We aim to develop an interactive segmentation model that can offer accuracy and reliability for the segmentation of the irregularly shaped spinal cord and canal in degenerative cervical myelopathy (DCM) through manual click and model refinement. A dataset of 1444 frames from 294 magnetic resonance imaging records of DCM patients was used and we developed two different segmentation models for comparison : auto-segmentation and interactive segmentation. The former was based on U-Net and utilized a pretrained ConvNeXT-tiny as its encoder. For the latter, we employed an interactive segmentation model structured by SimpleClick, a large model that utilizes a vision transformer as its backbone, together with simple fine-tuning. The segmentation performance of the two models were compared in terms of their Dice scores, mean intersection over union (mIoU), Average Precision and Hausdorff distance. The efficiency of the interactive segmentation model was evaluated by the number of clicks required to achieve a target mIoU. Our model achieved better scores across all four-evaluation metrics for segmentation accuracy, showing improvements of +6.4%, +1.8%, +3.7%, and -53.0% for canal segmentation, and +11.7%, +6.0%, +18.2%, and -70.9% for cord segmentation with 15 clicks, respectively. The required clicks for the interactive segmentation model to achieve a 90% mIoU for spinal canal with cord cases and 80% mIoU for spinal cord cases were 11.71 and 11.99, respectively. We found that the interactive segmentation model significantly outperformed the auto-segmentation model. By incorporating simple manual inputs, the interactive model effectively identified regions of interest, particularly in the complex and irregular shapes of the spinal cord, demonstrating both enhanced accuracy and adaptability.

Traditional brain connectivity studies have focused mainly on structural connectivity, often relying on tractography with diffusion MRI (dMRI) to reconstruct white matter pathways. In parallel, studies of functional connectivity have examined correlations in brain activity using fMRI. However, emerging methodologies are advancing our understanding of brain networks. Here we explore advanced connectivity approaches beyond conventional tractography, focusing on dMRI morphometry and the integration of structural and functional connectivity analysis. dMRI morphometry enables quantitative assessment of white matter pathway volumes through statistical comparison with normative populations, while functional connectivity reveals network organization that is not restricted to direct anatomical connections. More recently, approaches that combine diffusion tensor imaging (DTI) with functional correlation tensor (FCT) analysis have been introduced, and these complementary methods provide new perspectives into brain structure-function relationships. Together, such approaches have important implications for neurodevelopmental and neurological disorders as well as brain plasticity. The integration of these methods with artificial intelligence techniques have the potential to support both basic neuroscience research and clinical applications.

While deep learning has catalyzed breakthroughs across numerous domains, its broader adoption in clinical settings is inhibited by the costly and time-intensive nature of data acquisition and annotation. To further facilitate medical machine learning, we present an ultrasound dataset of 10,223 brightness-mode (B-mode) images consisting of sagittal slices of porcine spinal cords (N = 25) before and after a contusion injury. We additionally benchmark the performance metrics of several state-of-the-art object detection algorithms to localize the site of injury and semantic segmentation models to label the anatomy for comparison and creation of task-specific architectures. Finally, we evaluate the zero-shot generalization capabilities of the segmentation models on human ultrasound spinal cord images to determine whether training on our porcine dataset is sufficient for accurately interpreting human data. Our results show that the YOLOv8 detection model outperforms all evaluated models for injury localization, achieving a mean Average Precision (mAP50-95) score of 0.606. Segmentation metrics indicate that the DeepLabv3 segmentation model achieves the highest accuracy on unseen porcine anatomy, with a Mean Dice score of 0.587, while SAMed achieves the highest mean Dice score generalizing to human anatomy (0.445). To the best of our knowledge, this is the largest annotated dataset of spinal cord ultrasound images made publicly available to researchers and medical professionals, as well as the first public report of object detection and segmentation architectures to assess anatomical markers in the spinal cord for methodology development and clinical applications.

