Advanced Magnetic Resonance Imaging Research Articles

Potential Advantage of Magnetic Resonance Imaging in Detecting Thoracic Wall Infiltration in Pleural Mesothelioma. A Retrospective Single Center Analysis.

Abstract Background Thoracic wall infiltration in pleural mesothelioma (PM) can be an important prognostic factor when determining the extent of resection. Currently, standardized imaging for restaging after neoadjuvant systemic therapy comprises contrast enhanced Computed Tomography (CT) or Positron Emission Tomography (PET/CT) scan. Aims However, thoracic Magnetic Resonance Imaging (MRI) could be a better discriminator of chest wall infiltration prior to surgery. To increase the preoperative staging accuracy, we introduced a MRI protocol at our center. Methods A retrospective analysis of this prospective new imaging protocol was performed from 07/2018 to 08/2023, including descriptive analysis for patient`s sex, age, nicotine consumption, asbestos exposure, histological subtype, TNM-stage, RECIST criteria and number of neoadjuvant therapy cycles. Preoperative restaging included routine imaging and MRI. After histological diagnosis of PM, neoadjuvant therapy was conducted, followed by partial pleurectomy or extended pleurectomy/decortication, with intraoperative biopsies of suspicious chest wall lesions. The CT/MRI results were compared to the intraoperative biopsies. Sensitivity and specificity of both modalities were analyzed. Results Twenty-five patients (mean age 65.4, 12% female) with possible chest wall infiltration were included out of the 35 patients with PM treated during the observation period. Of the 10 patients with actual chest wall infiltration, 9 (90%) had a T-Stage of 3 or higher, 9 (90%) had PM of epithelioid histologic subtype and 4 (40%) a R2-Resection. The mean overall survival of all patients was 18.88 months (8-58). In our study, thoracic MRI showed a high sensitivity (90%) and specificity (100%) for the detection of chest wall infiltration, especially when compared to the CT scan (sensitivity of 10%). Conclusion With the adjunctive use of thoracic MRI we demonstrated a higher sensitivity for detection of chest wall infiltration compared to conventional imaging prior to surgery. This may facilitate the preoperative assessment of the extent of resection. Nevertheless, larger studies are required to confirm these results.

Benefits of Modern Imaging Techniques in Medicine

Relevance. For more than 100 years, medical practice has used imaging methods to observe pathological changes localized in the human body. The first method was radiography, which became popular among doctors, but later it turned out that high radiation exposure increases the incidence of tumor diseases, which is why standards for permissible radiation doses were developed for patients and radiologists. The introduction of radiocontrast agents also had contraindications that should be taken into account. Ultrasound, computed tomography (CT), magnetic resonance imaging (MRI), and angiography methods have significantly increased the diagnostic capabilities of instrumental methods. The purpose of this review is to analyze the advantages and disadvantages of modern imaging methods in medicine. Methods. To achieve the purpose of the review, a literature search was conducted in Google Scholar and PubMed databases. Results. 1) The main indication for the X-ray method is visualization of bone structures. 2) X-ray contrast methods are optimal for studying hollow organs and blood vessels. 3) The advantage of MRI is the ability to visualize soft tissue well. 4) CT helps to reconstruct the spatial architecture of organs. 5) Ultrasound allows you to observe the condition of the embryo and fetus, as well as internal organs.

Scan With Me (SWiM): A train-the-trainer program to upskill MRI personnel in low- and middle- income countries.

PurposeAccess to magnetic resonance imaging (MRI) in low-and-middle-income countries (LMICs) remains among the poorest in the world. The lack of skilled MRI personnel exacerbates access gaps, reinforcing longstanding health disparities. The SWiM Program aims to sustainably create a network of highly skilled MRI technologists in LMICs who will facilitate the transfer of MRI knowledge and skills to their peers and contribute to implementation of highly valuable imaging protocols for impactful clinical and research use. MethodsThe program introduces a case-based curriculum designed using a novel train-the-trainer approach, integrated with peer collaborative learning to upskill practicing MRI technologists in LMICs. The six-week curriculum uses the Teach-Try-Use approach, which combines self-paced didactic lectures covering the basics of MR image acquisition (teach) with hands-on expert-guided scanning experience (try), and implementation of protocols tailored to provide the best possible images on their infrastructures (use). Each program includes research translation skills training using an established advanced MRI technique relevant to LMICs. A pilot program focused on cardiac MRI (CMR) was conducted to assess the program’s curriculum, delivery, and evaluation methods. ResultsForty-three MRI technologists from 16 LMICs participated in the pilot CMR program and over the course of the training, implemented optimized CMR protocols that reduced acquisition times while improving image quality. The training resources and scanner-specific standardized protocols are published openly for public use on an online repository. In general, at the end of the program, learners reported considerable improvements in CMR knowledge and skills. All respondents to the program evaluation survey agreed to recommend the program to their colleagues, while 87% indicated interest in returning to help train others. DiscussionThe SWiM Program is the first masterclass in MRI acquisition for practicing imaging technologists in LMICS. The program holds the potential to help reduce disparities in MRI expertise and access. The support of the MRI community, imaging societies, and funding agencies will increase its reach and further its impact in democratizing MRI.

