Diffusion-weighted Magnetic Resonance Imaging Research Articles

Cerebral Embolic Protection: Is There a Benefit for Left Atrial and Mitral Valve Procedures?

This review aims to highlight the current evidence on the use of cerebral embolic protection devices (CEPD) in left atrial and transcatheter mitral valve procedures. It also aims to summarize the antithrombotic management of patients undergoing such procedures. Ischemic stroke is one of the most devastating complications of structural heart procedures. The manifestation of periprocedural stroke can range from asymptomatic and detectable only through brain imaging to major stroke with neurological deficits. CEP devices were initially developed to mitigate the risk of stroke associated with transcatheter aortic valve replacement (TAVR). However, the efficacy of such devices during different cardiac interventions is yet to be fully demonstrated, especially in left atrial appendage closure (LAAO), and mitral valve interventions. Few studies demonstrated that the risk of periprocedural strokes after LAAO and mitral valve interventions is not negligible and is highest during the periprocedural period and then falls. The majority of patients undergoing those procedures have cerebral ischemic injuries detected on diffusion-weighted magnetic resonance imaging (DW-MRI). Moreover, a reasonable number of those patients had debris embolization on the filters of the CEPD. Pharmacological therapy with antithrombotic agents before, during, or after structural heart interventions is crucial and should be tailored to each patient's risk of bleeding and ischemia. Close monitoring that includes a full neurological assessment and frequent follow-up visits with cardiac echocardiography are important. The risk of periprocedural stroke in left atrial and transcatheter mitral valve procedures is not negligible. Pharmacological therapy with antithrombotic agents before, during, or after structural heart interventions is important to mitigate the risk of stroke, especially the long-term risk. More prospective studies are needed to assess the efficacy of CEPD in such procedures.

Vendor-Specific Correction Software for Apparent Diffusion Coefficient Bias Due to Gradient Nonlinearity in Breast Diffusion-Weighted Imaging Using Ice-Water Phantom.

This study aimed to evaluate a vendor-specific correction software for apparent diffusion coefficient (ADC) bias due to gradient nonlinearity in breast diffusion-weighted magnetic resonance imaging using an ice-water phantom. The phantom consists of 5 plastic tubes with a length of 100 mm and a diameter of 15 mm, filled with distilled water and immersed in an ice-water bath. Diffusion-weighted images were acquired by echo-planar imaging sequence on a 3.0-T scanner. ADC maps with and without correction were calculated using 4 b -values (0, 100, 600, and 800 s/mm 2 ). The mean ADCs were measured using a rectangular profile with 5 × 40 pixels in the anterior-posterior (AP) and a square region of interest with 5 × 5 pixels in the right-left (RL) and superior-inferior (SI) directions on the ADC map. ADC was compared with and without correction using a paired t test. Additionally, ADC of the ice-water phantom was measured at the magnet isocenter. ADC increased in the AP and RL directions and decreased in the SI direction with increasing distance from the isocenter before correction. After the correction, ADC at the off-center positions in the AP, RL, and SI directions was reduced to within 5% of the expected value. There were significant differences in the ADC at the off-center positions without and with correction ( P < 0.001); however, ADC at the magnet isocenter did not vary after correction (1.08 ± 0.02 × 10 -3 mm 2 /s). The vendor-specific software corrected the ADC bias due to gradient nonlinearity at the off-center positions in the AP, RL, and SI directions. Therefore, the software will contribute to the accurate ADC assessment in breast DWI.

Robust, fast and accurate mapping of diffusional mean kurtosis

Diffusional kurtosis imaging (DKI) is a methodology for measuring the extent of non-Gaussian diffusion in biological tissue, which has shown great promise in clinical diagnosis, treatment planning, and monitoring of many neurological diseases and disorders. However, robust, fast, and accurate estimation of kurtosis from clinically feasible data acquisitions remains a challenge. In this study, we first outline a new accurate approach of estimating mean kurtosis via the sub-diffusion mathematical framework. Crucially, this extension of the conventional DKI overcomes the limitation on the maximum b-value of the latter. Kurtosis and diffusivity can now be simply computed as functions of the sub-diffusion model parameters. Second, we propose a new fast and robust fitting procedure to estimate the sub-diffusion model parameters using two diffusion times without increasing acquisition time as for the conventional DKI. Third, our sub-diffusion-based kurtosis mapping method is evaluated using both simulations and the Connectome 1.0 human brain data. Exquisite tissue contrast is achieved even when the diffusion encoded data is collected in only minutes. In summary, our findings suggest robust, fast, and accurate estimation of mean kurtosis can be realised within a clinically feasible diffusion-weighted magnetic resonance imaging data acquisition time.

