging data (e.g., multi-channel image input) progressively into high-level features relevant to prognosis prediction tasks.
  • raw imaging data e.g., multi-channel image input
  • the additional data analyzed by the deep learning model can include a binary lesion segmentation mask, patient clinical magnetic resonance I MRI images (such as but not limited to T1 -weighted images), brain imaging data from non-lesion subjects, network atlas data, or other similar data.
  • the deep learning model generates a prognosis prediction without use of predetermined lesion symptom or network maps.
  • outcome data useful in training the prognostic model may include additional data such as subject demographics and other health information. These data can be entered into statistical models to improve performance of outcome predictions.
  • additional data is optional, and prognosis can be determined using exclusively the information contained from the brain imaging.
  • Processes 500 and 600 may include additional implementations, such as any single implementation or any combination of implementations described below and/or in connection with one or more other processes described elsewhere herein.
  • the processing step further comprises a volumetric data conversion step, where the data series of interest is converted to volumetric data of interest.
  • the processing step further comprises a quality assurance step, where the volumetric data of interest comprise a plurality of images and where the plurality of images is input into a quality assurance deep learning model.
  • the quality assurance deep learning model assigns a quality score to each of the plurality of images. Once assigned, a set of images having a quality score above a predetermined threshold are selected and synthesized into one or more anatomical volumetric sequences.
  • the processing step further includes performing brain masking and skull stripping of the anatomical volumetric sequences.
  • the patient prognosis reports are used to improve patient care in a number of ways. For instance, a stroke involving a high-risk brain region prompts more aggressive interventions in the acute stage of a stroke, such as administering thrombolytics or undergoing a clot extraction procedure. Prognostic information may also be used to guide personalized rehabilitation, such as highlighting the need for specific types of therapy or the need for additional referrals (e.g. a neuropsychological referral for an individual with a lesion location that places them at high risk for cognitive deficits. In certain embodiments, lesion location information aids in the identification of which patients may be most suitable for augmenting rehabilitation with other treatments like neuromodulation. In further embodiments, the patient prognosis report allows for more efficient and personalized inquiries on patient follow-up. For a specific example, if a patient is at high risk for post-stroke pain or depression the clinician could increase screening of those symptoms.
  • Figs. 5 and 6 show example blocks of processes 500 and 600, respectively, in some implementations, processes 500 or 600 may include additional blocks, fewer blocks, different blocks, or differently arranged blocks than those depicted in Figs. 5 and 6. Additionally, or alternatively, two or more of the blocks of processes 500 and/or 600 may be performed in parallel.
  • the disclosed methods are performed according to the following data flow:
  • the disclosed methods are performed according to the following data flow:
  • Output Predicted Outcome Scores, which could include any functional outcome related to lesion location, such as cognition, motor outcomes, disability, mood, personality, pain, or others.
  • an automated tool was developed for predicting stroke outcomes in acute ischemic stroke patients.
  • the tool used in this example has five main steps: Digital Imaging and Communications in Medicine (“DICOM”) Selection (with several substeps), Data Preprocessing, MNI152 Registration, Lesion Segmentation, and Lesion Segmentation Outcome Prediction.
  • DICOM Digital Imaging and Communications in Medicine
  • MNI152 Registration Registration
  • Lesion Segmentation Registration
  • Lesion Segmentation Registration
  • Lesion Segmentation Outcome Prediction The output of this tool is reliable prognosis for a particular patient based on their CT or MRI scan data.
  • the initial cohort had 138 subjects. Upon applying the inclusion criteria, 119 were confirmed to have acute ischemic strokes. Notably, the original retrospective dataset had been processed manually, with available sequences determining subject inclusion. Aligning with current standards for prospective studies, the pipeline necessitated the complete suite of specified modalities for analysis. Consequently, 5 subjects were excluded due to the absence of the requisite MRI sequence data, resulting in a refined final cohort of 114 patients.
  • This exclusion criterion is a deviation from past manual processes and is reflective of a commitment to the integrity of automated, protocol-driven research. All patients included were adults over 18 years of age, with no history of alcohol or substance abuse, psychiatric disorders, or neurological conditions unrelated to the ischemic lesion. They had undergone neuropsychological testing at the University of Iowa Benton Neuropsychology Clinic, following the protocols of the Benton Neuropsychology Laboratory. The cognitive function of participants was evaluated using a comprehensive battery of neuropsychological tests.
