Magnetic Resonance Imaging Analysis Research Articles

Impact of Ocrelizumab on Disease Progression, Memory Improvement, and Quality of Life in Patients with Relapsing-Remitting Multiple Sclerosis: A Longitudinal MRI and Clinical Criteria Analysis.

Multiple sclerosis (MS) is a chronic, progressive neurological disorder that significantly impacts quality of life and functionality. Ocrelizumab, a monoclonal antibody targeting CD20-positive B cells, has emerged as a treatment for relapsing-remitting MS (RRMS). This study aimed to assess the impact of ocrelizumab on disease progression and quality of life over a longitudinal course, utilizing clinical criteria and magnetic resonance imaging (MRI) analyses. Conducted at the Neurology Department of Pius Brinzeu Clinical Emergency Hospital in Western Romania from 2020 to 2023, this observational study enrolled 93 patients with RRMS who commenced ocrelizumab therapy. The study employed the Expanded Disability Status Scale (EDSS) and MRI to evaluate disease progression, while quality of life was assessed using the World Health Organisation Quality of Life (WHOQOL) questionnaire, Beck Depression Index (BDI), and MOCA scales. Significant improvements were observed post-treatment. EDSS scores decreased from 4.61 to 4.08 (p = 0.038), indicating reduced disability. MRI analyses showed a substantial decrease in expansive lesions (from 67.74% to 26.88%, p < 0.001) and an increase in stationary lesions (from 32.26% to 73.12%, p < 0.001). Quality of life improvements were notable in the physical (from 58.42 to 64.84, p = 0.005) and environmental domains (from 63.21 to 68.44, p = 0.033). Cognitive functions, assessed via Montreal Cognitive Assessment (MOCA), showed a significant total score increase from 20.38 to 22.30 (p < 0.001). Subgroup analysis revealed more pronounced effects in females and younger patients, with a significant reduction in depressive symptoms measured by BDI scores (from 14.35 to 11.62, p = 0.003). Ocrelizumab significantly reduced disease activity and disability in RRMS patients, as demonstrated by improvements in EDSS scores and MRI findings. Quality of life and cognitive functions also showed considerable enhancements. These findings support ocrelizumab's efficacy in not only managing MS symptoms but also improving overall patient well-being.

Stereotactically intracerebral transplantation of neural stem cells for ischemic stroke attenuated inflammatory responses and promoted neurogenesis: an experimental study with monkeys.

Ischemic stroke is a common neurovascular disorder with high morbidity and mortality. However, the underlying mechanism of stereotactically intracerebral transplantation of human neural stem cell (hNSC) is not well elucidated. Four days after ischemic stroke induced by Rose Bengal photo-thrombosis, 7 cynomolgus monkeys were transplanted with hNSCs or vehicles stereotactically and followed up for 84 days. Behavioral assessments, magnetic resonance imaging, blood tests, and pathological analysis were performed before and after treatment. The proteome profiles of the left and right precentral gyrus and hippocampus were evaluated. Extracellular vesicle micro-RNA (miRNA) from the peripheral blood was extracted and analyzed. hNSC transplantation reduced the remaining infarcted lesion volume of cynomolgus monkeys with ischemic stroke without remarkable side effects. Proteomic analyses indicated that hNSC transplantation promoted GABAergic and glutamatergic neurogenesis, and restored the mitochondrial electron transport chain function in the ischemic infarcted left precentral gyrus or hippocampus. Immunohistochemical staining and qRT-PCT confirmed the promoting effects on neurogenesis and revealed that hNSCs attenuated post-infarct inflammatory responses by suppressing resident glia activation and mediating peripheral immune cell infiltration. Consistently, miRNA-sequencing revealed the miRNAs which were related to these pathways were down-regulated after hNSC transplantation. This study indicates that hNSCs can be effectively and safely used to treat ischemic stroke by promoting neurogenesis, regulating post-infarct inflammatory responses, and restoring mitochondrial function in both the infarct region and hippocampus.

Enhancing brain tumor classification in MRI scans with a multi-layer customized convolutional neural network approach.

