Multiple Magnetic Resonance Imaging Research Articles

This Is What Metabolic Dysfunction-Associated Steatotic Liver Disease Looks Like: Potential of a Multiparametric MRI Protocol.

Metabolic dysfunction-associated steatotic liver disease (MASLD) is a prevalent condition with a broad spectrum defined by liver biopsy. This gold standard method evaluates three features: steatosis, activity (ballooning and lobular inflammation), and fibrosis, attributing them to certain grades or stages using a semiquantitative scoring system. However, liver biopsy is subject to numerous restrictions, creating an unmet need for a reliable and reproducible method for MASLD assessment, grading, and staging. Noninvasive imaging modalities, such as magnetic resonance imaging (MRI), offer the potential to assess quantitative liver parameters. This review aims to provide an overview of the available MRI techniques for the three criteria evaluated individually by liver histology. Here, we discuss the possibility of combining multiple MRI parameters to replace liver biopsy with a holistic, multiparametric MRI protocol. In conclusion, the development and implementation of such an approach could significantly improve the diagnosis and management of MASLD, reducing the need for invasive procedures and paving the way for more personalized treatment strategies.

Convolutional neural network and glioma genotyping: A systematic review.

e14038 Background: Gliomas are the most common primary intracranial tumors. Overall, there are 23 different glioma types, and the grading and prognosis of each type are determined by their specific mutations. Several identified mutations are IDH gene status, CDKN2A/B homozygote delegation, PTEN mutation, p53 mutation, TERT promotor mutation, H3K27M mutation, MGMT promotor methylation, trisomy 7/monozomy10, EGFR amplification, ATRX mutation, 1p/19q codeletion, and BRAF mutation. These specific mutations and genomic features can have their distinctive radiologic features seen in different magnetic resonance imaging (MRI) sequences. By using a Convolutional Neural Network (CNN) for feature extraction, the genotyping power of MRI for glioma could be increased. To address this, we systematically reviewed the use of different CNN-based models to detect the genetic context of glioma based on the MRI images. Methods: We conducted a systematic search in PubMed, Embase, Scopus, Web of Science, and Google Scholar. We included original peer-reviewed human studies (retrospective or prospective), focusing on the use of CNN in individuals with glioma. We excluded animal studies, studies with no CNN-based models, reviews, book chapters, conference abstracts, and case reports. JBI checklists were utilized for quality assessment. Results: The initial search resulted in 973 articles. after removing duplicates, 563 studies entered the screening process by title and abstract. 59 studies met our inclusion criteria (56 retrospective and 3 prospective cohort studies) with a total of 26434 glioma patients included (studies ranged from 21 – 2648 individuals). 31 studies used CNN methods only in different stages of image processing (tumor segmentation, feature extraction, image encoder) and 28 studies used CNN as a classifier and mutation predictor. ResNet was the most commonly used CNN-based model (n = 14). U-net (n = 7) and DenseNet models (n = 4) were other commonly used CNN-based models. Included studies targeted IDH status (n=44), 1p/19q codeletion (n=18), MGMT promotor methylation (n=11), ATRX mutation (n=5), TERT promotor mutation (n=5), CDKN2A/B homozygote deletion (n=3), PTEN mutation (n=3), p53 mutation (n=3), H3K27M mutation (n=2), trisomy 7/monozomy10 (n=2), EGFR amplification (n=2), and BRAF mutation (n=1). Highly severe methodological heterogeneity among these studies excluded the possibility of meta-analysis. Conclusions: Current studies have shown that, as a non-invasive method, using CNN-based models on multiple MRI sequences increases the power of early detection and prediction of glioma genetic types before pathological confirmation and has promising results. However, DL performance varies among mutations. Combining multiple DL models with clinical and radiomic features in different and larger databases can further increase the final results.