Advanced neuroimaging techniques to explore the effects of motor and cognitive rehabilitation in multiple sclerosis.

Progress in magnetic resonance imaging (MRI) technology and analyses is improving our comprehension of multiple sclerosis (MS) pathophysiology. These advancements, which enable the evaluation of atrophy, microstructural tissue abnormalities, and functional plasticity, are broadening our insights into the effectiveness and working mechanisms of motor and cognitive rehabilitative treatments. This narrative review with selected studies discusses findings derived from the application of advanced MRI techniques to evaluate structural and functional neuroplasticity modifications underlying the effects of motor and cognitive rehabilitative treatments in people with MS (PwMS). Current applications as outcome measure in longitudinal trials and observational studies, their interpretation and possible pitfalls and limitations in their use are covered. Finally, we examine how the use of these techniques could evolve in the future to improve monitoring of motor and cognitive rehabilitative treatments. Despite substantial variability in study design and participant characteristics in rehabilitative studies for PwMS, improvements in motor and cognitive functions accompanied by structural and functional brain modifications induced by rehabilitation can be observed. However, significant enhancements to refine rehabilitation strategies are needed. Future studies in this field should strive to implement standardized methodologies regarding MRI acquisition and processing, possibly integrating multimodal measures. This will help identifying relevant markers of treatment response in PwMS, thus improving the use of rehabilitative interventions at individual level. The combination of motor and cognitive strategies, longer periods of treatment, as well as adequate follow-up assessments will contribute to enhance the quality of evidence in support of their routine use.

Concurrent inflammation-related brain reorganization in multiple sclerosis and depression

BackgroundNeuroinflammation affects brain tissue integrity in multiple sclerosis (MS) and may have a role in major depressive disorder (MDD). Whether advanced magnetic resonance imaging characteristics of the gray-to-white matter border serve as proxy of neuroinflammatory activity in MDD and MS remain unknown. MethodsWe included 684 participants (132 MDD patients with recurrent depressive episodes (RDE), 70 MDD patients with a single depressive episode (SDE), 222 MS patients without depressive symptoms (nMS), 58 MS patients with depressive symptoms (dMS), and 202 healthy controls (HC)). 3 T-T1w MRI-derived gray-to-white matter contrast (GWc) was used to reconstruct and characterize connectivity alterations of GWc-covariance networks by means of modularity, clustering coefficient, and degree. A cross-validated support vector machine was used to test the ability of GWc to stratify groups according to their depression symptoms, measured with BDI, at the single-subject level in MS and MDD independently. FindingsMS and MDD patients showed increased modularity (ANOVA partial-η2 = 0.3) and clustering (partial-η2 = 0.1) compared to HC. In the subgroups, a linear trend analysis attested a gradient of modularity increases in the form: HC, dMS, nMS, SDE, and RDE (ANOVA partial-η2 = 0.28, p < 0.001) while this trend was less evident for clustering coefficient. Reduced morphological integrity (GWc) was seen in patients with increased depressive symptoms (partial-η2 = 0.42, P < 0.001) and was associated with depression scores across patient groups (r = -0.2, P < 0.001). Depressive symptoms in MS were robustly classified (88 %). ConclusionsSimilar structural network alterations in MDD and MS exist, suggesting possible common inflammatory events like demyelination, neuroinflammation that are caught by GWc analyses. These alterations may vary depending on the severity of symptoms and in the case of MS may elucidate the occurrence of comorbid depression.

