Cortical lesions in multiple sclerosis (MS) severely affect cognition, but their longitudinal evolution and impact on specific cognitive functions remain understudied. This study investigates the evolution of function-specific cognitive functioning over 10 years in people with MS and assesses the influence of cortical lesion load and formation on these trajectories. In this prospectively collected study, people with MS underwent 3T MRI (T1 and fluid-attenuated inversion recovery) at 3 study visits between 2008 and 2022. Cognitive functioning was evaluated based on neuropsychological assessment reflecting 7 cognitive functions: attention; executive functioning (EF); information processing speed (IPS); verbal fluency; and verbal, visuospatial, and working memory. Cortical lesions were manually identified on artificial intelligence-generated double-inversion recovery images. Linear mixed models were constructed to assess the temporal evolution between cortical lesion load and function-specific cognitive decline. In addition, analyses were stratified by MS disease stage: early and late relapsing-remitting MS (cutoff disease duration at 15 years) and progressive MS. The study included 223 people with MS (mean age, 47.8 ± 11.1 years; 153 women) and 62 healthy controls. All completed 5-year follow-up, and 37 healthy controls and 94 with MS completed 10-year follow-up. At baseline, people with MS exhibited worse functioning of IPS and working memory. Over 10 years, cognitive decline was most severe in attention, verbal memory, and EF. At baseline, people with MS had a median cortical lesion count of 7 (range 0-73), which was related to subsequent decline in attention (B[95% CI] = -0.22 [-0.40 to -0.03]) and verbal fluency (B[95% CI] = -0.23[-0.37 to -0.09]). Over time, cortical lesions increased by a median count of 4 (range -2 to 71), particularly in late and progressive disease, and was related to decline in verbal fluency (B [95% CI] = -0.33 [-0.51 to -0.15]). The associations between (change in) cortical lesion load and cognitive decline were not modified by MS disease stage. Cognition worsened over 10 years, particularly affecting attention, verbal memory, and EF, while preexisting impairments were worst in other functions such as IPS. Worse baseline cognitive functioning was related to baseline cortical lesions, whereas baseline cortical lesions and cortical lesion formation were related to cognitive decline in functions less affected at baseline. Accumulating cortical damage leads to spreading of cognitive impairments toward additional functions.

Chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI) has emerged as a powerful tool to image endogenous or exogenous macromolecules. CEST contrast highly depends on radiofrequency irradiation B₁ level. Spatial inhomogeneity of B₁ field would bias CEST measurement. Conventional interpolation-based B₁ correction method required CEST dataset acquisition under multiple B₁ levels, substantially prolonging scan time. The recently proposed supervised deep learning approach reconstructed B₁ inhomogeneity corrected CEST effect at the identical B₁ as of the training data, hindering its generalization to other B₁ levels. In this study, we proposed a Conditional Variational Autoencoder (CVAE)-based generative model to generate B₁ inhomogeneity corrected Z spectra from single CEST acquisition. The model was trained from pixel-wise source-target paired Z spectra under multiple B₁ with target B₁ as a conditional variable. Numerical simulation and healthy human brain imaging at 5 T were respectively performed to evaluate the performance of proposed model in B₁ inhomogeneity corrected CEST MRI. Results showed that the generated B₁-corrected Z spectra agreed well with the reference averaged from regions with subtle B₁ inhomogeneity. Moreover, the performance of the proposed model in correcting B₁ inhomogeneity in APT CEST effect, as measured by both MTR_asym and [Formula: see text] at 3.5 ppm, were superior over conventional Z/contrast-B₁-interpolation and other deep learning methods, especially when target B₁ were not included in sampling or training dataset. In summary, the proposed model allows generalized B₁ inhomogeneity correction, benefiting quantitative CEST MRI in clinical routines.

Pediatric bipolar disorder (PBD) is a highly debilitating condition, characterized by alternating episodes of mania and depression, with intervening periods of remission. Limited information is available about the functional and structural abnormalities in PBD, particularly when comparing type I with type II subtypes. Resting-state brain activity and structural grey matter, assessed through MRI, may provide insight into the neurobiological biomarkers of this disorder. In this study, Resting state Regional Homogeneity (ReHo) and grey matter concentration (GMC) data of 58 PBD patients, and 21 healthy controls matched for age, gender, education and IQ, were analyzed in a data fusion unsupervised machine learning approach known as transposed Independent Vector Analysis. Two networks significantly differed between BPD and HC. The first network included fronto- medial regions, such as the medial and superior frontal gyrus, the cingulate, and displayed higher ReHo and GMC values in PBD compared to HC. The second network included temporo-posterior regions, as well as the insula, the caudate and the precuneus and displayed lower ReHo and GMC values in PBD compared to HC. Additionally, two networks differ between type-I vs type-II in PBD: an occipito-cerebellar network with increased ReHo and GMC in type-I compared to type-II, and a fronto-parietal network with decreased ReHo and GMC in type-I compared to type-II. Of note, the first network positively correlated with depression scores. These findings shed new light on the functional and structural abnormalities displayed by pediatric bipolar patients.