Enhancing Brain Segmentation in MRI through Integration of Hidden Markov Random Field Model and Whale Optimization Algorithm

The automatic delineation and segmentation of the brain tissues from Magnetic Resonance Images (MRIs) is a great challenge in the medical context. The difficulty of this task arises out of the similar visual appearance of neighboring brain structures in MR images. In this study, we present an automatic approach for robust and accurate brain tissue boundary outlining in MR images. This algorithm is proposed for the tissue classification of MR brain images into White Matter (WM), Gray Matter (GM) and Cerebrospinal Fluid (CSF). The proposed segmentation process combines two algorithms, the Hidden Markov Random Field (HMRF) model and the Whale Optimization Algorithm (WOA), to enhance the treatment accuracy. In addition, we use the Whale Optimization Algorithm (WOA) to optimize the performance of the segmentation method. The experimental results from a dataset of brain MR images show the superiority of our proposed method, referred to HMRF-WOA, as compared to other reported approaches. The HMRF-WOA is evaluated on multiple MRI contrasts, including both simulated and real MR brain images. The well-known Dice coefficient (DC) and Jaccard coefficient (JC) were used as similarity metrics. The results show that, in many cases, our proposed method approaches the perfect segmentation with a Dice coefficient and Jaccard coefficient above 0.9.

Rate of Change in Cardiac Magnetic Resonance Imaging Measures Is Associated With Death in Duchenne Muscular Dystrophy.

Cardiovascular disease is the leading cause of death among patients with Duchenne muscular dystrophy (DMD). Identifying patients at risk of early death could allow for increased monitoring and more intensive therapy. Measures that associate with death could serve as surrogate outcomes in clinical trials. Duchenne muscular dystrophy subjects prospectively enrolled in observational studies were included. Models using generalized least squares were used to assess the difference of cardiac magnetic resonance measurements between deceased and alive subjects. A total of 63 participants underwent multiple cardiac magnetic resonance imaging and were included in the analyses. Twelve subjects (19.1%) died over a median follow-up of 5 years (interquartile range, 3.1-7.0). Rate of decline in left ventricular ejection fraction was faster in deceased than alive subjects (P<0.0001). Rate of increase in indexed left ventricular end-diastolic (P=0.0132) and systolic (P<0.0001) volumes were higher in deceased subjects. Faster worsening in midcircumferential strain was seen in deceased subjects (P=0.049) while no difference in global circumferential strain was seen. The rate of increase in late gadolinium enhancement, base T1, and mid T1 did not differ between groups. Duchenne muscular dystrophy death is associated with the rate of change in left ventricular ejection fraction, midcircumferential strain, and ventricular volumes. Aggressive medical therapy to decrease the rate of progression may improve the mortality rate in this population. A decrease in the rate of progression may serve as a valid surrogate outcome for therapeutic trials.

A discrepancy-aware self-distillation method for multi-modal glioma grading

Gliomas are the most common intracranial primary tumors. Accurate grading of gliomas is crucial in determining treatment options and prognosis. Clinicians conventionally rely on multiple Magnetic Resonance Imaging (MRI) sequences for accurate glioma assessment. Traditional deep learning methods typically utilize the individual MRI sequence with the annotations of Region of Interest (ROIs), incurring substantial manual effort while losing the complementary information provided by other sequences. This task is inherently a standard multi-modal problem. However, existing methods often adopt complex multi-stream networks for feature extraction and fusion, leading to high resource demands and potential feature redundancy. To address the above challenge, a discrepancy-aware self-distillation method is proposed for multi-modal glioma grading, which requires only a single-stream network to concurrently analyze multiple MRI sequences without auxiliary ROIs. The first Modality Discrepancy-aware Fusion (MDF) module considers the MRI imaging differentiation and widens the inter-modal contrasts, allowing the modality-specific features to be highlighted and the modality-invariant features to be diminished. Furthermore, the proposed Class Activation Self-Distillation (CASD) strategy leverages the generated Class Activation Maps (CAMs) as dark knowledge for the distillation process. This guides shallow layers to focus on the category discriminative features specifically within the lesion region. Extensive experiments were conducted on the BraTS2018 and BraTS2019 datasets to evaluate the effectiveness of our method. The results show that our method outperforms other relevant glioma grading and multi-modal fusion methods on both datasets.