Revolutionizing the Pancreatic Tumor Diagnosis: Emerging Trends in Imaging Technologies: A Systematic Review

Background and Objectives: The pancreas, ensconced within the abdominal cavity, requires a plethora of sophisticated imaging modalities for its comprehensive evaluation, with ultrasonography serving as a primary investigative technique. A myriad of pancreatic pathologies, encompassing pancreatic neoplasia and a spectrum of inflammatory diseases, are detectable through these imaging strategies. Nevertheless, the intricate anatomical confluence and the pancreas’s deep-seated topography render the visualization and accurate diagnosis of its pathologies a formidable endeavor. The objective of our paper is to review the best diagnostic imagistic tools for the pancreas. Materials and Methods: we have gathered several articles using Prisma guidelines to determine the best imagistic methods. The imperative of pancreatic scanning transcends its diagnostic utility, proving to be a pivotal element in a multitude of clinical specialties, notably surgical oncology. Within this domain, multidetector computed tomography (MDCT) of the pancreas holds the distinction of being the paramount imaging modality, endorsed for its unrivaled capacity to delineate the staging and progression of pancreatic carcinoma. In synergy with MDCT, there has been a notable advent of avant-garde imaging techniques in recent years. These advanced methodologies, including ultrasonography, endoscopic ultrasonography, contrast-enhanced ultrasonography, and magnetic resonance imaging (MRI) conjoined with magnetic resonance cholangiopancreatography (MRCP), have broadened the horizon of tumor characterization, offering unparalleled depth and precision in oncological assessment. Other emerging diagnostic techniques, such as elastography, also hold a lot of potential and promise for the future of pancreatic imaging. Fine needle aspiration (FNA) is a quick, minimally invasive procedure to evaluate lumps using a thin needle to extract tissue for analysis. It is less invasive than surgical biopsies and usually performed as an outpatient with quick recovery. Its accuracy depends on sample quality, and the risks include minimal bleeding or discomfort. Results, guiding further treatment, are typically available within a week. Elastography is a non-invasive medical imaging technique that maps the elastic properties and stiffness of soft tissue. This method, often used in conjunction with ultrasound or MRI, helps differentiate between hard and soft areas in tissue, providing valuable diagnostic information. It is particularly useful for assessing liver fibrosis, thyroid nodules, breast lumps, and musculoskeletal conditions. The technique is painless and involves applying gentle pressure to the area being examined. The resulting images show tissue stiffness, indicating potential abnormalities. Elastography is advantageous for its ability to detect diseases in early stages and monitor treatment effectiveness. The procedure is quick, safe, and requires no special preparation, with results typically available immediately. Results: The assembled and gathered data shows the efficacy of various techniques in discerning the nature and extent of neoplastic lesions within the pancreas. Conclusions: The most common imaging modalities currently used in diagnosing pancreatic neoplasms are multidetector computed tomography (MDCT), endoscopic ultrasound (EUS), and magnetic resonance imaging (MRI), alongside new technologies, such as elastography.

DF-QSM: Data Fidelity based Hybrid Approach for Improved Quantitative Susceptibility Mapping of the Brain.

Quantitative Susceptibility Mapping (QSM) is an advanced magnetic resonance imaging (MRI) technique to quantify the magnetic susceptibility of the tissue under investigation. Deep learning methods have shown promising results in deconvolving the susceptibility distribution from the measured local field obtained from the MR phase. Although existing deep learning based QSM methods can produce high-quality reconstruction, they are highly biased toward training data distribution with less scope for generalizability. This work proposes a hybrid two-step reconstruction approach to improve deep learning based QSM reconstruction. The susceptibility map prediction obtained from the deep learning methods has been refined in the framework developed in this work to ensure consistency with the measured local field. The developed method was validated on existing deep learning and model-based deep learning methods for susceptibility mapping of the brain. The developed method resulted in improved reconstruction for MRI volumes obtained with different acquisition settings, including deep learning models trained on constrained (limited) data settings.

Peak Resembling N-acetylaspartate (NAA) on Magnetic Resonance Spectroscopy of Brain Metastases.

Background and Objectives: Differentiating between a high-grade glioma (HGG) and solitary cerebral metastasis presents a challenge when using standard magnetic resonance imaging (MRI) alone. Magnetic resonance spectroscopy (MRS), an advanced MRI technique, may assist in resolving this diagnostic dilemma. N-acetylaspartate (NAA), an amino acid found uniquely in the central nervous system and in high concentrations in neurons, typically suggests HGG over metastatic lesions in spectra from ring-enhancing lesions. This study investigates exceptions to this norm. Materials and Methods: We conducted an MRS study on 49 histologically confirmed and previously untreated patients with brain metastases, employing single-voxel (SVS) techniques with short and long echo times, as well as magnetic resonance spectroscopic imaging (MRSI). Results: In our cohort, 44 out of 49 (90%) patients demonstrated a typical MR spectroscopic profile consistent with secondary deposits: a Cho peak, very low or absent Cr, absence of NAA, and the presence of lipids. A peak at approximately 2 ppm, termed the "NAA-like peak", was present in spectra obtained with both short and long echo times. Among the MRS data from 49 individuals, we observed a peak at 2.0 ppm in five brain metastases from mucinous carcinoma of the breast, mucinous non-small-cell lung adenocarcinoma, two metastatic melanomas, and one metastatic non-small-cell lung cancer. Pathohistological verification of mucin in two of these five cases suggested this peak likely represents N-acetyl glycoproteins, indicative of mucin expression in cancer cells. Conclusions: The identification of a prominent peak at 2.0 ppm could be a valuable diagnostic marker for distinguishing single ring-enhancing lesions, potentially associated with mucin-expressing metastases, offering a new avenue for diagnostic specificity in challenging cases.