Robust, fast and accurate mapping of diffusional mean kurtosis.

Diffusional kurtosis imaging (DKI) is a methodology for measuring the extent of non-Gaussian diffusion in biological tissue, which has shown great promise in clinical diagnosis, treatment planning, and monitoring of many neurological diseases and disorders. However, robust, fast, and accurate estimation of kurtosis from clinically feasible data acquisitions remains a challenge. In this study, we first outline a new accurate approach of estimating mean kurtosis via the sub-diffusion mathematical framework. Crucially, this extension of the conventional DKI overcomes the limitation on the maximum b-value of the latter. Kurtosis and diffusivity can now be simply computed as functions of the sub-diffusion model parameters. Second, we propose a new fast and robust fitting procedure to estimate the sub-diffusion model parameters using two diffusion times without increasing acquisition time as for the conventional DKI. Third, our sub-diffusion-based kurtosis mapping method is evaluated using both simulations and the Connectome 1.0 human brain data. Exquisite tissue contrast is achieved even when the diffusion encoded data is collected in only minutes. In summary, our findings suggest robust, fast, and accurate estimation of mean kurtosis can be realised within a clinically feasible diffusion-weighted magnetic resonance imaging data acquisition time.

Discovering prominent differences in structural and functional connectomes using a multinomial stochastic block model

ABSTRACT Understanding the differences between functional and structural human brain connectivity has been a focus of an extensive amount of neuroscience research. We employ a novel approach using the multinomial stochastic block model (MSBM) to explicitly extract components that characterize prominent differences across graphs. We analyze structural and functional connectomes derived from high-resolution diffusion-weighted MRI and fMRI scans of 250 Human Connectome Project subjects, analyzed at group connectivity level across 50 subjects. The inferred brain partitions revealed consistent, spatially homogeneous clustering patterns across inferred resolutions demonstrating the MSBM’s reliability in identifying brain areas with prominent structure-function differences. Prominent differences in low-resolution brain maps (K = {3, 4} clusters) were attributed to weak functional connectivity in the bilateral anterior temporal lobes, while higher resolution results (K ≥ 25) revealed stronger interhemispheric functional than structural connectivity. Our findings emphasize significant differences in high-resolution functional and structural connectomes, revealing challenges in extracting meaningful connectivity measurements from both modalities, including tracking fibers through the corpus callosum and attenuated functional connectivity in anterior temporal lobe fMRI data, which we attribute to increased noise levels. The MSBM emerges as a valuable tool for understanding differences across graphs, with potential future applications and avenues beyond the current focus on characterizing modality-specific distinctions in connectomics data.

Assessment of chemical-shift and diffusion-weighted magnetic resonance imaging in differentiating malignant and benign vertebral lesions in oncologic patients. A single institution experience.

To analyze the contribution of two non-standard magnetic resonance imaging (MRI) techniques the chemical-shift image (CSI), and diffusion-weighted imaging (DWI) in distinguishing malignant and benign vertebral bone marrow lesions (VBMLs). Conventional spine MRI protocol, followed by CSI and DWI was performed with a 1.5 T system on 102 oncologic patients between January 2020 and December 2023. From the identified 325 VBMLs, 102 representative lesions (one per patient) were selected. VBMLs were divided into malignant (n = 74) and benign (n = 28) based on histopathology, or imaging follow-up. The quantitative parameters for VBMLs assessment were signal intensity ratio (SIR) derived from CSI and apparent diffusion coefficient (ADC) derived from DWI. The malignant VBMLs had significantly higher SIR values (p < 0.05) and lower ADC values compared to benign VBMLs (p < 0.05). The area under the curve (AUC) was 0.953 (p < 0.001) for SIR, and 0.894 for ADC (p < 0.001) (cut-off at > 0.82, and ≤ 1.57x10-3 mm2/s, respectively). The sensitivity and specificity for SIR were 93.6%, and 88.5%, while for ADC were 88.2% and 92.3% (respectively). The combined use of SIR and ADC improved the diagnostic accuracy to AUC of 0.988 (p < 0.001, cut-off at > 0.19), sensitivity, and specificity of 100.0% and 90.9% (respectively). Quantitative parameters, SIR and ADC, derived from two non-standard MRI techniques, CSI, and DWI, showed diagnostic strength in differentiating malignant and benign VBMLs. Combining both methods can further enhance the diagnostic performance and accuracy of spine MRI in clinical practice.