Implantation of metallic instrumentation is the mainstay of a variety of orthopaedic and spine surgeries. Postoperatively, imaging of the soft tissues around these implants is commonly required to assess for persistent, recurrent, and/or new pathology (ie, instrumentation loosening, particle disease, infection, neural compression); visualization of these pathologies often requires the superior soft-tissue contrast of magnetic resonance imaging (MRI). As susceptibility artifacts from ferromagnetic implants can result in unacceptable image quality, unique MRI approaches are often necessary to provide accurate imaging. In this text, a comprehensive review is provided on common artifacts encountered in orthopaedic MRI, including comparisons of artifacts from different metallic alloys and common nonpropriety/propriety MR metallic artifact reduction methods. The newest metal-artifact suppression imaging technology and future directions (ie, deep learning/artificial intelligence) in this important field will be considered.

Late Gadolinium-enhancement in cardiac magnetic resonance imaging (LGE-CMR) is the gold standard for assessing myocardial infarction (MI) size. Texture-based probability mapping (TPM) is a novel machine learning-based analysis of LGE images of myocardial injury. The ability of TPM to assess acute myocardial injury has not been determined. This proof-of-concept study aimed to determine how TPM responds to the dynamic changes in myocardial injury during one-year follow-up after a first-time revascularized acute MI. 41 patients with first-time acute ST-elevation MI and single-vessel occlusion underwent successful PCI. LGE-CMR images were obtained 2 days, 1 week, 2 months, and 1 year following MI. TPM size was compared with manual LGE-CMR based MI size, LV remodeling, and biomarkers. TPM size remained larger than MI by LGE-CMR at all time points, decreasing from 2 days to 2 months (p < 0.001) but increasing from 2 months to 1 year (p < 0.01). TPM correlated strongly with peak Troponin T (p < 0.001) and NT-proBNP (p < 0.001). At 1 week, 2 months, and 1 year, TPM showed a stronger correlation with NT-proBNP than MI size by LGE-CMR. Analyzing all collected pixels from 2 months to 1 year revealed a general increase in pixel scar probability in both the infarcted and non-infarcted regions. This proof-of-concept study suggests that TPM may offer additional insights into myocardial alterations in both infarcted and non-infarcted regions following acute MI. These findings indicate a potential role for TPM in assessing the overall myocardial response to infarction and the subsequent healing and remodeling process.

To assess the agreement between lumbar disc degeneration (DD) grading by the convolutional neural network model SpineNet and radiologist's visual grading. In a 14-year follow-up MRI study involving 19 male volunteers, lumbar DD was assessed by SpineNet and two radiologists using the Pfirrmann classification at baseline (age 37) and after 14 years (age 51). Pfirrmann summary scores (PSS) were calculated by summing individual disc grades. The agreement between the first radiologist and SpineNet was analyzed, with the second radiologist's grading used for inter-observer agreement. Significant differences were observed in the Pfirrmann grades and PSS assigned by the radiologist and SpineNet at both time points. SpineNet assigned Pfirrmann grade 1 to several discs and grade 5 to more discs compared to the radiologists. The concordance correlation coefficients (CCC) of PSS between the radiologist and SpineNet were 0.54 (95% CI: 0.28 to 0.79) at baseline and 0.54 (0.27 to 0.80) at follow-up. The average kappa (κ) values of 0.74 (0.68 to 0.81) at baseline and 0.68 (0.58 to 0.77) at follow-up. CCC of PSS between the radiologists was 0.83 (0.69 to 0.97) at baseline and 0.78 (0.61 to 0.95) at follow-up, with κ values ranging from 0.73 to 0.96. We found fair to substantial agreement in DD grading between SpineNet and the radiologist, albeit with notable discrepancies. These findings indicate that AI-based systems like SpineNet hold promise as complementary tools in radiological evaluation, including in longitudinal studies, but emphasize the need for ongoing refinement of AI algorithms.

We conducted a systematic review and meta-analysis to evaluate the performance of magnetic resonance imaging (MRI)-derived deep learning (DL) models in predicting 1p/19q codeletion status in glioma patients. The literature search was performed in four databases: PubMed, Web of Science, Embase, and Scopus. We included the studies that evaluated the performance of end-to-end DL models in predicting the status of glioma 1p/19q codeletion. The quality of the included studies was assessed by the Quality assessment of diagnostic accuracy studies-2 (QUADAS-2) METhodological RadiomICs Score (METRICS). We calculated diagnostic pooled estimates and heterogeneity was evaluated using I². Subgroup analysis and sensitivity analysis were conducted to explore sources of heterogeneity. Publication bias was evaluated by Deeks' funnel plots. Twenty studies were included in the systematic review. Only two studies had a low quality. A meta-analysis of the ten studies demonstrated a pooled sensitivity of 0.77 (95% CI: 0.63-0.87), a specificity of 0.85 (95% CI: 0.74-0.92), a positive diagnostic likelihood ratio (DLR) of 5.34 (95% CI: 2.88-9.89), a negative DLR of 0.26 (95% CI: 0.16-0.45), a diagnostic odds ratio of 20.24 (95% CI: 8.19-50.02), and an area under the curve of 0.89 (95% CI: 0.86-0.91). The subgroup analysis identified a significant difference between groups depending on the segmentation method used. DL models can predict glioma 1p/19q codeletion status with high accuracy and may enhance non-invasive tumor characterization and aid in the selection of optimal therapeutic strategies.