Headaches are a nearly universal human experience traditionally diagnosed based solely on symptoms. Recent advances in imaging techniques and artificial intelligence (AI) have enabled the development of automated headache detection systems, which can enhance clinical diagnosis, especially when symptom-based evaluations are insufficient. Current AI models often require extensive data, limiting their clinical applicability where data availability is low. However, deep learning models, particularly pre-trained ones and fine-tuned with smaller, targeted datasets can potentially overcome this limitation. By leveraging BioMedCLIP, a pre-trained foundational model combining a vision transformer (ViT) image encoder with PubMedBERT text encoder, we fine-tuned the pre-trained ViT model for the specific purpose of classifying headaches and detecting biomarkers from brain MRI data. The dataset consisted of 721 individuals: 424 healthy controls (HC) from the IXI dataset and 297 local participants, including migraine sufferers (n = 96), individuals with acute post-traumatic headache (APTH, n = 48), persistent post-traumatic headache (PPTH, n = 49), and additional HC (n = 104). The model achieved high accuracy across multiple balanced test sets, including 89.96% accuracy for migraine versus HC, 88.13% for APTH versus HC, and 83.13% for PPTH versus HC, all validated through five-fold cross-validation for robustness. Brain regions identified by Gradient-weighted Class Activation Mapping analysis as responsible for migraine classification included the postcentral cortex, supramarginal gyrus, superior temporal cortex, and precuneus cortex; for APTH, rostral middle frontal and precentral cortices; and, for PPTH, cerebellar cortex and precentral cortex. To our knowledge, this is the first study to leverage a multimodal biomedical foundation model in the context of headache classification and biomarker detection using structural MRI, offering complementary insights into the causes and brain changes associated with headache disorders.

To evaluate the image quality and clinical utility of ultra-fast T2-weighted imaging (UF-T2WI), which acquires all slice data in 7 s using a single-shot turbo spin-echo technique combined with dual-type deep learning (DL) reconstruction, incorporating DL-based image denoising and super-resolution processing, by comparing UF-T2WI with conventional T2WI. We analyzed data from 38 patients who underwent both conventional T2WI and UF-T2WI with the dual-type DL-based image reconstruction. Two board-certified radiologists independently performed blinded qualitative assessments of the patients' images obtained with UF-T2WI with DL and conventional T2WI, evaluating the overall image quality, anatomical structure visibility, and levels of noise and artifacts. In cases that included central nervous system diseases, the lesions' delineation was also assessed. The quantitative analysis included measurements of signal-to-noise ratios in white and gray matter and the contrast-to-noise ratio between gray and white matter. Compared to conventional T2WI, UF-T2WI with DL received significantly higher ratings for overall image quality and lower noise and artifact levels (p < 0.001 for both readers). The anatomical visibility was significantly better in UF-T2WI for one reader, with no significant difference for the other reader. The lesion visibility in UF-T2WI was comparable to that in conventional T2WI. Quantitatively, the SNRs and CNRs were all significantly higher in UF-T2WI than conventional T2WI (p < 0.001). The combination of SSTSE with dual-type DL reconstruction allows for the acquisition of clinically acceptable T2WI images in just 7 s. This technique shows strong potential to reduce MRI scan times and improve clinical workflow efficiency.

Deep learning has significantly advanced the field of computer vision; however, developing models that generalize effectively across diverse image domains remains a major research challenge. In this study, we introduce DeepFreqNet, a novel deep neural architecture specifically designed for high-performance multi-domain image classification. The innovative aspect of DeepFreqNet lies in its combination of three powerful components: multi-scale feature extraction for capturing patterns at different resolutions, depthwise separable convolutions for enhanced computational efficiency, and residual connections to maintain gradient flow and accelerate convergence. This hybrid design improves the architecture's ability to learn discriminative features and ensures scalability across domains with varying data complexities. Unlike traditional transfer learning models, DeepFreqNet adapts seamlessly to diverse datasets without requiring extensive reconfiguration. Experimental results from nine benchmark datasets, including MRI tumor classification, blood cell classification, and sign language recognition, demonstrate superior performance, achieving classification accuracies between 98.96% and 99.97%. These results highlight the effectiveness and versatility of DeepFreqNet, showcasing a significant improvement over existing state-of-the-art methods and establishing it as a robust solution for real-world image classification challenges.

Brain metastases (BMs) are the most common intracranial tumors and stereotactic radiotherapy improved the life quality of patient with BMs, while it requires more time and experience to delineate BMs precisely by oncologists. Deep Learning techniques showed promising applications in radiation oncology. Therefore, we proposed a deep learning-based automatic segmentation of primary tumor volumes for BMs in this work. Magnetic resonance imaging (MRI) of 158 eligible patients with BMs was retrospectively collected in the study. An automatic segmentation model called BUC-Net based on U-Net with cascade strategy and bottleneck module was proposed for auto-segmentation of BMs. The proposed model was evaluated using geometric metrics (Dice similarity coefficient (DSC), 95% Hausdorff distance (HD95) and Average surface distance (ASD)) for the performance of automatic segmentation, and Precision recall (PR) and Receiver operating characteristic (ROC) curve for the performance of automatic detection, and relative volume difference (RVD) for evaluation. Compared with U-Net and U-Net Cascade, the BUC-Net achieved the average DSC of 0.912 and 0.797, HD95 of 0.901 mm and 0.922 mm, ASD of 0.332 mm and 0.210 mm for the evaluation of automatic segmentation in binary classification and multiple classification, respectively. The average Area Under Curve (AUC) of 0.934 and 0.835 for (Precision-Recall) PR and Receiver Operating Characteristic (ROC) curve for the tumor detection. It also performed the minimum RVD with various diameter ranges in the clinical evaluation. The BUC-Net can achieve the segmentation and modification of BMs for one patient within 10 min, instead of 3-6 h by the conventional manual modification, which is conspicuous to improve the efficiency and accuracy of radiation therapy.