A spatially constrained independent component analysis jointly informed by structural and functional network connectivity

Abstract There are a growing number of neuroimaging studies motivating joint structural and functional brain connectivity. The brain connectivity of different modalities provides an insight into brain functional organization by leveraging complementary information, especially for brain disorders such as schizophrenia. In this paper, we propose a multimodal independent component analysis (ICA) model that utilizes information from both structural and functional brain connectivity guided by spatial maps to estimate intrinsic connectivity networks (ICNs). Structural connectivity is estimated through whole-brain tractography on diffusion-weighted MRI (dMRI), while functional connectivity is derived from resting-state functional MRI (rs-fMRI). The proposed structural-functional connectivity and spatially constrained ICA (sfCICA) model estimates ICNs at the subject level using a multiobjective optimization framework. We evaluated our model using synthetic and real datasets (including dMRI and rs-fMRI from 149 schizophrenia patients and 162 controls). Multimodal ICNs revealed enhanced functional coupling between ICNs with higher structural connectivity, improved modularity, and network distinction, particularly in schizophrenia. Statistical analysis of group differences showed more significant differences in the proposed model compared with the unimodal model. In summary, the sfCICA model showed benefits from being jointly informed by structural and functional connectivity. These findings suggest advantages in simultaneously learning effectively and enhancing connectivity estimates using structural connectivity.

Cholinergic Basal Forebrain Integrity and Cognition in Parkinson's Disease: A Reappraisal of Magnetic Resonance Imaging Evidence.

Cognitive impairment is a well-recognized and debilitating symptom of Parkinson's disease (PD). Degradation in the cortical cholinergic system is thought to be a key contributor. Both postmortem and in vivo cholinergic positron emission tomography (PET) studies have provided valuable evidence of cholinergic system changes in PD, which are pronounced in PD dementia (PDD). A growing body of literature has employed magnetic resonance imaging (MRI), a noninvasive, more cost-effective alternative to PET, to examine cholinergic system structural changes in PD. This review provides a comprehensive discussion of the methodologies and findings of studies that have focused on the relationship between cholinergic basal forebrain (cBF) integrity, based on T1- and diffusion-weighted MRI, and cognitive function in PD. Nucleus basalis of Meynert (Ch4) volume has been consistently reduced in cognitively impaired PD samples and has shown potential utility as a prognostic indicator for future cognitive decline. However, the extent of structural changes in Ch4, especially in early stages of cognitive decline in PD, remains unclear. In addition, evidence for structural change in anterior cBF regions in PD has not been well established. This review underscores the importance of continued cross-sectional and longitudinal research to elucidate the role of cholinergic dysfunction in the cognitive manifestations of PD. © 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Improving Stroke Outcome Prediction Using Molecular and Machine Learning Approaches in Large Vessel Occlusion.

Introduction: Predicting stroke outcomes in acute ischemic stroke (AIS) can be challenging, especially for patients with large vessel occlusion (LVO). Available tools such as infarct volume and the National Institute of Health Stroke Scale (NIHSS) have shown limited accuracy in predicting outcomes for this specific patient population. The present study aimed to confirm whether sudden metabolic changes due to blood-brain barrier (BBB) disruption during LVO reflect differences in circulating metabolites and RNA between small and large core strokes. The second objective was to evaluate whether integrating molecular markers with existing neurological and imaging tools can enhance outcome predictions in LVO strokes. Methods: The infarction volume in patients was measured using magnetic resonance diffusion-weighted images, and the 90-day stroke outcome was defined by a modified Rankin Scale (mRS). Differential expression patterns of miRNAs were identified by RNA sequencing of serum-driven exosomes. Nuclear magnetic resonance (NMR) spectroscopy was used to identify metabolites associated with AIS with small and large infarctions. Results: We identified 41 miRNAs and 11 metabolites to be significantly associated with infarct volume in a multivariate regression analysis after adjusting for the confounders. Eight miRNAs and ketone bodies correlated significantly with infarct volume, NIHSS (severity), and mRS (outcome). Through integrative analysis of clinical, radiological, and omics data using machine learning, our study identified 11 top features for predicting stroke outcomes with an accuracy of 0.81 and AUC of 0.91. Conclusions: Our study provides a future framework for advancing stroke therapeutics by incorporating molecular markers into the existing neurological and imaging tools to improve predictive efficacy and enhance patient outcomes.