Local recurrence and distant metastasis were a common manifestation of locoregionally advanced nasopharyngeal carcinoma (LA-NPC) after neoadjuvant chemoradiotherapy (NACT). To validate the clinical value of MRI radiomic models based on deep learning for predicting the recurrence of LA-NPC patients. A total of 328 NPC patients from four hospitals were retrospectively included and divided into the training(n = 229) and validation (n = 99) cohorts randomly. Extracting 975 traditional radiomic features and 1000 deep radiomic features from contrast enhanced T1-weighted (T1WI + C) and T2-weighted (T2WI) sequences, respectively. Least absolute shrinkage and selection operator (LASSO) was applied for feature selection. Five machine learning classifiers were conducted to develop three models for LA-NPC prediction in training cohort, namely Model I: traditional radiomic features, Model II: combined the deep radiomic features with Model I, and Model III: combined Model II with clinical features. The predictive performance of these models were evaluated by receive operating characteristic (ROC) curve analysis, area under the curve (AUC), accuracy, sensitivity and specificity in both cohorts. The clinical characteristics in two cohorts showed no significant differences. Choosing 15 radiomic features and 6 deep radiomic features from T1WI + C. Choosing 9 radiomic features and 6 deep radiomic features from T2WI. In T2WI, the Model II based on Random forest (RF) (AUC = 0.87) performed best compared with other models in validation cohort. Traditional radiomic model combined with deep radiomic features shows excellent predictive performance. It could be used assist clinical doctors to predict curative effect for LA-NPC patients after NACT.

Vestibular schwannoma (VS) is the most prevalent intracranial schwannoma. Surgery is one of the options for the treatment of VS, with the preservation of facial nerve (FN) function being the primary objective. Therefore, postoperative FN function prediction is essential. However, achieving automation for such a method remains a challenge. In this study, we proposed a fully automatic deep learning approach based on multi-sequence magnetic resonance imaging (MRI) to predict FN function after surgery in VS patients. We first developed a segmentation network 2.5D Trans-UNet, which combined Transformer and U-Net to optimize contour segmentation for radiomic feature extraction. Next, we built a deep learning network based on the integration of 1DConvolutional Neural Network (1DCNN) and Gated Recurrent Unit (GRU) to predict postoperative FN function using the extracted features. We trained and tested the 2.5D Trans-UNet segmentation network on public and private datasets, achieving accuracies of 89.51% and 90.66%, respectively, confirming the model's strong performance. Then Feature extraction and selection were performed on the private dataset's segmentation results using 2.5D Trans-UNet. The selected features were used to train the 1DCNN-GRU network for classification. The results showed that our proposed fully automatic radiomics pipeline outperformed the traditional radiomics pipeline on the test set, achieving an accuracy of 88.64%, demonstrating its effectiveness in predicting the postoperative FN function in VS patients. Our proposed automatic method has the potential to become a valuable decision-making tool in neurosurgery, assisting neurosurgeons in making more informed decisions regarding surgical interventions and improving the treatment of VS patients.

Cerebral vascular occlusion is a serious condition that can lead to stroke and permanent neurological damage due to insufficient oxygen and nutrients reaching brain tissue. Early diagnosis and accurate segmentation are critical for effective treatment planning. Due to its high soft tissue contrast, Magnetic Resonance Imaging (MRI) is commonly used for detecting these occlusions such as ischemic stroke. However, challenges such as low contrast, noise, and heterogeneous lesion structures in MRI images complicate manual segmentation and often lead to misinterpretations. As a result, deep learning-based Computer-Aided Diagnosis (CAD) systems are essential for faster and more accurate diagnosis and treatment methods, although they can sometimes face challenges such as high computational costs and difficulties in segmenting small or irregular lesions. This study proposes a novel U-Net architecture enhanced with ConvNeXtV2 blocks and GRN-based Multi-Layer Perceptrons (MLP) to address these challenges in cerebral vascular occlusion segmentation. This is the first application of ConvNeXtV2 in this domain. The proposed model significantly improves segmentation accuracy, even in low-contrast regions, while maintaining high computational efficiency, which is crucial for real-world clinical applications. To reduce false positives and improve overall accuracy, small lesions (≤5 pixels) were removed in the preprocessing step with the support of expert clinicians. Experimental results on the ISLES 2022 dataset showed superior performance with an Intersection over Union (IoU) of 0.8015 and a Dice coefficient of 0.8894. Comparative analyses indicate that the proposed model achieves higher segmentation accuracy than existing U-Net variants and other methods, offering a promising solution for clinical use.