  • a training data set is required for the Example’s classification models. PyDICOM was used to extract the DICOM header information for every scan in the dataset. Certain specific fields are especially prone to human errors. These fields were identified and were used to infer the type of scan, such as CT scan or MRI scan, and its orientation. From this classification, eleven features were extracted. The features included a combination of raw DICOM fields and their derivatives. The raw DICOM fields included:
  • b-value information was extracted to identify TraceW Diffusion-Weighted Imaging (DWI) scans.3 This process is manufacturer dependent and uses field (0018, 9087) for standard, (0043, 1039) for GE, (2001, 1003) for Philips, (0019, 100C) for Siemens ((0019, 000C) for old Siemens data), or (0065, 1009) for UHI data. All distinct b- values were combined into an array and create two fields: maximal b-value and a number of distinctive b-values. Separately, for image acquisition plane classification, (0020,0037) ImageOrientation (Patient) field was converted from an array into 6 separate features (of which only 2 are sufficient for model prediction).
  • the acquisition plane was automatically classified using a Decision Tree algorithm. At each split, the algorithm maximizes the reduction in impurity or variance, resulting in a treelike structure where each node represents a decision based on a feature value.
  • the final predictions are made by traversing the tree from the root node to a leaf node corresponding to the specific class.
  • the model performed at 99.96% accuracy on the test dataset. On 1453 axial, 303 coronal, and 510 sagittal scans in the test dataset, the model achieved precision and recall of 1.00 for each class and misclassified only a single sagittal image as coronal. It is important to note however, that imaging is rarely done in the perfectly square direction system, and at 45 degrees classification is ambiguous.
  • the Random Forest algorithm was selected for the model in this Experimental Step.
  • the Random Forrest Classifier using Scikit-Leam was implemented, and multiple model configurations were tested.
  • the finally selected configuration used Random Forrest with 100 estimators, a maximum depth of 7, and a Gini impurity criterion.
  • the model input is the array of 11 features described in Experimental Step 1, and the output is the probability for each 11 modality classes, where the highest probability is the model prediction.
  • a K-Fold Cross-Validation analysis provides better insights into the model behavior and confirms the robustness of the high-accuracy results.
  • the model was tested on an independent dataset of Minipig Head MRI. This dataset contained 357 datasets across four modalities and was collected using a body coil rather than the typical bird-cage coil. Table 2 displays precision, recall and fl -score metrics and the number of images of each modality in the support column. For each of the dataset modalities, a very high precision and recall was observed, except for the Field Map, which had a much lower recall at 0.79. Overall, the model performed at an accuracy of 98.6% - comparable to the performance on human data. Interestingly, however, the model misclassified one DWI gradient and Field Map image as TraceW, although this data type was absent in the dataset.
  • the data processing workflow has four main steps, each tailored to enhance the quality and usability of MRI data for stroke outcome analysis.
  • the DICOM images from the chosen modalities — T1 -weighted, T2- weighted, FLAIR, ADC, and Trace-Weighted — are transformed into NlfTI format via the dcm2niix tool and ITK. This step facilitates the transition from individual 2D slices to integrated 3D volumes, essential for subsequent spatial analyses.
  • the MIQA quality assurance tool leverages a deep learning model specifically trained for image quality assessment. This allows the objective selection of the highest-quality images for each modality. When faced with multiple sequences of the same modality, preference is given to the sequence that the MIQA tool rates as the highest quality.
  • the third step involves taking the best-quality anatomical scan and synthesizing a high-resolution, normalized Tl- weighted image suitable for registration.
  • the SynthSR tool was used.
  • the final step is the performance of brain masking and skull stripping using SynthStrip.
  • the brainmasks are critical for defining the specific region for registration, while the skull-stripped data is a prerequisite for the accurate segmentation of brain lesions.
  • Co-registration of MRI images is a pivotal step in the workflow due to the inherent distortions that may arise even within the same scanning session.
  • images are captured in close temporal proximity, different modalities can act like varied lenses, each introducing its own distortions. Additionally, involuntary patient movement during the session can lead to misalignment, which can negatively impact further processing. Thus, precise image registration is imperative.