Exploring neurodegenerative disorders using advanced magnetic resonance imaging of the glymphatic system.

The glymphatic system, a macroscopic waste clearance system in the brain, is crucial for maintaining neural health. It facilitates the exchange of cerebrospinal and interstitial fluid, aiding the clearance of soluble proteins and metabolites and distributing essential nutrients and signaling molecules. Emerging evidence suggests a link between glymphatic dysfunction and the pathogenesis of neurodegenerative disorders, including Alzheimer's, Parkinson's, and Huntington's disease. These disorders are characterized by the accumulation and propagation of misfolded or mutant proteins, a process in which the glymphatic system is likely involved. Impaired glymphatic clearance could lead to the buildup of these toxic proteins, contributing to neurodegeneration. Understanding the glymphatic system's role in these disorders could provide insights into their pathophysiology and pave the way for new therapeutic strategies. Pharmacological enhancement of glymphatic clearance could reduce the burden of toxic proteins and slow disease progression. Neuroimaging techniques, particularly MRI-based methods, have emerged as promising tools for studying the glymphatic system in vivo. These techniques allow for the visualization of glymphatic flow, providing insights into its function under healthy and pathological conditions. This narrative review highlights current MRI-based methodologies, such as motion-sensitizing pulsed field gradient (PFG) based methods, as well as dynamic gadolinium-based and glucose-enhanced methodologies currently used in the study of neurodegenerative disorders.

Advanced MRI metrics improve the prediction of baseline disease severity for individuals with degenerative cervical myelopathy

BACKGROUND CONTEXTDegenerative cervical myelopathy (DCM) is the most common form of atraumatic spinal cord injury globally. Degeneration of spinal discs, bony osteophyte growth and ligament pathology results in physical compression of the spinal cord contributing to damage of white matter tracts and grey matter cellular populations. This results in an insidious neurological and functional decline in patients which can lead to paralysis. Magnetic resonance imaging (MRI) confirms the diagnosis of DCM and is a prerequisite to surgical intervention, the only known treatment for this disorder. Unfortunately, there is a weak correlation between features of current commonly acquired MRI scans (“community MRI, cMRI”) and the degree of disability experienced by a patient. PURPOSEThis study examines the predictive ability of current MRI sequences relative to “advanced MRI” (aMRI) metrics designed to detect evidence of spinal cord injury secondary to degenerative myelopathy. We hypothesize that the utilization of higher fidelity aMRI scans will increase the effectiveness of machine learning models predicting DCM severity and may ultimately lead to a more efficient protocol for identifying patients in need of surgical intervention. STUDY DESIGN/SETTINGSingle institution analysis of imaging registry of patients with DCM. PATIENT SAMPLEA total of 296 patients in the cMRI group and 228 patients in the aMRI group. OUTCOME MEASURESPhysiologic measures: accuracy of machine learning algorithms to detect severity of DCM assessed clinically based on the modified Japanese Orthopedic Association (mJOA) scale. METHODSPatients enrolled in the Canadian Spine Outcomes Research Network registry with DCM were screened and 296 cervical spine MRIs acquired in cMRI were compared with 228 aMRI acquisitions. aMRI acquisitions consisted of diffusion tensor imaging, magnetization transfer, T2-weighted, and T2*-weighted images. The cMRI group consisted of only T2-weighted MRI scans. Various machine learning models were applied to both MRI groups to assess accuracy of prediction of baseline disease severity assessed clinically using the mJOA scale for cervical myelopathy. RESULTSThrough the utilization of Random Forest Classifiers, disease severity was predicted with 41.8% accuracy in cMRI scans and 73.3% in the aMRI scans. Across different predictive model variations tested, the aMRI scans consistently produced higher prediction accuracies compared to the cMRI counterparts. CONCLUSIONSaMRI metrics perform better in machine learning models at predicting disease severity of patients with DCM. Continued work is needed to refine these models and address DCM severity class imbalance concerns, ultimately improving model confidence for clinical implementation.