Diffusion-weighted MRI in characterization of enlarged cervical lymph nodes

Background Diffusion-weighted MRI (DWI) has been shown to enhance the visibility of sub-centimetric lymph nodes (LNs) by suppressing background tissue, thereby facilitating LN identification in comparison to conventional sequences. Aim The purpose of this research is to evaluate the efficacy of DWI and apparent diffusion coefficient in distinguishing among cancerous and benign cervical lymphadenopathy. Patients and methods This prospective research includes 50 cases complaining of cervical LN swelling referred to MR units of Radiodiagnosis and Clinical Oncology department at Tanta University Hospital, with symptoms of enlarged cervical LN. Results The most frequently encountered lesion was lymphoma in 12 (24.00%) cases, followed by metastatic from thyroid carcinoma in 7 (14%) cases and metastasis from cancer parotid in 6 (12%) cases, 5 (10%) cases were diagnosed as metastasis from tongue, metastasis from oropharyngeal mass was found in 4 (8%) cases, 3 cases were diagnosed as TB and acute inflammatory with percentage of (6%) for each one, where there were 2 cases diagnosed with percentage of (4%). This study revealed that DWI is a technique for investigation of tumors histological content of LNs and used for distinguishing among benign and cancerous ones. Conclusion DWI with apparent diffusion coefficient map is a novel and promising method for distinguishing cancerous from noncancerous LN. The addition of DWI to standard MRI gives extra biological and functional information for characterizing cervical LN.

Magnetic resonance diffusion-weighted imaging in lacrimal gland lymphoma versus inflammation: A comparative study.

To determine the role of diffusion-weighted imaging (DWI) with apparent diffusion coefficient (ADC) in differentiating the inflammatory process and malignant lymphoma of the lacrimal gland. A retrospective analysis of all subjects who underwent lacrimal gland biopsy and magnetic resonance (MR) imaging with DWI sequences during a 10-year period at the Sheba Medical Center, Israel. The lacrimal glands' ADC values were documented bilaterally by blinded observers and correlated with the final histology verified diagnoses. Twenty-eight patients were included, with 19 females (68%) with a mean ± SD age of 48.1 ± 25.7 years. The right orbit was involved in 14 cases (50%) and the left in 13 (46%); one patient (4%) had bilateral involvement. Seventeen cases (61%) had a final diagnosis of idiopathic inflammation or dacryoadenitis, and six cases (21%) were diagnosed with lymphoma. Additional diagnoses included pleomorphic adenoma in 3 (11%), adenoid cystic carcinoma, and solitary fibrous tumor. Lower mean ADC values were observed in the lymphoma versus inflammatory group (1.03 × 10-3 Vs. 1.45 × 10-3, P = 0.02). Restricted diffusion on MR imaging can serve as a diagnostic tool in the differentiation between inflammatory processes of the lacrimal gland and lymphoma.

Optimal Setup and Parameters of Diffusion-Weighted Magnetic Resonance Imaging for Translational Evaluation of a Tumor Progression Model for Soft Tissue Sarcomas