  • Co-registration was executed by selecting the highest quality anatomical scan (Tlw, T2w, or FLAIR) as the registration target. Subsequently, the ADC and Trace-Weighted images are co-registered to the target using rigid and affine transformations, aligning all images within the same physical space. This step is enhanced by the utilization of the brainmask for each image, which facilitates more precise sampling during co-registration.
  • the second phase involves transforming the images to the MNI space.
  • This standard space is widely recognized in neuroimaging and is particularly indispensable for lesionbehavior mapping studies, which necessitate that all data be situated within a uniform physical reference frame.
  • This example leverages the predictive power of specific brain regions within the left posterior white matter concerning general cognitive impairment.
  • the employed methods and tools have automated the categorization of subjects into high and low-risk groups, based on the overlap of the lesion masks — generated in the segmentation step — with a priori defined regions of interest (“ROI”s).
  • the initial ROI was derived from the most statistically significant anatomical cluster on the multivariate lesion- symptom map of general cognitive ability (g). This cluster was situated at the intersection of several white matter association tracts, notably featuring the arcuate fasciculus. Additionally, an ROI derived from an edge density map was incorporated, employing a threshold that has been previously shown to correlate with significant cognitive deficits.
  • a pivotal aspect of the tool’s design is its fully automated operation, which necessitates only two inputs: the directory path to the patient’s raw DICOM data directory and the destination path for the Brain Imaging Data Structure (BIDS) formatted results.
  • BIDS Brain Imaging Data Structure
  • the pipeline reaches completion in approximately 12 minutes when executed on a standard CPU. It is noteworthy that a substantial portion of this duration — up to 10 minutes — is dedicated to the computationally intensive task of the non-linear registration step.
  • the analysis process can begin with extracting key features from well-characterized DICOM header fields to identify image modality and an acquisition plane with high accuracy, such as over 99% accuracy.
  • the capabilities of the system in this example were expanded with post-processing functionality that allows to automatically select the modalities of interest and transform them into volumetric representation utilizing Neuroimaging Informatics Technology Initiative (NIITI) or Nearly Raw Raster Data (NRRD) format.
  • NIITI Neuroimaging Informatics Technology Initiative
  • NRRD Nearly Raw Raster Data
  • the selection of data can be motivated by standard post-stroke imaging protocols and can include: Tl-weighted, T2-weighted, Fluid-Attenuated Inverse Recovery (FLAIR), Apparent Diffusivity Coefficient (ADC), and the Diffusion Trace-Weighted acquired in axial direction or isotropic.
  • FLAIR Fluid-Attenuated Inverse Recovery
  • ADC Apparent Diffusivity Coefficient
  • Diffusion Trace-Weighted acquired in axial direction or iso
  • the data processing workflow has three main steps, each tailored to enhance the quality and usability of MRI data for stroke outcome analysis.
  • Second, data quality assurance can be performed using the Medical Image Quality Assurance (MIQA) quality assurance tool, which leverages a deep learning model specifically trained for image quality assessment. This allows the objective selection of the highest-quality images for each modality and best structural image overall, which can serve as the target image for subsequent registration.
  • MIQA Medical Image Quality Assurance
  • data derivatives can be created as follows. From the target image, a high- resolution, normalized T 1 -weighted image can be created using SynthSR. For each image, a binary brain mask was created using a custom trained model based on the ResUNet architecture, which achieved > 0.98 DICE score compared to ground-truth segmentations. The brain masks are also used to create the skull-stripped images which will be utilized in further parts of the system.
  • the subsequent task of the lesion-based outcome prediction utilizes lesion-symptom mapping techniques are defined in the Montreal Neurological Institute (MNI) space, a widely recognized standard in neuroimaging, and thus requiring us to transform patient data to the MNI space.
  • MNI Montreal Neurological Institute
  • the highest quality images are co-registered per modality to the target image (as defined in DICOM Data Selection).
  • the Tl-weighted image which was synthesized from the target image, is used to create the transform from the patient native space to MNI space.
  • the co-registered data can then be leveraged to resample all images to the template space.
  • Both co-registration and MNI registration use custom-implemented with ITK Rigid and Affine transformations.
  • MNI registration also performs non-linear SyN transformation utilizing Advanced Normalization Toolkit (ANTs). All stages utilize the pre-computed brain masks to define the registration sampling region.
  • ANTs Advanced Normalization Toolkit
  • This example produces a clinical tool that can automatically leverage clinical imaging data to generate individual predictions of cognitive outcomes after stroke.