The necessity of prompt and accurate brain tumor diagnosis is unquestionable for optimizing treatment strategies and patient prognoses. Traditional reliance on Magnetic Resonance Imaging (MRI) analysis, contingent upon expert interpretation, grapples with challenges such as time-intensive processes and susceptibility to human error. This research presents a novel Convolutional Neural Network (CNN) architecture designed to enhance the accuracy and efficiency of brain tumor detection in MRI scans. The dataset used in the study comprises 7,023 brain MRI images from figshare, SARTAJ, and Br35H, categorized into glioma, meningioma, no tumor, and pituitary classes, with a CNN-based multi-task classification model employed for tumor detection, classification, and location identification. Our methodology focused on multi-task classification using a single CNN model for various brain MRI classification tasks, including tumor detection, classification based on grade and type, and tumor location identification. The proposed CNN model incorporates advanced feature extraction capabilities and deep learning optimization techniques, culminating in a groundbreaking paradigm shift in automated brain MRI analysis. With an exceptional tumor classification accuracy of 99%, our method surpasses current methodologies, demonstrating the remarkable potential of deep learning in medical applications. This study represents a significant advancement in the early detection and treatment planning of brain tumors, offering a more efficient and accurate alternative to traditional MRI analysis methods.

Parallel Multilink Group Joint ICA: Fusion of 3D Structural and 4D Functional Data Across Multiple Resting fMRI Networks.

Multimodal neuroimaging research plays a pivotal role in understanding the complexities of the human brain and its disorders. Independent component analysis (ICA) has emerged as a widely used and powerful tool for disentangling mixed independent sources, particularly in the analysis of functional magnetic resonance imaging (fMRI) data. This paper extends the use of ICA as a unifying framework for multimodal fusion, introducing a novel approach termed parallel multilink group joint ICA (pmg-jICA). The method allows for the fusion of gray matter maps from structural MRI (sMRI) data to multiple fMRI intrinsic networks, addressing the limitations of previous models. The effectiveness of pmg-jICA is demonstrated through its application to an Alzheimer's dataset, yielding linked structure-function outputs for 53 brain networks. Our approach leverages the complementary information from various imaging modalities, providing a unique perspective on brain alterations in Alzheimer's disease. The pmg-jICA identifies several components with significant differences between HC and AD groups including thalamus, caudate, putamen with in the subcortical (SC) domain, insula, parahippocampal gyrus within the cognitive control (CC) domain, and the lingual gyrus within the visual (VS) domain, providing localized insights into the links between AD and specific brain regions. In addition, because we link across multiple brain networks, we can also compute functional network connectivity (FNC) from spatial maps and subject loadings, providing a detailed exploration of the relationships between different brain regions and allowing us to visualize spatial patterns and loading parameters in sMRI along with intrinsic networks and FNC from the fMRI data. In essence, developed approach combines concepts from joint ICA and group ICA to provide a rich set of output characterizing data-driven links between covarying gray matter networks, and a (potentially large number of) resting fMRI networks allowing further study in the context of structure/function links. We demonstrate the utility of the approach by highlighting key structure/function disruptions in Alzheimer's individuals.

Reduced olfactory bulb volume accompanies olfactory dysfunction after mild SARS-CoV-2 infection

Despite its high prevalence, the determinants of smelling impairment in COVID-19 remain not fully understood. In this work, we aimed to examine the association between olfactory bulb volume and the clinical trajectory of COVID-19-related smelling impairment in a large-scale magnetic resonance imaging (MRI) analysis. Data of non-vaccinated COVID-19 convalescents recruited within the framework of the prospective Hamburg City Health Study COVID Program between March and December 2020 were analyzed. At baseline, 233 participants underwent MRI and neuropsychological testing as well as a structured questionnaire for olfactory function. Between March and April 2022, olfactory function was assessed at follow-up including quantitative olfactometric testing with Sniffin’ Sticks. This study included 233 individuals recovered from mainly mild to moderate SARS-CoV-2 infections. Longitudinal assessment demonstrated a declining prevalence of self-reported olfactory dysfunction from 67.1% at acute infection, 21.0% at baseline examination and 17.5% at follow-up. Participants with post-acute self-reported olfactory dysfunction had a significantly lower olfactory bulb volume at baseline than normally smelling individuals. Olfactory bulb volume at baseline predicted olfactometric scores at follow-up. Performance in neuropsychological testing was not significantly associated with the olfactory bulb volume. Our work demonstrates an association of long-term self-reported smelling dysfunction and olfactory bulb integrity in a sample of individuals recovered from mainly mild to moderate COVID-19. Collectively, our results highlight olfactory bulb volume as a surrogate marker that may inform diagnosis and guide rehabilitation strategies in COVID-19.

Blood-brain barrier dysfunction and decreased transcription of tight junction proteins in epileptic dogs.