PurposeDefining a microscopic tumor infiltration boundary is critical to the success of radiation therapy. Currently, radiation oncologists use margins to geometrically expand the visible tumor for radiation treatment planning in soft tissue sarcomas (STS). Image-based models of tumor progression would be critical to personalize treatment radiation field to the pattern of sarcoma spread. Evaluation of these models is necessary to demonstrate feasibility in the clinical setting. This study presents an imaging protocol for the preclinical evaluation of a tumor progression model in extremity STS. MethodsWe recruited seven healthy volunteers and acquired diffusion-weighted MRI (DW-MRI) images of the thigh on an MRI scanner used for imaging cancer patients in a radiation oncology department. We developed a protocol, which includes positioning the patient, configuring the RF coils, and setting the diffusion-weighted MRI sequence parameters. To find the optimal parameter configuration, the image signal-to-noise ratio (SNR) and the directional variability (DV) of the principal eigenvector of the diffusion tensor were calculated. ResultsThe mean SNR across all trials and 12 thigh muscles was 41 with a range of 12 to 72. The mean DV was 13° and ranged from 11° to 23°. The longest scan time was 22 min 58 s and the shortest 11 min 46 s. For the high-resolution image with a voxel volume of 1.3×1.3×6 mm3 and 38 slices, the optimal parameters were found to be a repetition time of 8000 ms, 12 signal averages, and 6 gradient directions. This configuration resulted in a scan time of 11 min 46 s, an SNR of 34, and a DV of 13°. ConclusionsA DW-MRI scan duration acceptable for imaging cancer patients was achieved with an image quality suitable for reproducible modeling of tumor infiltration. The developed protocol can be used for preclinical evaluation in STS patients.

MRI-based radiomics for stratifying recurrence risk of early-onset rectal cancer: a multicenter study

Early-onset rectal cancer (EORC) is characterized by a unique disease process with different clinicopathological features compared with late-onset rectal cancer (LORC). Research on the risk of recurrence in EORC patients, however, is limited. We aim to develop a predictive model to accurately predict EORC recurrence risk. Rectal cancer patients who underwent radical surgery and T2-weighted imaging and diffusion-weighted imaging magnetic resonance imaging (MRI) were retrospectively enrolled from three medical institutions from November 2012 to November 2018. Differences in clinicopathological characteristics between EORC and LORC were compared. Five prediction models for disease-free survival were constructed based on clinicopathological variables and five radiomic features from pretreatment MRI of the EORC. A fixed cut-off value calculated in the training set was used to stratify EORC patients into high-risk and low-risk groups of post-operative recurrence. Model performance was evaluated by concordance index (C-index) and receiver operating characteristic curve. A total of 264 EORC patients (median age, 43 years, 163 males) and 778 LORC patients (median age, 62 years, 520 males) were enrolled. Pretreatment positive carcinoembryonic antigen [hazard ratio (HR)= 2.84, P= 0.006], pathological positive lymph node status (pN positive) [HR= 2.86, P= 0.011] and MRI-based radiomics score [HR= 2.72, P < 0.001] are independent risk factors for disease-free survival in EORC patients. The EORC-ClinPathRadiom model, constructed by integrating the clinicopathological characteristics and MRI-based radiomics features of EORC, showed C-index of 0.82, 0.82, and 0.81 in the training, internal, and external test sets, respectively. This model effectively stratified EORC patients into high risk and low risk of recurrence (HRs for the training, internal, and external test sets were 8.96, 6.81, and 7.46, respectively). The EORC-ClinPathRadiom model can effectively predict and stratify the risk of post-operative recurrence in EORC patients.

Inflammatory Pseudotumor of the Ulnar Nerve Mimicking a Malignant Peripheral Nerve Sheath Tumor: A Radiologic Puzzle.

Inflammatory pseudotumor (IPT) of a peripheral nerve is a rare non-neoplastic tumefactive inflammatory condition, often mimicking malignancy. The etiology of this condition is still unknown. Clinically and radiologically, the lesion can mimic a malignant tumor. This case report represents, as far as we know, the first publication describing the ultrasonography findings and the results of advanced dynamic contrast-enhanced magnetic resonance imaging (MRI) and diffusion-weighted MRI of IPT in a peripheral nerve. Suspicion of this entity on imaging can speed up the definitive diagnosis and potentially avoid overly radical treatment.

141 - Water-excitation improves the image quality of diffusion-weighted MR imaging in esophageal cancer:Comparison with spectral attenuated inversion recovery diffusion-weighted imaging

141 - Water-excitation improves the image quality of diffusion-weighted MR imaging in esophageal cancer:Comparison with spectral attenuated inversion recovery diffusion-weighted imaging

Brain network fingerprints of Alzheimer's disease risk factors in mouse models with humanized APOE alleles