White Matter Alterations in Military Service Members With Remote Mild Traumatic Brain Injury

Mild traumatic brain injury (mTBI) is the signature injury experienced by military service members and is associated with poor neuropsychiatric outcomes. Yet, there is a lack of reliable clinical tools for mTBI diagnosis and prognosis. To examine the white matter microstructure and neuropsychiatric outcomes of service members with a remote history of mTBI (ie, mTBI that occurred over 2 years ago) using diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI). This case-control study examined 98 male service members enrolled in a study at the National Intrepid Center of Excellence. Eligible participants were active duty status or able to enroll in the Defense Enrollment Eligibility Reporting system, ages 18 to 60 years, and had a remote history of mTBI; controls were matched by age. Remote history of mTBI. White matter microstructure was assessed using a region-of-interest approach of skeletonized diffusion images, including DTI (fractional anisotropy, mean diffusivity, radial diffusivity and axial diffusivity) and NODDI (orientation dispersion index [ODI], isotropic volume fraction, intra-cellular volume fraction). Neuropsychiatric outcomes associated with posttraumatic stress disorder (PTSD) and postconcussion syndrome were assessed. A total of 65 male patients with a remote history of mTBI (mean [SD] age, 40.5 [5.0] years) and 33 age-matched male controls (mean [SD] age, 38.9 [5.6] years) were included in analysis. Compared with the control cohort, the 65 service members with mTBI presented with significantly more severe PTSD-like symptoms (mean [SD] PTSD CheckList-Civilian [PCL-C] version scores: control, 19.0 [3.8] vs mTBI, 41.2 [11.6]; P < .001). DTI and NODDI metrics were altered in the mTBI group compared with the control, including intra-cellular volume fraction of the right cortico-spinal tract (β = -0.029, Cohen d = 0.66; P < .001), ODI of the left posterior thalamic radiation (β = -0.006, Cohen d = 0.55; P < .001), and ODI of the left uncinate fasciculus (β = 0.013, Cohen d = 0.61; P < .001). In service members with mTBI, fractional anisotropy of the left uncinate fasciculus was associated with postconcussion syndrome (β = 5.4 × 10-3; P = .003), isotropic volume fraction of the genu of the corpus callosum with PCL-C (β = 4.3 × 10-4; P = .01), and ODI of the left fornix and stria terminalis with PCL-C avoidance scores (β = 1.2 × 10-3; P = .02). In this case-control study of military-related mTBI, the results suggest that advanced magnetic resonance imaging techniques using NODDI can reveal white matter microstructural alterations associated with neuropsychiatric symptoms in the chronic phase of mTBI. Diffusion trends observed throughout widespread white matter regions-of-interest may reflect mechanisms of neurodegeneration as well as postinjury tissue scarring and reorganization.

Stroke-Induced Secondary Neurodegeneration of the Corticospinal Tract-Time Course and Mechanisms Underlying Signal Changes in Conventional and Advanced Magnetic Resonance Imaging.

Secondary neurodegeneration refers to the final result of several simultaneous and sequential mechanisms leading to the loss of substance and function in brain regions connected to the site of a primary injury. Stroke is one of the most frequent primary injuries. Among the subtypes of post-stroke secondary neurodegeneration, axonal degeneration of the corticospinal tract, also known as Wallerian degeneration, is the most known, and it directly impacts motor functions, which is crucial for the motor outcome. The timing of its appearance in imaging studies is usually considered late (over 4 weeks), but some diffusion-based magnetic resonance imaging (MRI) techniques, as diffusion tensor imaging (DTI), might show alterations as early as within 7 days from the stroke. The different sequential pathological stages of secondary neurodegeneration provide an interpretation of the signal changes seen by MRI in accordance with the underlying mechanisms of axonal necrosis and repair. Depending on the employed MRI technique and on the timing of imaging, different rates and thresholds of Wallerian degeneration have been provided in the literature. In fact, three main pathological stages of Wallerian degeneration are recognizable-acute, subacute and chronic-and MRI might show different changes: respectively, hyperintensity on T2-weighted sequences with corresponding diffusion restriction (14-20 days after the injury), followed by transient hypointensity of the tract on T2-weighted sequences, and by hyperintensity and atrophy of the tract on T2-weighted sequences. This is the main reason why this review is focused on MRI signal changes underlying Wallerian degeneration. The identification of secondary neurodegeneration, and in particular Wallerian degeneration, has been proposed as a prognostic indicator for motor outcome after stroke. In this review, the main mechanisms and neuroimaging features of Wallerian degeneration in adults are addressed, focusing on the time and mechanisms of tissue damage underlying the signal changes in MRI.