Epilepsy in dogs and humans is associated with blood-brain barrier (BBB) dysfunction (BBBD), which may involve dysfunction of tight junction (TJ) proteins, matrix metalloproteases, and astrocytes. Imaging techniques to assess BBB integrity, to identify potential treatment strategies, have not yet been evaluated in veterinary medicine. Some dogs with idiopathic epilepsy (IE) will exhibit BBBD. Identifying BBBD may improve antiepileptic treatment in the future. Twenty-seven dogs with IE and 10 healthy controls. Retrospective, prospective cohort study. Blood-brain barrier permeability (BBBP) scores were calculated for the whole brain and piriform lobe of all dogs by using dynamic contrast enhancement (DCE) magnetic resonance imaging (MRI) and subtraction enhancement analysis (SEA). Matrix metalloproteinase-9 (MMP9) activity in serum and cerebrospinal fluid (CSF) was measured and its expression in the piriform lobe was examined using immunofluorescent staining. Gene expression of TJ proteins and astrocytic transporters was analyzed in the piriform lobe. The DCE-MRI analysis of the piriform lobe identified higher BBBP score in the IE group when compared with controls (34.5% vs 26.5%; P = .02). Activity and expression of MMP9 were increased in the serum, CSF, and piriform lobe of IE dogs as compared with controls. Gene expression of Kir4.1 and claudin-5 in the piriform lobe of IE dogs was significantly lower than in control dogs. Our findings demonstrate BBBD in dogs with IE and were supported by increased MMP9 activity and downregulation of astrocytic potassium channels and some TJ proteins. Blood brain barrier dysfunction may be a novel antiepileptic therapy target.

The use of radiomic analysis of magnetic resonance imaging findings in predicting features of early osteoarthritis of the knee-a systematic review and meta-analysis.

The primary aim of this study was to systematically review current literature evaluating the use of radiomics in establishing the role of magnetic resonance imaging (MRI) findings in native knees in predicting features of osteoarthritis (OA).A systematic review was performed with respect to PRISMA guidelines in search of studies reporting radiomic analysis of magnetic resonance imaging (MRI) to analyse patients with native knee OA. Sensitivity and specificity of radiomic analyses were included for meta-analysis.Following our initial literature search of 1271 studies, only 5 studies met our inclusion criteria. This included 1730 patients (71.5% females) with a mean age of 55.4 ± 15.6years (range 24-66). The mean RQS of included studies was 16.6 (11-21). Meta-analysis demonstrated the pooled sensitivity and specificity for MRI in predicting features of OA in patients with native knees were 0.74 (95% CI 0.71, 0.78) and 0.85 (95% CI 0.83, 0.87), respectively.The results of this systematic review suggest that the high sensitivities and specificity of MRI-based radiomics may represent potential biomarker in the early identification and classification of native knee OA. Such analysis may inform surgeons to facilitate earlier non-operative management of knee OA in the select pre-symptomatic patients, prior to clinical or radiological evidence of degenerative change.

[123I]CLINDE SPECT as a neuroinflammation imaging approach in a rat model of stroke

Poststroke neuroinflammation exacerbates disease progression. [11C]PK11195-positron emission tomography (PET) imaging has been used to visualize neuroinflammation; however, its short half-life of 20 min limits its clinical use. [123I]CLINDE has a longer half-life (13h); therefore, [123I]CLINDE-single-photon emission computed tomography (SPECT) imaging is potentially more practical than [11C]PK11195-PET imaging in clinical settings. The objectives of this study were to 1) validate neuroinflammation imaging using [123I]CLINDE and 2) investigate the mechanisms underlying stroke in association with neuroinflammation using multimodal techniques, including magnetic resonance imaging (MRI), gas-PET, and histological analysis, in a rat model of ischemic stroke, that is, permanent middle cerebral artery occlusion (pMCAo). At 6 days post-pMCAo, [123I]CLINDE-SPECT considerably corresponded to the immunohistochemical images stained with the CD68 antibody (a marker for microglia/microphages), comparable to the level observed in [11C]PK11195-PET images. In addition, the [123I]CLINDE-SPECT images corresponded well with autoradiography images. Rats with severe infarcts, as defined by MRI, exhibited marked neuroinflammation in the peri-infarct area and less neuroinflammation in the ischemic core, accompanied by a substantial reduction in the cerebral metabolic rate of oxygen (CMRO2) in 15O-gas-PET. Rats with moderate-to-mild infarcts exhibited neuroinflammation in the ischemic core, where CMRO2 levels were mildly reduced. This study demonstrates that [123I]CLINDE-SPECT imaging is suitable for neuroinflammation imaging and that the distribution of neuroinflammation varies depending on the severity of infarction.

Deep learning-based magnetic resonance imaging analysis for chronic cerebral hypoperfusion risk.