A model-based MR parameter mapping network robust to substantial variations in acquisition settings

Deep learning methods show great potential for the efficient and precise estimation of quantitative parameter maps from multiple magnetic resonance (MR) images. Current deep learning-based MR parameter mapping (MPM) methods are mostly trained and tested using data with specific acquisition settings. However, scan protocols usually vary with centers, scanners, and studies in practice. Thus, deep learning methods applicable to MPM with varying acquisition settings are highly required but still rarely investigated. In this work, we develop a model-based deep network termed MMPM-Net for robust MPM with varying acquisition settings. A deep learning-based denoiser is introduced to construct the regularization term in the nonlinear inversion problem of MPM. The alternating direction method of multipliers is used to solve the optimization problem and then unrolled to construct MMPM-Net. The variation in acquisition parameters can be addressed by the data fidelity component in MMPM-Net. Extensive experiments are performed on R2 mapping and R1 mapping datasets with substantial variations in acquisition settings, and the results demonstrate that the proposed MMPM-Net method outperforms other state-of-the-art MR parameter mapping methods both qualitatively and quantitatively.

A Study of Hippocampal T2 Relaxometry Value in Temporal Lobe Epilepsy Patient

In the advancement of the current imaging technique for detection of mesial temporal sclerosis in temporal lobe epilepsy patient, there are multiple Magnetic resonance imaging (MRI) quantitative techniques. One of the long known to be valuable quantitative technique is T2 relaxometry where it has overall better accuracy for detection of mesial temporal sclerosis compared with the qualitative assessment only. A comparative cross-sectional study was conducted in Hospital Universiti Sains Malaysia from August 2021 to January 2023 which include 51 participants, 19 (36.5%) were temporal lobe epilepsy patient and 33 (63.5%) were control. The hippocampal T2 relaxometry was calculated for temporal lobe epilepsy patient and control. The mean T2 relaxometry reading for these groups were compared using Independent T-test. The correlation of T2 relaxometry value with age, duration of epilepsy and seizure frequency were analysed using Pearson correlation. Cohen Kappa inter-rater reliability was done for MR qualitative assessment. The participant age range was from 15 to 65 years old. Total of 23 samples were male (44.2%). The mean T2 relaxometry for epilepsy patient was higher than control. For correlation, moderate to good positive correlation was seen between the duration of epilepsy with T2 relaxometry of right hippocampus. Whereas poor positive correlation between the right hippocampus T2 relaxometry with age (P&gt;0.01), as well poor positive correlation between both hippocampus T2 relaxometry with seizure frequency (P&gt;0.01). There was one patient (5.2%) reported to be negative for mesial temporal sclerosis by qualitative assessment, but the T2 relaxometry reading however was raised. As a conclusion, T2 relaxometry sequence can be use as adjunct to the current qualitative assessment to increase sensitivity and specificity in detecting mesial temporal sclerosis.&#x0D; Keywords: Temporal lobe epilepsy, Mesial temporal sclerosis, T2 relaxometry, MRI epilepsy protocol

Sex differences in the pleiotropy of hearing difficulty with imaging-derived phenotypes: a brain-wide investigation.

Hearing difficulty (HD) is one of the major health burdens in older adults. While aging-related changes in the peripheral auditory system play an important role, genetic variation associated with brain structure and function could also be involved in HD predisposition. We analyzed a large-scale HD genome-wide association study (GWAS; Ntotal = 501,825, 56% females) and GWAS data related to 3,935 brain imaging-derived phenotypes (IDPs) assessed in up to 33,224 individuals (52% females) using multiple magnetic resonance imaging modalities. To investigate HD pleiotropy with brain structure and function, we conducted genetic correlation, latent causal variable, Mendelian randomization, and multivariable generalized linear regression analyses. Additionally, we performed local genetic correlation and multi-trait colocalization analyses to identify genomic regions and loci implicated in the pleiotropic mechanisms shared between HD and brain IDPs. We observed a widespread genetic correlation of HD with 120 IDPs in females, 89 IDPs in males, and 171 IDPs in the sex-combined analysis. The latent causal variable analysis showed that some of these genetic correlations could be due to cause-effect relationships. For seven correlations, the causal effects were also confirmed by the Mendelian randomization approach: vessel volume→HD in the sex-combined analysis; hippocampus volume→HD, cerebellum grey matter volume→HD, primary visual cortex volume→HD, and HD→fluctuation amplitudes of node 46 in resting-state functional MRI dimensionality 100 in females; global mean thickness→HD and HD→mean orientation dispersion index in superior corona radiata in males. The local genetic correlation analysis identified 13 pleiotropic regions between HD and these seven IDPs. We also observed a colocalization signal for the rs13026575 variant between HD, primary visual cortex volume, and SPTBN1 transcriptomic regulation in females. Brain structure and function may have a role in the sex differences in HD predisposition via possible cause-effect relationships and shared regulatory mechanisms.