Machine Learning Approaches to Identify Affected Brain Regions in Movement Disorders Using MRI Data: A Systematic Review and Diagnostic Meta-analysis.

Movement disorders such as Parkinson's disease are associated with structural and functional changes in specific brain regions. Advanced magnetic resonance imaging (MRI) techniques combined with machine learning (ML) are promising tools for identifying imaging biomarkers and patterns associated with these disorders. We aimed to systematically identify the brain regions most commonly affected in movement disorders using ML approaches applied to structural and functional MRI data. We searched the PubMed and Scopus databases using relevant keywords up to June 2023 for studies that used ML approaches to detect brain regions associated with movement disorders using MRI data. A systematic review and diagnostic meta-analysis. Sixty-seven studies with 6,285 patients were included. Studies utilizing 1.5T or 3T MR scanners and the acquisition of diffusion tensor imaging (DTI), structural MRI (sMRI), functional MRI (fMRI), or a combination of these were included. The authors independently assessed the study quality using the CLAIM and QUADAS-2 criteria and extracted data on diagnostic accuracy measures. Sensitivity, specificity, accuracy, and area under the curve were pooled using random-effects models. Q statistics and the I2 index were used to evaluate heterogeneity, and Begg's funnel plot was used to identify publication bias. sMRI showed the highest sensitivity (93%) and mixed modalities had the highest specificity (90%) for detecting regional abnormalities. sMRI had a 94% sensitivity for identifying subcortical changes. The support vector machine (93%) and logistic regression (91%) models exhibited high diagnostic accuracies. The combination of advanced MR neuroimaging techniques and ML is a promising approach for identifying brain biomarkers and affected regions in movement disorders with subcortical structures frequently implicated. Structural MRI, in particular, showed strong performance. 1 TECHNICAL EFFICACY: Stage 2.

Identification of Severe Grading in Knee OsteoArthritis from MRI using Ensemble Deep Learning.

Accurate finding of Knee Osteoarthritis (KOA) from structural Magnetic Resonance Imaging (MRI) is a difficult task and is greatly subject to user variation. Furthermore, the identification of knee osteoarthritis (KOA) from MRI scans presents a challenge due to the limited information available. A novel methodology using an ensemble Deep Learning algorithm, combining EfficientNet-B3 and ResNext-101 architectures, aims to forecast KOA advancement, bridging the identified gap in clinical trials. The study aims to develop a precise predictive model for knee osteoarthritis using advanced deep-learning architectures and structural MRI scan data. By utilizing an ensemble technique, the model's accuracy in predicting disease development is enhanced, surpassing the limitations of traditional biomarkers. The study used the Osteoarthritis Initiative dataset to develop an ensemble Deep Learning model that combined EfficientNet-B3 and ResNext-101 architectures. Techniques like cropping, gamma correction, and in-slice rotation were used to expand the dataset and improve the model's generalization capacity. The Deep Learning model demonstrated 93% validation accuracy on the OAI dataset, accurately capturing subtle patterns of knee osteoarthritis progression. Augmentation approaches enhanced its resilience. Our ensemble Deep Learning approach, using ResNext-101 and EfficientNet-B3 architectures, accurately predicts knee osteoarthritis courses using structural MRI data, demonstrating the importance of data augmentation for improved predictive tools.

Navigating Neural Landscapes: A Comprehensive Review of Magnetic Resonance Imaging (MRI) and Magnetic Resonance Spectroscopy (MRS) Applications in Epilepsy.

This review comprehensively explores the evolving role of neuroimaging, specifically magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS), in epilepsy research and clinical practice. Beginning with a concise overview of epilepsy, the discussion emphasizes the crucial importance of neuroimaging in diagnosing and managing this complex neurological disorder. The review delves into the applications of advanced MRI techniques, including high-field MRI, resting-state fMRI, and connectomics, highlighting their impact on refining our understanding of epilepsy's structural and functional dimensions. Additionally, it examines the integration of machine learning in the analysis of intricate neuroimaging data. Moving to the clinical domain, the review outlines the utility of neuroimaging in pre-surgical evaluations and the monitoring of treatment responses and disease progression. Despite significant strides, challenges and limitations are discussed in the routine clinical incorporation of neuroimaging. The review explores promising developments in MRI and MRS technology, potential advancements in imaging biomarkers, and the implications for personalized medicine in epilepsy management. The conclusion underscores the transformative potential of neuroimaging and advocates for continued exploration, collaboration, and technological innovation to propel the field toward a future where tailored, effective interventions improve outcomes for individuals with epilepsy.