Chronic cerebral hypoperfusion (CCH) is a frequently encountered clinical condition that poses a diagnostic challenge due to its nonspecific symptoms. To enhance the diagnosis of CCH and non-CCH through Magnetic Resonance Imaging (MRI), offering support in clinical decision-making and recommendations to ultimately elevate diagnostic accuracy and optimize patient treatment outcomes. In the retrospective research, we collected 204 routine brain magnetic resonance imaging (MRI) from March 1 to September 10 2022, as training and testing cohorts. And a validation cohort with 108 samples was collected from November 14 2022 to August 4 2023. MRI sequences were processed to obtain T1-weighted (T1WI) and T2-weighted (T2WI) sequence images for each patient. We propose CCH-Network (CCHNet), an end-to-end deep learning model, integrating convolution and Transformer modules to capture local and global structural information. Our novel adversarial training method improves feature knowledge capture, enhancing both generalization ability and efficiency in predicting CCH risk. We assessed the classification performance of the proposed model CCHNet by comparing it with existing state-of-the-art deep learning algorithms, including ResNet34, DenseNet121, VGG16, Convnext, ViT, Coat, and TransFG. To better validate model performance, we compared the results of the proposed model with eight neurologists to evaluate their consistency. CCHNet achieved an AUC of 91.6% (95% CI: 86.8-99.1), with an accuracy (ACC) of 85.0% (95% CI: 75.6-95.2). It demonstrated a sensitivity (SE) of 80.0% (95% CI: 71.6-95.6) and a specificity (SP) of 90.0% (95% CI: 82.3-97.8) in the testing cohort. In the validation cohort, the model demonstrated an AUC of 86.0% (95% CI: 80.3-93.0), an ACC of 84.2% (95% CI: 70.2-93.6), a SE of 83.3% (95% CI: 68.3-95.5), and a SP of 84.7% (95% CI: 70.3-96.8). The model improved the diagnostic performance of MRI with high SE and SP, providing a promising method for the diagnosis of CCH.

Neuroanatomical alterations in brain disorder: A magnetic resonance imaging analysis

Background: Bipolar disorder is a serious mental disorder caused by strong mood fluctuations, affecting 2% of the world's population. People with bipolar disorder experience both manic and depressive episodes, which can lead to suicidal thoughts and changes in appetite, activity, and focus. There are different subtypes of bipolar disorder, with cyclothymic disorder being the milder of the two. Bipolar I is characterized by manic periods, while bipolar II is marked by hypomanic and significant depressive episodes. Bipolar disorder is a biological, genetic, and environmental condition that can affect anyone at any age. Methods: In this study, we enrolled a total of 49 bipolar disorder (BD) participants to 44 healthy control (HC) subjects. Bipolar disorder patients were enlisted with their consent using web portals, medical facilities, and local marketing. MRI contraindications, medical history, self-reported using a mood-altering medication, inadequate vision, and left-handedness were among the exclusion criteria. The various tissue structures which are: the gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) were examined using voxel-based morphometry (VBM) to ascertain these changes. We employed statistical parametric mapping (SPM) for both image processing and statistical analysis. Results: Our findings demonstrate that individuals with bipolar disorder showed a substantial volume decrease in both gray matter (GM) and white matter (WM), with gray matter (GM) and white matter (WM) volume decreases being more noticeable in the frontal, temporal, parietal, and occipital lobes. Additionally, individuals with bipolar disorder showed no CSF volume in any of the brain regions. Conclusion: Our VBM research backs up earlier studies that link changes in brain structure, particularly gray matter and white matter, to bipolar disorder. These discoveries shed more information on the neurology of bipolar disorder and could guide the creation of more potent diagnostic and treatment modalities.

Modeling cerebrospinal fluid dynamics across the entire intracranial space through integration of four-dimensional flow and intravoxel incoherent motion magnetic resonance imaging

