Vision Mamba models promise transformer-level performance at linear computational cost, but their reliance on serializing 2D images into 1D sequences introduces a critical, yet overlooked, design choice: the patch scan order. In medical imaging, where modalities like brain MRI contain strong anatomical priors, this choice is non-trivial. This paper presents the first systematic study of how scan order impacts MRI segmentation. We introduce Multi-Scan 2D (MS2D), a parameter-free module for Mamba-based architectures that facilitates exploring diverse scan paths without additional computational cost. We conduct a large-scale benchmark of 21 scan strategies on three public datasets (BraTS 2020, ISLES 2022, LGG), covering over 70,000 slices. Our analysis shows conclusively that scan order is a statistically significant factor (Friedman test: $\chi^{2}_{20}=43.9, p=0.0016$), with performance varying by as much as 27 Dice points. Spatially contiguous paths -- simple horizontal and vertical rasters -- consistently outperform disjointed diagonal scans. We conclude that scan order is a powerful, cost-free hyperparameter, and provide an evidence-based shortlist of optimal paths to maximize the performance of Mamba models in medical imaging.

Tractography algorithms leverage diffusion MRI to reconstruct the fibrous architecture of the brain's white matter. Among machine learning approaches, reinforcement learning (RL) has emerged as a promising framework for tractography, outperforming traditional methods in several key aspects. TractOracle-RL, a recent RL-based approach, reduces false positives by incorporating anatomical priors into the training process via a reward-based mechanism. In this paper, we investigate four extensions of the original TractOracle-RL framework by integrating recent advances in RL, and we evaluate their performance across five diverse diffusion MRI datasets. Results demonstrate that combining an oracle with the RL framework consistently leads to robust and reliable tractography, regardless of the specific method or dataset used. We also introduce a novel RL training scheme called Iterative Reward Training (IRT), inspired by the Reinforcement Learning from Human Feedback (RLHF) paradigm. Instead of relying on human input, IRT leverages bundle filtering methods to iteratively refine the oracle's guidance throughout training. Experimental results show that RL methods trained with oracle feedback significantly outperform widely used tractography techniques in terms of accuracy and anatomical validity.

The lenticulostriate arteries (LSAs) supply critical subcortical brain structures and are affected in cerebral small vessel disease (CSVD). Changes in their morphology are linked to cardiovascular risk factors and may indicate early pathology. 7T Time-of-Flight MR angiography (TOF-MRA) enables clear LSA visualisation. We aimed to develop a semi-automated pipeline for quantifying 3D LSA morphology from 7T TOF-MRA in CSVD patients. We used data from a local 7T CSVD study to create a pipeline, LUMEN, comprising two stages: vessel segmentation and LSA quantification. For segmentation, we fine-tuned a deep learning model, DS6, and compared it against nnU-Net and a Frangi-filter pipeline, MSFDF. For quantification, centrelines of LSAs within basal ganglia were extracted to compute branch counts, length, tortuosity, and maximum curvature. This pipeline was applied to 69 subjects, with results compared to traditional analysis measuring LSA morphology on 2D coronal maximum intensity projection (MIP) images. For vessel segmentation, fine-tuned DS6 achieved the highest test Dice score (0.814±0.029) and sensitivity, whereas nnU-Net achieved the best balanced average Hausdorff distance and precision. Visual inspection confirmed that DS6 was most sensitive in detecting LSAs with weak signals. Across 69 subjects, the pipeline with DS6 identified 23.5±8.5 LSA branches. Branch length inside the basal ganglia was 26.4±3.5 mm, and tortuosity was 1.5±0.1. Extracted LSA metrics from 2D MIP analysis and our 3D analysis showed fair-to-moderate correlations. Outliers highlighted the added value of 3D analysis. This open-source deep-learning-based pipeline offers a validated tool quantifying 3D LSA morphology in CSVD patients from 7T-TOF-MRA for clinical research.