Morphometric similarity differences in drug-naive Parkinson's disease correlate with transcriptomic signatures.

Differences in cortical morphology have been reported in individuals with Parkinson's disease (PD). However, the pathophysiological mechanism of transcriptomic vulnerability in local brain regions remains unclear. This study aimed to characterize the morphometric changes of brain regions in early drug-naive PD patients and uncover the brain-wide gene expression correlates. The morphometric similarity (MS) network analysis was used to quantify the interregional structural similarity from multiple magnetic resonance imaging anatomical indices measured in each brain region of 170 early drug-naive PD patients and 123 controls. Then, we applied partial least squares regression to determine the relationship between regional changes in MS and spatial transcriptional signatures from the Allen Human Brain Atlas dataset, and identified the specific genes related to MS differences in PD. We further investigated the biological processes by which the PD-related genes were enriched and the cellular characterization of these genes. Our results showed that MS was mainly decreased in cingulate, frontal, and temporal cortical areas and increased in parietal and occipital cortical areas in early drug-naive PD patients. In addition, genes whose expression patterns were associated with regional MS changes in PD were involved in astrocytes, excitatory, and inhibitory neurons and were functionally enriched in neuron-specific biological processes related to trans-synaptic signaling and nervous system development. These findings advance our understanding of the microscale genetic and cellular mechanisms driving macroscale morphological abnormalities in early drug-naive PD patients and provide potential targets for future therapeutic trials.

Dynamic changes in perivascular space morphology predict signs of spaceflight-associated neuro-ocular syndrome in bed rest

During long-duration spaceflight, astronauts experience headward fluid shifts and expansion of the cerebral perivascular spaces (PVS). A major limitation to our understanding of the changes in brain structure and physiology induced by spaceflight stems from the logistical difficulties of studying astronauts. The current study aimed to determine whether PVS changes also occur on Earth with the spaceflight analog head-down tilt bed rest (HDBR). We examined how the number and morphology of magnetic resonance imaging-visible PVS (MV-PVS) are affected by HDBR with and without elevated carbon dioxide (CO2). These environments mimic the headward fluid shifts, body unloading, and elevated CO2 observed aboard the International Space Station. Additionally, we sought to understand how changes in MV-PVS are associated with signs of Spaceflight Associated Neuro-ocular Syndrome (SANS), ocular structural alterations that can occur with spaceflight. Participants were separated into two bed rest campaigns: HDBR (60 days) and HDBR + CO2 (30 days with elevated ambient CO2). Both groups completed multiple magnetic resonance image acquisitions before, during, and post-bed rest. We found that at the group level, neither spaceflight analog affected MV-PVS quantity or morphology. However, when taking into account SANS status, persons exhibiting signs of SANS showed little or no MV-PVS changes, whereas their No-SANS counterparts showed MV-PVS morphological changes during the HDBR + CO2 campaign. These findings highlight spaceflight analogs as models for inducing changes in MV-PVS and implicate MV-PVS dynamic compliance as a mechanism underlying SANS. These findings may lead to countermeasures to mitigate health risks associated with human spaceflight.