BackgroundBidirectional reciprocal motion of cerebrospinal fluid (CSF) was quantified using four-dimensional (4D) flow magnetic resonance imaging (MRI) and intravoxel incoherent motion (IVIM) MRI. To estimate various CSF motions in the entire intracranial region, we attempted to integrate the flow parameters calculated using the two MRI sequences. To elucidate how CSF dynamics deteriorate in Hakim’s disease, an age-dependent chronic hydrocephalus, flow parameters were estimated from the two MRI sequences to assess CSF motion in the entire intracranial region.MethodsThis study included 127 healthy volunteers aged ≥ 20 years and 44 patients with Hakim’s disease. On 4D flow MRI for measuring CSF motion, velocity encoding was set at 5 cm/s. For the IVIM MRI analysis, the diffusion-weighted sequence was set at six b-values (i.e., 0, 50, 100, 250, 500, and 1000 s/mm2), and the biexponential IVIM fitting method was adapted. The relationships between the fraction of incoherent perfusion (f) on IVIM MRI and 4D flow MRI parameters including velocity amplitude (VA), absolute maximum velocity, stroke volume, net flow volume, and reverse flow rate were comprehensively evaluated in seven locations in the ventricles and subarachnoid spaces. Furthermore, we developed a new parameter for fluid oscillation, the Fluid Oscillation Index (FOI), by integrating these two measurements. In addition, we investigated the relationship between the measurements and indices specific to Hakim’s disease and the FOIs in the entire intracranial space.ResultsThe VA on 4D flow MRI was significantly associated with the mean f-values on IVIM MRI. Therefore, we estimated VA that could not be directly measured on 4D flow MRI from the mean f-values on IVIM MRI in the intracranial CSF space, using the following formula; e0.2(f−85) + 0.25. To quantify fluid oscillation using one integrated parameter with weighting, FOI was calculated as VA × 10 + f × 0.02. In addition, the FOIs at the left foramen of Luschka had the strongest correlations with the Evans index (Pearson’s correlation coefficient: 0.78). The other indices related with Hakim’s disease were significantly associated with the FOIs at the cerebral aqueduct and bilateral foramina of Luschka. FOI at the cerebral aqueduct was also elevated in healthy controls aged ≥ 60 years.ConclusionsWe estimated pulsatile CSF movements in the entire intracranial CSF space in healthy individuals and patients with Hakim’s disease using FOI integrating VA from 4D flow MRI and f-values from IVIM MRI. FOI is useful for quantifying the CSF oscillation.

Visualization of Reissner's membrane in the mouse inner ear using highly sensitive magnetic resonance imaging analysis

Although research on hearing loss, including the identification of causative genes, has become increasingly active, the pathogenic mechanism of hearing loss remains unclear. One of the reasons for this is that the structure of the inner ear of mice, which is commonly used as a genetically modified animal model, is too small and complex, making it difficult to accurately capture abnormalities and dynamic changes in vivo. Especially, Reissner's membrane is a very important structure that separates the perilymph and endolymph of the inner ear. This malformation or damage induces abnormalities in hearing and balance. Until now, imaging analyses, such as magnetic resonance imaging (MRI) and computed tomography, are performed to investigate the inner ear structure in vivo; however, it has been difficult to analyze the small inner ear structure of mice owing to resolution. Therefore, there is an urgent need to develop an image analysis method that can accurately capture the structure of the inner ear of mice including Reissner's membrane, both dynamically and statically.This study aimed to investigate whether it is possible to accurately capture the structure (e.g., Reissner's membrane) and abnormalities of the inner ear of mice using an 11.7 T MRI. By combining two types of MRI methods, in vivo and ex vivo, we succeeded for the first time in capturing the fine structure of the normal mouse inner ear, such as the Reissner's membrane, and inflammatory lesions of otitis media mouse models in detail and accurately. In the future, we believe that understanding the state of Reissner's membrane during living conditions will greatly contribute to the development of research on inner ear issues, such as hearing loss.

Fully automatic quantification for hand synovitis in rheumatoid arthritis using pixel-classification-based segmentation network in DCE-MRI.

A classification-based segmentation method is proposed to quantify synovium in rheumatoid arthritis (RA) patients using a deep learning (DL) method based on time-intensity curve (TIC) analysis in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). This retrospective study analyzed a hand MR dataset of 28 RA patients (six males, mean age 53.7years). A researcher, under expert guidance, used in-house software to delineate regions of interest (ROIs) for hand muscles, bones, and synovitis, generating a dataset with 27,255 pixels with corresponding TICs (muscle: 11,413, bone: 8502, synovitis: 7340). One experienced musculoskeletal radiologist performed ground truth segmentation of enhanced pannus in the joint bounding box on the 10th DCE phase, or around 5min after contrast injection. Data preprocessing included median filtering for noise reduction, phase-only correlation algorithm for motion correction, and contrast-limited adaptive histogram equalization for improved image contrast and noise suppression. TIC intensity values were normalized using zero-mean normalization. A DL model with dilated causal convolution and SELU activation function was developed for enhanced pannus segmentation, tested using leave-one-out cross-validation. 407 joint bounding boxes were manually segmented, with 129 synovitis masks. On the pixel-based level, the DL model achieved sensitivity of 85%, specificity of 98%, accuracy of 99% and precision of 84% for enhanced pannus segmentation, with a mean Dice score of 0.73. The false-positive rate for predicting cases without synovitis was 0.8%. DL-measured enhanced pannus volume strongly correlated with ground truth at both pixel-based (r = 0.87, p < 0.001) and patient-based levels (r = 0.84, p < 0.001). Bland-Altman analysis showed the mean difference for hand joints at the pixel-based and patient-based levels were -9.46 mm3 and -50.87 mm3, respectively. Our DL-based DCE-MRI TIC shape analysis has the potential for automatic segmentation and quantification of enhanced synovium in the hands of RA patients.