Simulation of translational motion correction during cartesian brain MRI

Brain Magnetic Resonance Imaging (MRI) is invaluable for non-invasively capturing detailed anatomical and functional information. However, motion artifacts, particularly during brain imaging, can compromise the precision of scans. This study explores motion correction techniques, focusing on the widely-used PROPELLER method and its application to Golden-angle Cartesian Randomized Time-resolved (GOCART) acquisition. While PROPELLER effectively corrects in-plane translation and rotation, its use with cartesian data demands increased sampling. GOCART, a high-speed cartesian sampling scheme, has shown promise in Dynamic Contrast-Enhanced (DCE) MRI, yet its specific artifacts in brain imaging remain underexplored. Our simulation framework assesses PROPELLER correction for translational motion in GOCART-sampled data, examining two motion directions, varied frequencies, and different temporal resolutions. Serving as a vital pre-clinical testing tool, this platform contributes to the optimization of motion correction algorithms, addressing challenges and refining imaging protocols for enhanced diagnostic reliability in advanced brain MRI.

Post-COVID-19 conditions: a systematic review on advanced magnetic resonance neuroimaging findings.

Post-COVID conditions (PCCs) cover a wide spectrum of lingering symptoms experienced by survivors of coronavirus disease 2019 (COVID-19). Neurological and neuropsychiatric sequelae are common in PCCs. Advanced magnetic resonance imaging (MRI) techniques can reveal subtle alterations in brain structure, function, and perfusion that underlie these sequelae. This systematic review aimed to synthesize findings from studies that used advanced MRI to characterize brain changes in individuals with PCCs. A detailed literature search was conducted in the PubMed and Scopus databases to identify relevant studies that used advanced MRI modalities, such as structural MRI (sMRI), diffusion tensor imaging (DTI), functional MRI (fMRI), and perfusion-weighted imaging (PWI), to evaluate brain changes in PCCs. Twenty-five studies met the inclusion criteria, comprising 1219 participants with PCCs. The most consistent findings from sMRI were reduced gray matter volume (GMV) and cortical thickness (CTh) in cortical and subcortical regions. DTI frequently reveals increased mean diffusivity (MD), radial diffusivity (RD), and decreased fractional anisotropy (FA) in white matter tracts (WMTs) such as the corpus callosum, corona radiata, and superior longitudinal fasciculus. fMRI demonstrated altered functional connectivity (FC) within the default mode, salience, frontoparietal, somatomotor, subcortical, and cerebellar networks. PWI showed decreased cerebral blood flow (CBF) in the frontotemporal area, thalamus, and basal ganglia. Advanced MRI shows changes in the brain networks and regions of the PCCs, which may cause neurological and neuropsychiatric problems. Multimodal neuroimaging may help understand brain-behavior relationships. Longitudinal studies are necessary to better understand the progression of these brain anomalies.

Infantile Acute Subdural Hematohygroma: A Clinical and Neuroimaging Analysis of 18 Cases

&amp;lt;i&amp;gt;Objective&amp;lt;/i&amp;gt;: Even after the advent of magnetic resonance imaging (MRI), few studies have focused on the clinical profiles and neuroimaging characteristics of acute subdural hematohygroma (ASDHy) in infants. The present study attempted to elucidate the pathogenesis of ASDHy by analyzing the clinical and neuroimaging findings of ASDHy. &amp;lt;i&amp;gt;Materials &amp; Methods&amp;lt;/i&amp;gt;: The subjects were 18 patients (eight males and ten females) younger than 2 years with ASDHy. Their median age at diagnosis was 3.8 months (range: 2-10 months), and 15 (83%) were younger than 4 months. Imaging studies found no evidence of any primary brain injury. &amp;lt;i&amp;gt;Results&amp;lt;/i&amp;gt;: Five patients experienced a fall, and 13 (72%) had no history of head trauma. The presenting symptoms and signs were the acute onset of generalized convulsive seizures and or repeated vomiting. Retinal hemorrhage occurred in 14 patients (78%). The imaging studies revealed a large amount of bilateral, subdural fluid collection with an intensity higher than that of cerebrospinal fluid on MRI. Benign enlargement of the subarachnoid space (BESS) was found in 14 patients (78%), an enlarged sylvian fissure (LSF) was found in all the patients (100%), and greater, left-sided enlargement was evident in 16 patients (89%). Two patients underwent subdural drainage, and 16 patients (89%) were conservatively managed. All the patients had a favorable outcome. &amp;lt;i&amp;gt;Conclusions&amp;lt;/i&amp;gt;: ASDHy is a self-limiting disorder associated with retinal hemorrhage and a benign clinical course. Most cases develop in early infancy. LSF presumably loosens the tight fixation of the temporal lobes at the middle cranial fossa, thereby increasing the rotational force of the brain even during mild or unnoticeable impacts. Increased cranio-cerebral disproportion, including BESS and LSF, is a structural vulnerability which can lead to ASDHy in early infancy.