Comparative evaluation of multiparametric lumbar MRI radiomic models for detecting osteoporosis

BackgroundOsteoporosis is a serious global public health issue. Currently, there are few studies that explore the use of multiparametric MRI radiomics for osteoporosis detection. The purpose of this study was to compare the performance of radiomics features from multiple MRI sequences (T1WI, T2WI and T1WI combined with T2WI) for detecting osteoporosis in patients.MethodsA retrospective analysis was performed on 160 patients who had undergone dual-energy X-ray absorptiometry(DXA) and lumbar magnetic resonance imaging (MRI) at our hospital. Among them, 86 patients were diagnosed with abnormal bone mass (osteoporosis or low bone mass), and 74 patients were diagnosed with normal bone mass based on the DXA results. Sagittal T1-and T2-weighted images of all patients were imported into the uAI Research Portal (United Imaging Intelligence) for image delineation and radiomics analysis, where a series of radiomic features were obtained. A radiomic model that included T1WI, T2WI, and T1WI+T2WI was established using features selected by LASSO regression. We used ROC curve analysis to evaluate the predictive efficacy of each model for identifying bone abnormalities and conducted decision curve analysis (DCA) to evaluate the net benefit of each model. Finally, we validated the model in a sample of 35 patients from different health care institution.ResultsThe T1WI + T2WI radiomics model showed better screening performance for patients with abnormal bone mass. In the training group, the sensitivity was 0.758, the specificity was 0.78, and the accuracy was 0.768 (AUC =0.839, 95% CI=0.757-0.901). In the validation group, the sensitivity was 0.792, the specificity was 0.875, and the accuracy was 0.833 (AUC =0.86, 95% CI=0.73-0.943).The DCA also showed that the combined model had better net benefits. In the external validation group, the sensitivity was 0.764, the specificity was 0.833, and the accuracy was 0.8 (AUC =0.824, 95% CI 0.678-0.969).ConclusionsRadiomics-based multiparametric MRI can be used for the quantitative analysis of lumbar MRI and for accurately screening patients with abnormal bone mass.

An MRI Compatible Data Acquisition Device for Rat Brain Recording Inside 16.4T Magnet.

Concurrent recording of neural activities and functional magnetic resonance imaging (fMRI) data is useful for studying the neurovascular coupling relationship. This article presents a low-noise, frequency-shaping based neural recorder chip that is insensitive to radio frequency (RF) pulses and gradient echo artifacts under strong magnetic environment. To support simultaneous recording of local field potentials (LFPs), extracellular spikes, and fMRI data, a magnetic resonance imaging (MRI) compatible data acquisition (DAQ) device based on the designed recorder chip is developed with multiple circuit optimization techniques. Bench-top measurement shows that the designed DAQ device has 4.5 μV input-referred noise integrated from 300 Hz to 3000 Hz, which is not greatly affected by electromagnetic interference (EMI) at ultrahigh magnetic field (UMF, 16.4 T). In animal experiments, the designed DAQ device has been demonstrated to be capable of acquiring both the LFPs and extracellular spikes from a rat's brain before, during, and after MRI scanning. Besides, no obvious artifacts are seen from the designed DAQ device at multiple typical MRI scanning modes, and the system recovery time after gradient artifacts is reduced from more than 25 ms to less than 5 ms. The proposed DAQ device architecture based on the frequency-shaping neural recorder chip is MRI compatible and can provide highly competitive performance for concurrent recording of neural activities and fMRI data.

Comprehensive review and update of stricturing Crohn's disease.

Up to 50% of patients with Crohn's disease develop a stricture within 10years of diagnosis. Crohn's strictures can compose of inflammation, fibrosis or smooth muscle expansion and usually a combination of these. There have been numerous new developments in imaging modalities in determining the composition of Crohn's strictures. Magnetic resonance imaging remains the best upfront imaging modality to characterize Crohn's strictures. Gastrointestinal ultrasound (GIUS) has an increasing role in clinical practice, particularly for monitoring stricture response as a treat-to-target tool. Novel imaging techniques to differentiate between fibrosis and inflammatory strictures have been developed including contrast-enhanced GIUS, strain or shear wave elastography with GIUS and multiple new magnetic resonance imaging (MRI) protocols, including diffusion weighted, delayed contrast enhancement and magnetization transfer MR protocols. However, these techniques require further validation and standardization. Regarding therapeutics, anti-tumor necrosis agents with a treat-to-target strategy have the highest quality evidence in treating strictures and can lead to stricture regression in some cases. Endoscopic balloon dilatation remains a mainstay in the treatment algorithm of treating predominantly fibrostenotic Crohn's strictures, particularly those which are symptomatic, < 5cm in length and not causing prestenotic dilatation. Endoscopic balloon dilatation has greater effectiveness in anastomotic strictures. Surgery remains an important treatment option in Crohn's strictures, with segmental resection and stricturoplasty having their own advantages and disadvantages.Kono-S anastomosis may be superior to conventional anastomosis for endoscopic recurrence; however, further high-quality studies are required to confirm this. Using risk stratification models such as the BACARDI risk model is important to guide management decisions between a medical and surgical approach. Early post-operative medical prophylaxis with an advanced therapy is an important consideration to prevent disease recurrence. This review expands on the above topics, highlights research gaps and provides a suggested investigation and management pathway in stricturing Crohn's disease.