This study aimed to validate the utility of commercially available vendor-neutral deep learning (DL) image enhancement software for improving the image quality of multiparametric MRI for gliomas in a multinational setting. A total of 294 patients from three institutions (NYU, Severance, and SNUH) who underwent glioma MRI protocols were included in this retrospective study. DL image enhancement was performed on T2-weighted (T2W), T2 FLAIR, and postcontrast T1-weighted (T1W) imaging using commercially available DL image enhancement software. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated for both conventional and DL-enhanced images. Three neuroradiologists, one from each institution, independently evaluated the following image quality parameters in both images using a 5-point scale: overall image quality, noise, gray-white matter differentiation, truncation artifact, motion artifact, pulsation artifact, and main lesion conspicuity. The quantitative and qualitative image parameters were compared between conventional and DL-enhanced images. Compared with conventional images, DL-enhanced images showed significantly higher SNRs and CNRs in T2W, T2 FLAIR, and postcontrast T1W imaging (all P < 0.001). The average scores of radiologist assessments in overall image quality, noise, gray-white matter differentiation, and main lesion conspicuity were significantly higher for DL-enhanced images than conventional images in T2W, T2 FLAIR, and postcontrast T1W imaging (all P < 0.001). Regarding artifacts, truncation artifacts decreased (all P < 0.001), while pre-existing motion and pulsation artifacts were not further exaggerated in most structural MRI sequences. In conclusion, DL image enhancement using commercially available vendor-neutral software improved image quality and reduced truncation artifacts in multiparametric glioma MRI.

Multiple sclerosis (MS) is a chronic neurological condition that affects approximately 150 000 people in the UK and presents a significant healthcare burden, including the high costs of disease-modifying treatments (DMTs). DMTs have substantially reduced the risk of relapse and moderately reduced disability progression. Patients exhibit a wide range of responses to available DMTs. The Predicting Optimal INdividualised Treatment response in MS (POINT-MS) cohort was established to predict the individual treatment response by integrating comprehensive clinical phenotyping with imaging, serum and genetic biomarkers of disease activity and progression. Here, we present the baseline characteristics of the cohort and provide an overview of the study design, laying the groundwork for future analyses. POINT-MS is a prospective, observational research cohort and biobank of 781 adult participants with a diagnosis of MS who consented to study enrolment on initiation of a DMT at the Queen Square MS Centre (National Hospital of Neurology and Neurosurgery, University College London Hospital NHS Trust, London) between 01/07/2019 and 31/07/2024. All patients were invited for clinical assessments, including the expanded disability status scale (EDSS) score, brief international cognitive assessment for MS and various patient-reported outcome measures (PROMs). They additionally underwent MRI at 3T, optical coherence tomography and blood tests (for genotyping and serum biomarkers quantification), at baseline (i.e., within 3 months from commencing a DMT), and between 6-12 (re-baseline), 18-24, 30-36, 42-48 and 54-60 months after DMT initiation. 748 participants provided baseline data. They were mostly female (68%) and White (75%) participants, with relapsing-remitting MS (94.3%), and with an average age of 40.8 (±10.9) years and a mean disease duration of 7.9 (±7.4) years since symptom onset. Despite low disability (median EDSS 2.0), cognitive impairment was observed in 40% of participants. Most patients (98.4%) had at least one comorbidity. At study entry, 59.2% were treatment naïve, and 83.2% initiated a high-efficacy DMT. Most patients (76.4%) were in either full- or part-time employment. PROMs indicated heterogeneous impairments in physical and mental health, with a greater psychological than physical impact and with low levels of fatigue. When baseline MRI scans were compared with previous scans (available in 668 (89%) patients; mean time since last scan 9±8 months), 26% and 8.5% of patients had at least one new brain or spinal cord lesion at study entry, respectively. Patients showed a median volume of brain lesions of 6.14 cm³, with significant variability among patients (CI 1.1 to 34.1). When brain tissue volumes z-scores were obtained using healthy subjects (N=113, (mean age 42.3 (± 11.8) years, 61.9% female)) from a local MRI database, patients showed a slight reduction in the volumes of the whole grey matter (-0.16 (-0.22 to -0.09)), driven by the deep grey matter (-0.47 (-0.55 to -0.40)), and of the whole white matter (-0.18 (-0.28 to -0.09)), but normal cortical grey matter volumes (0.10 (0.05 to 0.15)). The mean upper cervical spinal cord cross-sectional area (CSA), as measured from volumetric brain scans, was 62.3 (SD 7.5) mm². When CSA z-scores were obtained from the same healthy subjects used for brain measures, patients showed a slight reduction in CSA (-0.15 (-0.24 to -0.10)). Modelling with both standard statistics and machine learning approaches is currently planned to predict individualised treatment response by integrating all the demographic, socioeconomic, clinical data with imaging, genetic and serum biomarkers. The long-term output of this research is a stratification tool that will guide the selection of DMTs in clinical practice on the basis of the individual prognostic profile. We will complete long-term follow-up data in 4 years (January 2029). The biobank and MRI repository will be used for collaborative research on the mechanisms of disability in MS.