Radiofrequency lesion in atrial wall: how variable is it? 9 Tesla MRI analysis of RF lesion volume in a swine model

Abstract Background Little is known about radiofrequency (RF) lesion volume (rLV). Most of animal or ex-vivo studies quantify the size of RF lesions size using width and/or depth, while electrophysiologists use manufacturers’ indirect indices (such as Ablation Index or Lesion Size Index). Purpose We aimed to evaluate rLV by high resolution 9-Tesla magnetic resonance imaging (MRI), in ex-vivo swine model. We studied the variability of rLV and extra-cardiac effects in two groups of swine, one treated with low energy (LE)-long application RF (20W-50 sec) and another treated with high energy (HE)-short application RF (50 W-20 sec). Methods This was a prospective observational study on 18 piglets, that were anesthetized, mechanically ventilated, and divided into LE (n = 9) and HE (n = 9) groups (Figure 1). An irrigated contact force catheter was used to perform RF applications in 4 largely separated spots: 2 on right atrium and 2 on left atrium wall. RF parameters were monitored during RF. Each animal was sacrificed after 5-7 days, and RF lesion samples were excised and acquired by 9 MRI. Results 70 RF applications were performed (36 in LE group and 34 in HE group). MRI was performed on 26 samples of LE group and 28 of HE group (Figure 2). The median RF lesion volume was 89.3 mm3 (IQR 47.9-175.9). Regarding lesion volume, the HE groups showed 35% higher volume than LE group (mean lesion volume 86.2 mm3 vs 116.3 mm3, p = 0.119). HE group also showed more complications (0 vs 7, p=0.02) and more extracardiac lesion (14 vs 18, p=0.613). Conclusions There was a wide and unpredictable variability of RF lesion on atrial wall, even under controlled experimental conditions, which could explain arrhythmias recurrences. The variability seems greatest, although not statistically significant, during HE applications. The dose/effect relationship needs to be carefully studied for treatments of cardiac arrhythmias for both RF and other forms of energy available today.Study flow-chartComparison between groups

Correlations of characteristics with tissue involvement in knee gouty arthritis: Magnetic resonance imaging analysis

ObjectiveThis study investigates the MRI features of knee gouty arthritis (KGA), examines its relationship with the extent of tissue involvement, and assesses whether risk factors can predict KGA. Materials and methodsPatients diagnosed with KGA underwent MRI examinations, and two independent observers retrospectively analyzed data from 44 patients (49 knees). These patients were divided into mild and severe groups based on tissue involvement observed during arthroscopy. MRI features were summarized, and the intraclass correlation coefficient evaluated interobserver reproducibility. Single-factor analysis compared clinical indicators and MRI features between groups, while Cramer's V coefficient assessed correlations. Multivariate logistic regression identified predictors of tissue involvement extent, and a ROC curve evaluated diagnostic performance. ResultsAmong 49 knees, 18 had mild and 31 had severe tissue involvement. Key MRI features included ligament sketch-like changes, meniscal urate deposition, irregularly serrated cartilage changes, low-signal signs within joint effusion, synovial proliferation, Hoffa's fat pad synovitis, gouty tophi, bone erosion, and bone marrow edema. The interobserver reliability of the MRI features was good. Significant differences (P < 0.05) were observed between the groups for anterior cruciate ligament (ACL) sketch-like changes, Hoffa's fat pad synovitis, and gouty tophi. ACL sketch-like changes (r = 0.309), Hoffa's fat pad synovitis (r = 0.309), and gouty tophi (r = 0.408) were positively correlated with the extent of tissue involvement (P < 0.05). ACL sketch-like changes (OR = 9.019, 95 % CI: 1.364–61.880), Hoffa's fat pad synovitis (OR = 6.472, 95 % CI: 1.041–40.229), and gouty tophi (OR = 5.972, 95 % CI: 1.218–29.276) were identified as independent predictors of tissue involvement extent (P < 0.05). The area under the ROC curve was 0.862, with a sensitivity of 67.70 %, specificity of 94.40 %, and accuracy of 79.14 %. ConclusionThis comprehensive analysis of MRI features identifies ligament sketch-like changes, meniscal urate deposition, and low-signal signs within joint effusion as characteristic MRI manifestations of KGA. Irregular cartilage changes are valuable for differential diagnosis in young and middle-aged patients. ACL sketch-like changes, Hoffa's fat pad synovitis, and gouty tophi correlate with tissue involvement severity and are critical in predicting and assessing the extent of tissue involvement in KGA.