Evaluation of the quality and the productivity of neuroradiological reading of multiple sclerosis follow-up MRI scans using an intelligent automation software

PurposeThe assessment of multiple sclerosis (MS) lesions on follow-up magnetic resonance imaging (MRI) is tedious, time-consuming, and error-prone. Automation of low-level tasks could enhance the radiologist in this work. We evaluate the intelligent automation software Jazz in a blinded three centers study, for the assessment of new, slowly expanding, and contrast-enhancing MS lesions.MethodsIn three separate centers, 117 MS follow-up MRIs were blindly analyzed on fluid attenuated inversion recovery (FLAIR), pre- and post-gadolinium T1-weighted images using Jazz by 2 neuroradiologists in each center. The reading time was recorded. The ground truth was defined in a second reading by side-by-side comparison of both reports from Jazz and the standard clinical report. The number of described new, slowly expanding, and contrast-enhancing lesions described with Jazz was compared to the lesions described in the standard clinical report.ResultsA total of 96 new lesions from 41 patients and 162 slowly expanding lesions (SELs) from 61 patients were described in the ground truth reading. A significantly larger number of new lesions were described using Jazz compared to the standard clinical report (63 versus 24). No SELs were reported in the standard clinical report, while 95 SELs were reported on average using Jazz. A total of 4 new contrast-enhancing lesions were found in all reports. The reading with Jazz was very time efficient, taking on average 2min33s ± 1min0s per case. Overall inter-reader agreement for new lesions between the readers using Jazz was moderate for new lesions (Cohen kappa = 0.5) and slight for SELs (0.08).ConclusionThe quality and the productivity of neuroradiological reading of MS follow-up MRI scans can be significantly improved using the dedicated software Jazz.

Multi-modal brain tumor segmentation via disentangled representation learning and region-aware contrastive learning

Brain tumors are threatening the life and health of people in the world. Automatic brain tumor segmentation using multiple MR images is challenging in medical image analysis. It is known that accurate segmentation relies on effective feature learning. Existing methods address the multi-modal MR brain tumor segmentation by explicitly learning a shared feature representation. However, these methods fail to capture the relationship between MR modalities and the feature correlation between different target tumor regions. In this paper, I propose a multi-modal brain tumor segmentation network via disentangled representation learning and region-aware contrastive learning. Specifically, a feature fusion module is first designed to learn the valuable multi-modal feature representation. Subsequently, a novel disentangled representation learning is proposed to decouple the fused feature representation into multiple factors corresponding to the target tumor regions. Furthermore, contrastive learning is presented to help the network extract tumor region-related feature representations. Finally, the segmentation results are obtained using the segmentation decoders. Quantitative and qualitative experiments conducted on the public datasets, BraTS 2018 and BraTS 2019, justify the importance of the proposed strategies, and the proposed approach can achieve better performance than other state-of-the-art approaches. In addition, the proposed strategies can be extended to other deep neural networks.