Teres minor denervation and pathologies resulting in shoulder joint instability and rotator cuff tears: A retrospective cross-sectional MRI study.

Teres minor denervation (TMD) has gained increasing attention in recent years, particularly with the advent of magnetic resonance imaging (MRI). The potential association between TMD and shoulder instability or rotator cuff tear remains a subject of interest in the orthopedic community. In this retrospective and cross-sectional study, authors aim to investigate the potential association between TMD and shoulder instability or rotator cuff tears. Authors retrospectively analyzed MRI findings from 105 patients with TMD, focusing on rotator cuff pathologies, posterior labrocapsular complex (PLCC) tears, and posteroinferior glenohumeral joint capsule alterations. Authors assessed the association between TMD and rotator cuff and PLCC tears. For the multivariate analysis, partial proportional odds models were constructed for subscapularis (SSC) and SSP tears. Rotator cuff tears were present in 82.9% of subjects, with subscapularis (SSC) tears being the most frequent (77.1%). A significant association was observed between TMD and rotator cuff pathology (P = .002). PLCC tears were found in 82.3% of patients, and humeral position relative to the osseous glenoid was noted in 60% of patients with TMD. A significant association was identified between TMD and shoulder instability or labral/capsular abnormalities (P < .001). More than half of the cases exhibited a long tethering appearance toward the axillary neurovascular bundle on T1-weighted sagittal images. Our findings suggest that TMD is significantly associated with rotator cuff tears and shoulder instability. This study highlights the importance of identifying and treating PLCC tears in patients with TMD to address shoulder instability. Further research is needed to elucidate the role of TMD in the pathogenesis of shoulder instability and rotator cuff pathology.

Decreased uncinate fasciculus integrity in functional seizures following traumatic brain injury.

The uncinate fasciculus (UF) has been implicated previously in contributing to the pathophysiology of functional (nonepileptic) seizures (FS). FS are frequently preceded by adverse life events (ALEs) and present with comorbid psychiatric symptoms, yet neurobiological correlates of these factors remain unclear. To address this gap, using advanced diffusion magnetic resonance imaging (dMRI), UF tracts in a large cohort of patients with FS and pre-existing traumatic brain injury (TBI + FS) were compared to those in patients with TBI without FS (TBI-only). We hypothesized that dMRI measures in UF structural connectivity would reveal UF differences when controlling for TBI status. Partial correlation tests assessed the potential relationships with psychiatric symptom severity measures. Participants with TBI-only (N = 46) and TBI + FS (N = 55) completed a series of symptom questionnaires and MRI scanning. Deterministic tractography via diffusion spectrum imaging (DSI) was implemented in DSI studio (https://dsi-studio.labsolver.org) with q-space diffeomorphic reconstruction (QSDR), streamline production, and manual segmentation to assess bilateral UF integrity. Fractional anisotropy (FA), radial diffusivity (RD), streamline counts, and their respective asymmetry indices (AIs) served as estimates of white matter integrity. Compared to TBI-only, TBI + FS participants demonstrated decreased left hemisphere FA and RD asymmetry index (AI) for UF tracts (both p < .05, false discovery rate [FDR] corrected). Additionally, TBI + FS reported higher symptom severity in depression, anxiety, and PTSD measures (all p < .01). Correlation tests comparing UF white matter integrity differences to psychiatric symptom severity failed to reach criteria for significance (all p > .05, FDR corrected). In a large, well-characterized sample, participants with FS had decreased white matter health after controlling for the history of TBI. Planned follow-up analysis found no evidence to suggest that UF connectivity measures are a feature of group differences in mood or anxiety comorbidities for FS. These findings suggest that frontolimbic structural connectivity may play a role in FS symptomology, after accounting for prior ALEs and comorbid psychopathology severity.