Multimodal imaging investigation of structural rich club alterations in Alzheimer's disease and mild cognitive impairment: Amyloid deposition, structural atrophy, and functional activation differences.

Alzheimer's disease (AD) is characterized by significant cerebral dysfunction, including increased amyloid deposition, gray matter atrophy, and changes in brain function. The involvement of highly connected network hubs, known as the "rich club," in the pathology of the disease remains inconclusive despite previous research efforts. In this study, we aimed to systematically assess the link between the rich club and AD using a multimodal neuroimaging approach. We employed network analyses of diffusion magnetic resonance imaging (MRI), longitudinal assessments of gray matter atrophy, amyloid deposition measurements using positron emission tomography (PET) imaging, and meta-analytic data on functional activation differences. Our study focused on evaluating the role of both the structural brain network's core and extended rich club regions in individuals with mild cognitive impairment (MCI) and those diagnosed with AD. Our findings revealed that structural rich club regions exhibited accelerated gray matter atrophy and increased amyloid deposition in both MCI and AD. Importantly, these regions remained unaffected by altered functional activation patterns observed outside the core rich club regions. These results shed light on the connection between two major AD biomarkers and the rich club, providing valuable insights into AD as a potential disconnection syndrome.

MRIO: the Magnetic Resonance Imaging Acquisition and Analysis Ontology.

Magnetic resonance imaging of the brain is a useful tool in both the clinic and research settings, aiding in the diagnosis and treatments of neurological disease and expanding our knowledge of the brain. However, there are many challenges inherent in managing and analyzing MRI data, due in large part to the heterogeneity of data acquisition. To address this, we have developed MRIO, the Magnetic Resonance Imaging Acquisition and Analysis Ontology. MRIO provides well-reasoned classes and logical axioms for the acquisition of several MRI acquisition types and well-known, peer-reviewed analysis software, facilitating the use of MRI data. These classes provide a common language for the neuroimaging research process and help standardize the organization and analysis of MRI data for reproducible datasets. We also provide queries for automated assignment of analyses for given MRI types. MRIO aids researchers in managing neuroimaging studies by helping organize and annotate MRI data and integrating with existing standards such as Digital Imaging and Communications in Medicine and the Brain Imaging Data Structure, enhancing reproducibility and interoperability. MRIO was constructed according to Open Biomedical Ontologies Foundry principles and has contributed several classes to the Ontology for Biomedical Investigations to help bridge neuroimaging data to other domains. MRIO addresses the need for a "common language" for MRI that can help manage the neuroimaging research, by enabling researchers to identify appropriate analyses for sets of scans and facilitating data organization and reporting.

In situ temperature determination using magnetic resonance spectroscopy thermometry for noninvasive postmortem examinations.

Magnetic resonance spectroscopy (MRS) thermometry offers a noninvasive, localized method for estimating temperature by leveraging the temperature-dependent chemical shift of water relative to a temperature-stable reference metabolite under suitable calibration. Consequentially, this technique has significant potential as a tool for postmortem MR examinations in forensic medicine and pathology. In these examinations, the deceased are examined at a wide range of body temperatures, and MRS thermometry may be used for the temperature adjustment of magnetic resonance imaging (MRI) protocols or for corrections in the analysis of MRI or MRS data. However, it is not yet clear to what extent postmortem changes may influence temperature estimation with MRS thermometry. In addition, N-acetylaspartate, which is commonly used as an in vivo reference metabolite, is known to decrease with increasing postmortem interval (PMI). This study shows that lactate, which is not only present in significant amounts postmortem but also has a temperature-stable chemical shift, can serve as a suitable reference metabolite for postmortem MRS thermometry. Using lactate, temperature estimation in postmortem brain tissue of severed sheep heads was accurate up to 60 h after death, with a mean absolute error of less than 0.5°C. For this purpose, published calibrations intended for in vivo measurements were used. Although postmortem decomposition resulted in severe metabolic changes, no consistent deviations were observed between measurements with an MR-compatible temperature probe and MRS thermometry with lactate as a reference metabolite. In addition, MRS thermometry was applied to 84 deceased who underwent a MR examination as part of the legal examination. MRS thermometry provided plausible results of brain temperature in comparison with rectal temperature. Even for deceased with a PMI well above 60 h, MRS thermometry still provided reliable readings. The results show a good suitability of MRS thermometry for postmortem examinations in forensic medicine.