Autism spectrum disorder (ASD) affects social interaction, communication, and behavior. Early diagnosis is important as it enables timely intervention that can significantly improve long-term outcomes, but current diagnostic, which rely heavily on behavioral observations and clinical interviews, are often subjective and time-consuming. This study introduces an AI-based approach that uses T1-weighted structural MRI (sMRI) scans, network analysis, and vision transformers to automatically diagnose ASD. sMRI data from 79 ASD patients and 105 healthy controls were obtained from the Autism Brain Imaging Data Exchange (ABIDE) database. Complex network analysis (CNA) features and ViT (Vision Transformer) features were developed for predicting ASD. Five models were developed for each type of features: logistic regression, support vector machine (SVM), gradient boosting (GB), K-nearest neighbors (KNN), and neural network (NN). 25 models were further developed by federating the two sets of 5 models. Model performance was evaluated using accuracy, area under the receiver operating characteristic curve (AUC-ROC), sensitivity, and specificity via fivefold cross-validation. The federate model CNA(KNN)-ViT(NN) achieved highest performance, with accuracy 0.951 ± 0.067, AUC-ROC 0.980 ± 0.020, sensitivity 0.963 ± 0.050, and specificity 0.943 ± 0.047. The performance of the ViT-based models exceeds that of the complex network-based models on 80% of the performance metrics. By federating CNA models, the ViT models can achieve better performance. This study demonstrates the feasibility of using CNA and ViT models for the automated diagnosis of ASD. The proposed CNA(KNN)-ViT(NN) model achieved better accuracy in ASD classification based solely on T1 sMRI images. The proposed method's reliance on widely available T1 sMRI scans highlights its potential for integration into routine clinical examinations, facilitating more efficient and accessible ASD screening.

Glioma is the most common primary malignant brain tumor and preoperative genetic profiling is essential for the management of glioma patients. Our study focused on tumor regions segmentation and predicting the World Health Organization (WHO) grade, isocitrate dehydrogenase (IDH) mutation, and 1p/19q codeletion status using deep learning models on preoperative MRI. To achieve accurate tumor segmentation, we developed an optimal mass transport (OMT) approach to transform irregular MRI brain images into tensors. In addition, we proposed an algebraic preclassification (APC) model utilizing multimode OMT tensor singular value decomposition (SVD) to estimate preclassification probabilities. The fully automated deep learning model named OMT-APC was used for multitask classification. Our study incorporated preoperative brain MRI data from 3,565 glioma patients across 16 datasets spanning Asia, Europe, and America. Among these, 2,551 patients from 5 datasets were used for training and internal validation. In comparison, 1,014 patients from 11 datasets, including 242 patients from The Cancer Genome Atlas (TCGA), were used as independent external test. The OMT segmentation model achieved mean lesion-wise Dice scores of 0.880. The OMT-APC model was evaluated on the TCGA dataset, achieving accuracies of 0.855, 0.917, and 0.809, with AUC scores of 0.845, 0.908, and 0.769 for WHO grade, IDH mutation, and 1p/19q codeletion, respectively, which outperformed the four radiologists in all tasks. These results highlighted the effectiveness of our OMT and tensor SVD-based methods in brain tumor genetic profiling, suggesting promising applications for algebraic and geometric methods in medical image analysis.

Traditional approaches for molecular imaging of Parkinson's disease (PD) in vivo require radioactive isotopes, lengthy scan times, or deliver only low spatial resolution. Recent advances in saturation transfer-based PD magnetic resonance imaging (MRI) have provided biochemical insights, although the image contrast is semi-quantitative and nonspecific. Here, we combined a rapid molecular MRI acquisition paradigm with deep learning based reconstruction for multi-metabolite quantification of glutamate, mobile proteins, semisolid, and mobile macromolecules in an acute MPTP (1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine) mouse model. The quantitative parameter maps are in general agreement with the histology and MR spectroscopy, and demonstrate that semisolid magnetization transfer (MT), amide, and aliphatic relayed nuclear Overhauser effect (rNOE) proton volume fractions may serve as PD biomarkers.

Major depressive disorder (MDD) is a highly prevalent mental health condition with significant public health implications. Early detection is crucial for timely intervention, but current diagnostic methods often rely on subjective clinical assessments, leading to delayed or inaccurate diagnoses. Advances in neuroimaging and machine learning (ML) offer the potential for objective and accurate early detection. This study aimed to develop and validate ML models using multisite functional magnetic resonance imaging data for the early detection of MDD, compare their performance, and evaluate their clinical applicability. We used functional magnetic resonance imaging data from 1200 participants (600 with early-stage MDD and 600 healthy controls) across 3 public datasets. In total, 4 ML models-support vector machine, random forest, gradient boosting machine, and deep neural network-were trained and evaluated using a 5-fold cross-validation framework. Models were assessed for accuracy, sensitivity, specificity, F1-score, and area under the receiver operating characteristic curve. Shapley additive explanations values and activation maximization techniques were applied to interpret model predictions. The deep neural network model demonstrated superior performance with an accuracy of 89% (95% CI 86%-92%) and an area under the receiver operating characteristic curve of 0.95 (95% CI 0.93-0.97), outperforming traditional diagnostic methods by 15% (P<.001). Key predictive features included altered functional connectivity between the dorsolateral prefrontal cortex, anterior cingulate cortex, and limbic regions. The model achieved 78% sensitivity (95% CI 71%-85%) in identifying individuals who developed MDD within a 2-year follow-up period, demonstrating good generalizability across datasets. Our findings highlight the potential of artificial intelligence-driven approaches for the early detection of MDD, with implications for improving early intervention strategies. While promising, these tools should complement rather than replace clinical expertise, with careful consideration of ethical implications such as patient privacy and model biases.