Neuroimaging studies of brain function are important research methods widely applied to stroke patients. Currently, a large number of studies have focused on functional imaging of the gray matter cortex. Relevant research indicates that certain areas of the gray matter cortex in stroke patients exhibit abnormal brain activity during resting state. However, studies on brain function based on white matter remain insufficient. The changes in functional connectivity caused by stroke in white matter, as well as the repair or compensation mechanisms of white matter function after stroke, are still unclear. The aim of this study is to investigate and demonstrate the changes in brain functional connectivity activity in the white matter region of stroke patients. Revealing the recombination characteristics of white matter functional networks after stroke, providing potential biomarkers for rehabilitation therapy Provide new clinical insights for the rehabilitation and treatment of stroke patients. We recruited 36 stroke patients and 36 healthy controls for resting-state functional magnetic resonance imaging (rs-fMRI). Regional Homogeneity (ReHo) and Degree Centrality (DC), which are sensitive to white matter functional abnormalities, were selected as feature vectors. ReHo reflects local neuronal synchrony, while DC quantifies global network hub properties. The combination of both effectively characterizes functional changes in white matter. ReHo evaluates the functional consistency of different white matter regions by calculating the activity similarity between adjacent brain regions. Additionally, DC analysis of white matter was used to investigate the connectivity patterns and organizational principles of functional networks between white matter regions. This was achieved by calculating the number of connections in each brain region to identify changes in neural activation of white matter regions that significantly impact the brain network. Furthermore, ReHo and DC metrics were used as feature vectors for classification using machine learning algorithms. The results indicated significant differences in white matter DC and ReHo values between stroke patients and healthy controls. In the two-sample t-test analysis of white matter DC, stroke patients showed a significant reduction in DC values in the corpus callosum genu (GCC), corpus callosum body (BCC), and left anterior corona radiata (ACRL) regions (GCC: 0.143 vs. 1.024; BCC: 0.238 vs. 1.143; ACRL: 0.143 vs. 0.821, p < 0.001). However, an increase in DC values was observed in the left superior longitudinal fasciculus (SLF_L) region (1.190 vs. 0.190, p < 0.001). In the two-sample t-test analysis of white matter ReHo, stroke patients exhibited a decrease in ReHo values in the GCC and BCC regions (GCC: 0.859 vs. 1.375; BCC: 1.156 vs. 1.687, p < 0.001), indicating values lower than those in the healthy control group. Using leave-one-out cross-validation (LOOCV) to evaluate the white matter DC and ReHo feature values through SVM classification models for stroke patients and healthy controls, the DC classification AUC was 0.89, and the ReHo classification AUC reached 0.98. These results suggest that the features possess validity and discriminative power. These findings suggest alterations in functional connectivity in specific white matter regions following stroke. Specifically, we observed a weakening of functional connectivity in the genu of the corpus callosum (GCC), the body of the corpus callosum (BCC), and the left anterior corona radiata (ACR_L) regions, while compensatory functional connectivity was enhanced in the left superior longitudinal fasciculus (SLF_L) region. These findings reveal the reorganization characteristics of white matter functional networks after stroke, which may provide potential biomarkers for the rehabilitation treatment of stroke patients and offer new clinical insights for their rehabilitation and treatment.