Alzheimer's disease (AD) presents complex challenges due to its multifactorial nature, poorly understood etiology, and late detection. The mechanisms through which genetic and modifiable risk factors influence disease susceptibility are under intense investigation, with APOE being the major genetic risk factor for late onset AD. Yet the impact of unique risk factors on brain networks is difficult to disentangle, and their interactions remain unclear.To model multiple risk factors, including APOE genotype, age, sex, diet, and immunity we used a cross sectional design, leveraging mice expressing human APOE and NOS2 genes, conferring a reduced immune response compared to mouse Nos2. We used network topological and GraphClass analyses of brain connectomes derived from accelerated diffusion-weighted MRI to assess the global and local impact of risk factors, in the absence of AD pathology.Aging and a high-fat diet impacted extensive networks comprising AD-vulnerable regions, including the temporal association cortex, amygdala, and the periaqueductal gray, involved in stress responses. Sex impacted networks including sexually dimorphic regions (thalamus, insula, hypothalamus) and key memory-processing areas (fimbria, septum). APOE genotypes modulated connectivity in memory, sensory, and motor regions, while diet and immunity both impacted the insula and hypothalamus. Notably, these risk factors converged on a circuit comprising 63 of 54,946 total connections (0.11% of the connectome), highlighting shared vulnerability amongst multiple AD risk factors in regions essential for sensory integration, emotional regulation, decision making, motor coordination, memory, homeostasis, and interoception. APOE genotype specific immune signatures support the design of interventions tailored to risk profiles. Sparse Canonical Correlation Analysis (CCA) including spatial memory as a risk factor resulted in a network comprising 80 edges, showing significant overlap with risk-associated networks from GraphClass. The largest overlaps were observed with networks impacted by diet (47 edges), immunity (39 edges), APOE3 vs 4 (26 edges), sex (23 edges), and age (19 edges), the resulting networks supporting the use of sensory cues in spatial memory retrieval.These network-based biomarkers hold translational value for distinguishing high-risk versus low-risk participants at preclinical AD stages, suggest circuits as potential therapeutic targets, and advance our understanding of network fingerprints associated with AD risk.

The QIBA Profile for Diffusion-Weighted MRI: Apparent Diffusion Coefficient as a Quantitative Imaging Biomarker.

The apparent diffusion coefficient (ADC) provides a quantitative measure of water mobility that can be used to probe alterations in tissue microstructure due to disease or treatment. Establishment of the accepted level of variance in ADC measurements for each clinical application is critical for its successful implementation. The Diffusion-Weighted Imaging Biomarker Committee of the Quantitative Imaging Biomarkers Alliance (QIBA) has recently advanced the ADC Profile from the consensus to clinically feasible stage for the brain, liver, prostate, and breast. This profile distills multiple studies on ADC repeatability and describes detailed procedures to achieve stated performance claims on an observed ADC change within acceptable confidence limits. In addition to reviewing the current ADC Profile claims, this report has used recent literature to develop proposed updates for establishing metrology benchmarks for mean lesion ADC change that account for measurement variance. Specifically, changes in mean ADC exceeding 8% for brain lesions, 27% for liver lesions, 27% for prostate lesions, and 15% for breast lesions are claimed to represent true changes with 95% confidence. This report also discusses the development of the ADC Profile, highlighting its various stages, and describes the workflow essential to achieving a standardized implementation of advanced quantitative diffusion-weighted MRI in the clinic. The presented QIBA ADC Profile guidelines should enable successful clinical application of ADC as a quantitative imaging biomarker and ensure reproducible ADC measurements that can be used to confidently evaluate longitudinal changes and treatment response for individual patients.

Deep Learning Classification of Ischemic Stroke Territory on Diffusion-Weighted MRI: Added Value of Augmenting the Input with Image Transformations.

Our primary aim with this study was to build a patient-level classifier for stroke territory in DWI using AI to facilitate fast triage of stroke to a dedicated stroke center. A retrospective collection of DWI images of 271 and 122 consecutive acute ischemic stroke patients from two centers was carried out. Pretrained MobileNetV2 and EfficientNetB0 architectures were used to classify territorial subtypes as middle cerebral artery, posterior circulation, or watershed infarcts along with normal slices. Various input combinations using edge maps, thresholding, and hard attention versions were explored. The effect of augmenting the three-channel inputs of pre-trained models on classification performance was analyzed. ROC analyses and confusion matrix-derived performance metrics of the models were reported. Of the 271 patients included in this study, 151 (55.7%) were male and 120 (44.3%) were female. One hundred twenty-nine patients had MCA (47.6%), 65 patients had posterior circulation (24%), and 77 patients had watershed (28.0%) infarcts for center 1. Of the 122 patients from center 2, 78 (64%) were male and 44 (34%) were female. Fifty-two patients (43%) had MCA, 51 patients had posterior circulation (42%), and 19 (15%) patients had watershed infarcts. The Mobile-Crop model had the best performance with 0.95 accuracy and a 0.91 mean f1 score for slice-wise classification and 0.88 accuracy on external test sets, along with a 0.92 mean AUC. In conclusion, modified pre-trained models may be augmented with the transformation of images to provide a more accurate classification of affected territory by stroke in DWI.