  • BNT Boston Naming Test
  • the BNT is a commonly used assessment of post-stroke language function. It has previously been shown that post-stroke BNT performance can be successfully predicted in independent datasets using information about lesion location (i.e., lesion-behavior maps) and its relationship to distributed structural and functional brain networks (i.e., lesion-network maps) making it well- suited for demonstrating the feasibility of automated cognitive outcome prediction.
  • Lesionbehavior and lesion-network maps were used to generate a simple proof-of-concept outcome prediction model that can be applied to the automatically generated lesion segmentations to obtain predicted BNT scores for individual patients as part of our end-to-end pipeline.
  • Model training was performed using functions implemented in the MATLAB r2022b Statistics and Machine Learning Toolbox.
  • a ridge regression model was trained with stratified 5-fold cross-validation to tune the lambda hyperparameter. Training set patients were stratified into 4 groups based on the quartiles of the training set BNT score distribution prior to defining the train/test partitions for hyper-parameter optimization using the function cvpartition() with the “Stratify” option set to true. Model training was conducted using the function fitrlinear(). The outcome variable for the model was the normalized BNT score.
  • the predictor data were defined as lesion loads on each of the lesion-behavior, structural lesion-network, and functional lesion-network maps, where lesion loads for each map are computed as the sum of voxel weights within each patient’s lesion, divided by the sum of all voxel values within the map.
  • Predictor and outcome data were standardized (i.e., converted to z-scores) prior to model training, and the center and scale parameters obtained from the training dataset were saved so that they could be applied to the test dataset prior to obtaining model predictions.
  • the lesion load predictor computation step was embedded along with the fully trained prediction model into the automated pipeline, along with the center and scale factors obtained from the training dataset.
  • the full end-to-end pipeline was then run to produce lesion segmentations, compute lesion load predictors, and obtain predicted BNT scores for each patient in the test dataset.
  • the prediction performance in the test dataset was evaluated by computing the prediction R-squared (i.e., explained variance) using the sum-of- squares formulation along with the Pearson correlation between predicted and observed scores.
  • test dataset for this example comprised retrospective clinical data from 263 acute ischemic stroke patients treated at the University of Iowa Healthcare system. All imaging data were acquired within one week of stroke onset, with sampling dates ranging from 2001 to 2024. The dataset reflects the diversity of clinical settings, encompassing images from 27 unique scanner models across 6 manufacturers. This variability in imaging sources underscores the robustness of our system across different hardware configurations.
  • Subjects were deemed eligible for processing if their scanning session included an ADC sequence, a Trace-Weighted image, and at least one structural image (T 1 -weighted, T2- weighted, or FLAIR). These eligibility criteria ensure the availability of essential data for our analytical pipeline while accommodating the variability in clinical imaging protocols. All eligible subjects were subsequently enrolled in a research program that included a battery of behavioral tests administered during the chronic stage of stroke. Due to various clinical and logistical factors, not all patients completed the same set of assessments. Table 3 summarizes the behavioral tests employed in this study, detailing the number of patients with available scores for each test and the number of patients classified as impaired.
  • Fig. 7 shows that it often under-estimated the severity of the predicted deficits (i.e., many of the cases fall to the left of the identity line). This result demonstrates the feasibility of cognitive outcome prediction within the context of a fully automated lesion segmentation and outcome prediction pipeline.

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Abstract

L'invention divulgue un procédé d'analyse de données d'imagerie cérébrale à partir d'un patient atteint de lésions cérébrales et de génération d'informations de pronostic. Le procédé comprend l'obtention de données d'imagerie du cerveau d'un patient à partir d'un scanner et la classification des données par plan et modalité d'acquisition pour sélectionner une série de données d'intérêt. Le procédé consiste en outre à traiter la série de données d'intérêt pour générer des masques cérébraux et des données volumétriques correspondantes. Les masques cérébraux et les données volumétriques correspondantes sont ensuite co-enregistrés dans un espace cérébral commun, et des données volumétriques enregistrées sont générées. Une segmentation de lésion est effectuée pour générer un masque de lésion dans l'espace cérébral commun. Les données de masque de lésion sont appliquées à un modèle de pronostic entraîné, qui comprend un modèle d'apprentissage automatique configuré pour délivrer un pronostic basé sur la probabilité de la lésion cérébrale du patient.
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