Characteristics and prognostic value of cardiac magnetic resonance strain analysis in patients with different phenotypes of heart failure.

Strain analysis of cardiac magnetic resonance imaging (CMR) is important for the prognosis of heart failure (HF). Herein, we aimed to identify the characteristics and prognostic value of strain analysis revealed by CMR in different HF phenotypes. Participants with HF, including HF with reduced ejection fraction, HF with mildly reduced ejection fraction, and HF with preserved ejection fraction, and controls were enrolled. The baseline information and clinical parameters of participants were collected, and echocardiography and CMR examination were performed. Three-dimensional strain analysis was performed in the left ventricle, right ventricle, left atrium, and right atrium using CMR. A multifactor Cox risk proportional model was established to assess the influencing factors of cardiovascular adverse events in patients with HF. During a median follow-up of 999 days (range: 616-1334), 20.6% of participants (73/354) experienced adverse events (HF readmission and/or cardiovascular death). Univariable Cox regression revealed that a 1% increase in left atrial global longitudinal strain (LAGLS) was associated with a hazard ratio (HR) of 1.21 [95% confidence interval (CI):1.15-1.28; P < 0.001]. Left ventricular global circumferential strain (LVGCS) (HR, 1.18; 95% CI: 1.12-1.24; P < 0.001), and left ventricular global longitudinal strain (LVGLS) (HR, 1.27; 95% CI: 1.20-1.36; P < 0.001) were also associated with HF hospitalizations and cardiovascular deaths. Among clinical variables, hypertension (HR, 2.11; 95% CI: 1.33-13.36; P = 0.002), cardiomyopathy (HR, 2.26; 95% CI: 1.42-3.60; P < 0.001) were associated with outcomes in univariable analysis. Multivariable analyses revealed that LAGLS (95% CI: 1.08-1.29; P < 0.001), LVGLS (95% CI:1.08-1.29; P < 0.001) and LVGCS (95% CI: 1.19-1.51; P < 0.001) were significantly associated with outcomes. Among clinical variables, hypertension (95% CI: 1.09-3.73; P < 0.025) remained a risk factor. CMR plays an obvious role in phenotyping HF. Strain analysis, particularly left atrial and left ventricular strain analysis (LAGLS, LVGLS, and LVGCS) has good value in predicting adverse outcome events.

Enhanced Segmentation of Ischemic Stroke Lesion in MRI Images Using a Geometrically Customised Deep Convolution Model (GCDCM)

Ischemic stroke lesion often known as a stroke, is a significant health issue that requires accurate analysis and classification of brain magnetic resonance imaging (MRI) data. In this study, we propose a novel deep transfer learning approach, called geometrically customized deep convolution model, for the purpose of MRI analysis and classification of brain stroke. Neurostroke segmentation is a serious medical image processing challenge. Segmented regions aid disease identification and treatment. Anywhere can form thrombi. Segmentation facilitates automatic detection because they can be any size or shape. Popular image analysis tool MRI diagnoses well. This diagnostic method shows brain stroke architecture. MRI must replace manual detection. Online datasets recommended cerebral stroke detection and segmentation. Deep learning model MRI scans and detectron 2 with masked CNN Nets segment thrombus. This net architecture recognises dataset stroke boundaries. Classifying strokes with vgg16, resnet50, inceptionv3, and resnet5 transfer learning is possible. Mask the image, then binary predict by eliminating the skull, extracting features, and iterating to find stroke. The model and thrombus mask are predicted if the binary prediction matches the human forecast. Otherwise, data processing resumes. Binary prediction uses the segmentation region and pixels overlap between the ground truth and predicted segmentation to calculate parameters. Compared to reality, the categorization of medical images with weak signals seems tough, especially with a short "train" dataset. Mixing deep learning architectures avoids these drawbacks and extracts signals to accurately classify classes. Deep neural networks best recognise, find, and divide computer vision objects for clinical image analysis. Preprocessing MRI scans, skull stripping with deep CNN architecture combinational net, and brain stroke segmentation are our main tasks. Modern medical image processing is hard. Flexible and uneven borders make brain strokes hard to identify and segment. The transfer learning-based super pixel approach segments brainstrokes. Because we predict every visual pixel, dense prediction occurs. Early discovery of thrombus improves treatment and survival. These procedures have considerably improved our quality indexes.