Role of diffusion weighted imaging in assessment of anterior cruciate ligament pathology

Background. The anterior cruciate ligament (ACL) is a structure that is vital to preserving the knee's normal stability. However, because the ACL is the ligament that sustains injuries the most frequently, it is essential to distinguish between complete and incomplete tears in order to treat patients with incomplete tears without surgery. To diagnose cruciate ligament tears, knee joint magnetic resonance imaging (MRI) is frequently used. But sometimes it's difficult because edema may hide the ligament fibers. ADC mapping may be useful to confirm whether or not the ligament is continuous for an appropriate diagnosis of incomplete tears because it has been demonstrated that ligament fibers are better visible on diffusion weighted imaging (DWI) and ADC mapping. Objective. To ascertain how anterior cruciate ligament (ACL) tears are diagnosed and classified as partial or complete using diffusion-weighted (DW) magnetic resonance imaging (MRI) and to compare it with conventional MRI sequences. Results. For partial tears, 10 cases (50%) were identified using arthroscopy/follow-up, while observer 1 identified 12 cases (60%) and observer 2 identified 9 cases (45%) using DWI/ADC. For complete tears, 10 cases (50%) were identified using arthroscopy/follow-up, while observer 1 identified 8 cases (40%) and observer 2 identified 11 cases (55%) using DWI/ADC. Conclusions. DWI and ADC are useful tools for diagnosing tears, particularly complete tears, but that caution should be exercised when using these techniques to diagnose partial tears.

Role of MRI in Diagnosis of Rapidly Progressive Cognitive Impairment- A Case Of Sporadic Creutzfeldt Jakob Disease (sCJD)

Abstract Background The role of neuroimaging in dementia extends beyond its traditional role of excluding neurosurgical lesions. Radiological findings may support the diagnosis of specific neurodegenerative disorders and aid differentiation from other neurodegenerative conditions e.g. CJD1. sCJD is a fatal neurodegenerative disease, which typically presents with neuropsychiatric symptoms, myoclonus, cerebellar and pyramidal/extrapyramidal symptoms. The diagnosis often is quite difficult, requiring high degree of suspicion, advanced neuroimaging and biochemical markers. Confirmation is only possible via brain biopsy. The condition can result in rapidly progressive dementia/neurocognitive decline and death, usually within a year from onset. Methods We report a case of a rapidly progressive dementia in a 79-year-old male who was diagnosed with probable sCJD, after having an MRI which picked up suspicious characteristic findings leading to the eventual diagnosis confirmation via further workup. The patient presented with acute rapid progressive forgetfulness and functional decline of 2 weeks with initial workup unyielding. Results A contrast MRI Brain eventually showed focal increased T2 and flair signals in the right parafalcine and parietal cortex demonstrating mild fluid restriction without any focal contrast enhancement, a pattern highly suspicious for sCJD. A CSF sample subsequently revealed RT-QulC positivity confirming the diagnosis. The patient eventually passed away despite ward-based care after 4 weeks of admission due to rapid neurological decline. A brain biopsy was not deemed feasible given patient’s condition. Conclusion The case highlights a relatively atypical symptomology of sCJD and, highlights the role of MRI imaging in assessment and diagnosis of dementias and specifically in sCJD. Increasing use of advanced neuroimaging may aid in diagnosis of specific/rare causes of rapidly progressive dementias. Reference Vitali, P., Maccagnano, E., Caverzasi, E., Henry, R.G., Haman, A., Torres-Chae, C., Johnson, D.Y., Miller, B.L. and Geschwind, M.D., 2011. Diffusion-weighted MRI hyperintensity patterns differentiate CJD from other rapid dementias. Neurology, 76(20), pp. 1711-1719.