Quantitative evaluation of natural progression of fatty infiltration and muscle atrophy in chronic rotator cuff tears without tear extension using magnetic resonance imaging

BackgroundThe pathology of and mechanisms underlying muscle degeneration remain unclear. We aimed to quantitatively evaluate the natural changes in fatty infiltration and muscle atrophy in patients with chronic rotator cuff tears using 3-dimensional 2-point Dixon magnetic resonance imaging (MRI). MethodsThirty patients with nonoperatively observed rotator cuff tears without tear extension were evaluated using multiple MRI examinations with a minimum interval of 2 years. The fatty infiltration ratio (%fat) and muscle volume of the rotator cuff muscles were compared between the two examinations in those with supraspinatus tear <2 cm (<2 cm SSP group), supraspinatus tear ≥2 cm (≥2 cm SSP group), and massive tear (massive group). The supraspinatus (SSP), infraspinatus and teres minor (ISP+TM), and subscapularis (SSC) muscles were evaluated. ResultsThe massive group showed a significantly greater %fat than the <2 and ≥2 cm SSP groups in the SSP (P=0.002) and ISP+TM muscles (P<0.001). The total muscle volume did not differ among the three groups for all rotator cuff muscle components. The %fat values did not change in any rotator cuff components during the follow-up period in all groups. The total muscle volume in the massive group significantly decreased in the SSP (P=0.018) and ISP+TM muscles (P=0.013). ConclusionThe present results indicate that fatty infiltration of the torn muscle occurs in the early phase after a rotator cuff tear, whereas muscle atrophy appears to progress gradually in chronic rotator cuff tears. Early intervention before muscle degeneration should be considered if the tear involves the infraspinatus tendon.

Static and dynamic brain morphological changes in isolated REM sleep behavior disorder compared to normal aging.

To assess whether cerebral structural alterations in isolated rapid eye movement sleep behavior disorder (iRBD) are progressive and differ from those of normal aging and whether they are related to clinical symptoms. In a longitudinal study of 18 patients with iRBD (age, 66.1 ± 5.7 years; 13 males; follow-up, 1.6 ± 0.6 years) and 24 age-matched healthy controls (age, 67.0 ± 4.9 years; 12 males; follow-up, 2.0 ± 0.9 years), all participants underwent multiple extensive clinical examinations, neuropsychological tests, and magnetic resonance imaging at baseline and follow-up. Surface-based cortical reconstruction and automated subcortical structural segmentation were performed on T1-weighted images. We used mixed-effects models to examine the differences between the groups and the differences in anatomical changes over time. None of the patients with iRBD demonstrated phenoconversion during the follow-up. Patients with iRBD had thinner cortices in the frontal, occipital, and temporal regions, and more caudate atrophy, compared to that in controls. In similar regions, group-by-age interaction analysis revealed that patients with iRBD demonstrated significantly slower decreases in cortical thickness and caudate volume with aging than that observed in controls. Patients with iRBD had lower scores on the Korean version of the Mini-Mental Status Examination (p = 0.037) and frontal and executive functions (p = 0.049) at baseline than those in controls; however, no significant group-by-age interaction was identified. Patients with iRBD show brain atrophy in the regions that are overlapped with the areas that have been documented to be affected in early stages of Parkinson's disease. Such atrophy in iRBD may not be progressive but may be slower than that in normal aging. Cognitive impairment in iRBD is not progressive.

Behavioral disinhibition in stroke.

Post-stroke behavioral disinhibition (PSBD) is common in stroke survivors and often presents as impulsive, tactless or vulgar behavior. However, it often remains undiagnosed and thus untreated, even though it can lead to a longer length of stay in a rehabilitation facility. The proposed study will aim to evaluate the clinical, neuropsychological and magnetic resonance imaging (MRI) correlates of PSBD in a cohort of stroke survivors and describe its 12-month course. This prospective cohort study will recruit 237 patients and will be conducted at the Neurology Unit of the Prince of Wales Hospital. The project duration will be 24 months. The patients will be examined by multiple MRI methods, including diffusion-weighted imaging, within 1 week after stroke onset. The patients and their caregivers will receive a detailed assessment at a research clinic at 3, 9 and 15 months after stroke onset (T1, T2 and T3, respectively). The disinhibition subscale of the Frontal Systems Behavior Scale (FrSBe) will be completed by each subject and caregiver, and scores ≥65 will be considered to indicate PSBD.A stepwise logistic regression will be performed to assess the importance of lesions in the regions of interest (ROIs), together with other significant variables identified in the univariate analyses. For patients with PSBD at T1, the FrSBe disinhibition scores will be compared between the groups of patients with and without ROI infarcts, using covariance analysis. The demographic, clinical and MRI variables of remitters and non-remitters will be examined again at T2 and T3 by logistic regression. This project will be the first MRI study on PSBD in stroke survivors. The results will shed light on the associations of lesions in the orbitofrontal cortex, anterior temporal lobe and subcortical brain structures with the risk of PSBD. The obtained data will advance our understanding of the pathogenesis and clinical course of PSBD in stroke, as well as other neurological conditions. The findings are thus likely to be applicable to the large population of patients with neurological disorders at risk of PSBD and are expected to stimulate further research in this field.