Brain tumor segmentation plays a critical role in clinical diagnosis and treatment planning, yet the variability in imaging quality across different MRI scanners presents significant challenges to model generalization. To address this, we propose the Edge Iterative MRI Lesion Localization System (EdgeIMLocSys), which integrates Continuous Learning from Human Feedback to adaptively fine-tune segmentation models based on clinician feedback, thereby enhancing robustness to scanner-specific imaging characteristics. Central to this system is the Graph-based Multi-Modal Interaction Lightweight Network for Brain Tumor Segmentation (GMLN-BTS), which employs a Modality-Aware Adaptive Encoder (M2AE) to extract multi-scale semantic features efficiently, and a Graph-based Multi-Modal Collaborative Interaction Module (G2MCIM) to model complementary cross-modal relationships via graph structures. Additionally, we introduce a novel Voxel Refinement UpSampling Module (VRUM) that synergistically combines linear interpolation and multi-scale transposed convolutions to suppress artifacts while preserving high-frequency details, improving segmentation boundary accuracy. Our proposed GMLN-BTS model achieves a Dice score of 85.1% on the BraTS2017 dataset with only 4.58 million parameters, representing a 98% reduction compared to mainstream 3D Transformer models, and significantly outperforms existing lightweight approaches. This work demonstrates a synergistic breakthrough in achieving high-accuracy, resource-efficient brain tumor segmentation suitable for deployment in resource-constrained clinical environments.

Precise segmentation of brain tumors from magnetic resonance imaging (MRI) is essential for neuro-oncology diagnosis and treatment planning. Despite advances in deep learning methods, automatic segmentation remains challenging due to tumor morphological heterogeneity and complex three-dimensional spatial relationships. Current techniques primarily rely on visual features extracted from MRI sequences while underutilizing semantic knowledge embedded in medical reports. This research presents a multi-level fusion architecture that integrates pixel-level, feature-level, and semantic-level information, facilitating comprehensive processing from low-level data to high-level concepts. The semantic-level fusion pathway combines the semantic understanding capabilities of Contrastive Language-Image Pre-training (CLIP) models with the spatial feature extraction advantages of 3D U-Net through three mechanisms: 3D-2D semantic bridging, cross-modal semantic guidance, and semantic-based attention mechanisms. Experimental validation on the BraTS 2020 dataset demonstrates that the proposed model achieves an overall Dice coefficient of 0.8567, representing a 4.8% improvement compared to traditional 3D U-Net, with a 7.3% Dice coefficient increase in the clinically important enhancing tumor (ET) region.

Precise segmentation of brain tumors from magnetic resonance imaging (MRI) is essential for neuro-oncology diagnosis and treatment planning. Despite advances in deep learning methods, automatic segmentation remains challenging due to tumor morphological heterogeneity and complex three-dimensional spatial relationships. Current techniques primarily rely on visual features extracted from MRI sequences while underutilizing semantic knowledge embedded in medical reports. This research presents a multi-level fusion architecture that integrates pixel-level, feature-level, and semantic-level information, facilitating comprehensive processing from low-level data to high-level concepts. The semantic-level fusion pathway combines the semantic understanding capabilities of Contrastive Language-Image Pre-training (CLIP) models with the spatial feature extraction advantages of 3D U-Net through three mechanisms: 3D-2D semantic bridging, cross-modal semantic guidance, and semantic-based attention mechanisms. Experimental validation on the BraTS 2020 dataset demonstrates that the proposed model achieves an overall Dice coefficient of 0.8567, representing a 4.8% improvement compared to traditional 3D U-Net, with a 7.3% Dice coefficient increase in the clinically important enhancing tumor (ET) region.