Cancer Radiomic and Perfusion Imaging Automated Framework: Validation on Musculoskeletal Tumors.

Limitations from commercial software applications prevent the implementation of a robust and cost-efficient high-throughput cancer imaging radiomic feature extraction and perfusion analysis workflow. This study aimed to develop and validate a cancer research computational solution using open-source software for vendor- and sequence-neutral high-throughput image processing and feature extraction. The Cancer Radiomic and Perfusion Imaging (CARPI) automated framework is a Python-based software application that is vendor- and sequence-neutral. CARPI uses contour files generated using an application of the user's choice and performs automated radiomic feature extraction and perfusion analysis. This workflow solution was validated using two clinical data sets, one consisted of 40 pelvic chondrosarcomas and 42 sacral chordomas with a total of 82 patients, and a second data set consisted of 26 patients with undifferentiated pleomorphic sarcoma (UPS) imaged at multiple points during presurgical treatment. Three hundred sixteen volumetric contour files were processed using CARPI. The application automatically extracted 107 radiomic features from multiple magnetic resonance imaging sequences and seven semiquantitative perfusion parameters from time-intensity curves. Statistically significant differences (P < .00047) were found in 18 of 107 radiomic features in chordoma versus chondrosarcoma, including six first-order and 12 high-order features. In UPS postradiation, the apparent diffusion coefficient mean increased 41% in good responders (P = .0017), while firstorder_10Percentile (P = .0312) was statistically significant between good and partial/nonresponders. The CARPI processing of two clinical validation data sets confirmed the software application's ability to differentiate between different types of tumors and help predict patient response to treatment on the basis of radiomic features. Benchmark comparison with five similar open-source solutions demonstrated the advantages of CARPI in the automated perfusion feature extraction, relational database generation, and graphic report export features, although lacking a user-friendly graphical user interface and predictive model building.

Enhanced Brain Tumor Segmentation and Size Estimation in MRI Samples using Hybrid Optimization

The area of medical imaging specialization, specifically in the context of brain tumor segmentation, has long been challenged by the inherent complexity and variability of brain structures. Traditional segmentation methods often struggle to accurately differentiate between the diverse types of tissues within the brain, such as white matter, grey matter, and cerebrospinal fluid, leading to suboptimal results in tumor identification and delineation. These limitations necessitate the development of more advanced and precise segmentation techniques to enhance diagnostic accuracy and treatment planning. In response to these challenges, the proposed study introduces a novel segmentation approach that combines the Grey Wolf Optimization approach and the Cuckoo Search approach within a Fuzzy C-Means (FCM) framework. The integration of GWO and CS is designed to leverage their respective strengths in optimizing the segmentation of brain tissues. This hybrid approach was rigorously tested across multiple Magnetic Resonance Imaging (MRI) datasets, demonstrating significant enhancements over existing segmentation methods. The study observed a 4,9 % improvement in accuracy, 3,5 % increase in precision, 4,5 % higher recall, 3,2 % less delay, and 2,5 % better specificity in tumor segmentation. The implications of these advancements are profound. By achieving higher precision and accuracy in brain tumor segmentation, the proposed method can substantially aid in early diagnosis and accurate staging of brain tumors, eventually leading to more effective treatment planning and improved patient outcomes. Furthermore, the integration of GWO and CS within the FCM process sets a new benchmark in medical imaging, paving the way for future investigation in the field of study