Timely intervention for Alzheimers disease (AD) requires early detection. The development of immunotherapies targeting amyloid-beta and tau underscores the need for accessible, time-efficient biomarkers for early diagnosis. Here, we directly applied our previously developed MRI-based deep learning model for AD to the large Chinese SILCODE cohort (722 participants, 1,105 brain MRI scans). The model -- initially trained on North American data -- demonstrated robust cross-ethnic generalization, without any retraining or fine-tuning, achieving an AUC of 91.3% in AD classification with a sensitivity of 95.2%. It successfully identified 86.7% of individuals at risk of AD progression more than 5 years in advance. Individuals identified as high-risk exhibited significantly shorter median progression times. By integrating an interpretable deep learning brain risk map approach, we identified AD brain subtypes, including an MCI subtype associated with rapid cognitive decline. The models risk scores showed significant correlations with cognitive measures and plasma biomarkers, such as tau proteins and neurofilament light chain (NfL). These findings underscore the exceptional generalizability and clinical utility of MRI-based deep learning models, especially in large and diverse populations, offering valuable tools for early therapeutic intervention. The model has been made open-source and deployed to a free online website for AD risk prediction, to assist in early screening and intervention.

A three-dimensional convolutional neural network was developed to classify T1-weighted brain MRI scans as healthy or Alzheimer. The network comprises 3D convolution, pooling, batch normalization, dense ReLU layers, and a sigmoid output. Using stochastic noise injection and five-fold cross-validation, the model achieved test set accuracy of 0.912 and area under the ROC curve of 0.961, an improvement of approximately 0.027 over resizing alone. Sensitivity and specificity both exceeded 0.90. These results align with prior work reporting up to 0.10 gain via synthetic augmentation. The findings demonstrate the effectiveness of simple augmentation for 3D MRI classification and motivate future exploration of advanced augmentation methods and architectures such as 3D U-Net and vision transformers.

Our study presents PNN-UNet as a method for constructing deep neural networks that replicate the planarian neural network (PNN) structure in the context of 3D medical image data. Planarians typically have a cerebral structure comprising two neural cords, where the cerebrum acts as a coordinator, and the neural cords serve slightly different purposes within the organism's neurological system. Accordingly, PNN-UNet comprises a Deep-UNet and a Wide-UNet as the nerve cords, with a densely connected autoencoder performing the role of the brain. This distinct architecture offers advantages over both monolithic (UNet) and modular networks (Ensemble-UNet). Our outcomes on a 3D MRI hippocampus dataset, with and without data augmentation, demonstrate that PNN-UNet outperforms the baseline UNet and several other UNet variants in image segmentation.

Magnetic Resonance Imaging (MRI) is the gold standard in countless diagnostic procedures, yet hardware complexity, long scans, and cost preclude rapid screening and point-of-care use. We introduce Imageless Magnetic Resonance Diagnosis (IMRD), a framework that bypasses k-space sampling and image reconstruction by analyzing raw one-dimensional MR signals. We identify potentially impactful embodiments where IMRD requires only optimized pulse sequences for time-domain contrast, minimal low-field hardware, and pattern recognition algorithms to answer clinical closed queries and quantify lesion burden. As a proof of concept, we simulate multiple sclerosis lesions in silico within brain phantoms and deploy two extremely fast protocols (approximately 3 s), with and without spatial information. A 1D convolutional neural network achieves AUC close to 0.95 for lesion detection and R2 close to 0.99 for volume estimation. We also perform robustness tests under reduced signal-to-noise ratio, partial signal omission, and relaxation-time variability. By reframing MR signals as direct diagnostic metrics, IMRD paves the way for fast, low-cost MR screening and monitoring in resource-limited environments.

Low back pain (LBP) is a prevalent pain condition whose persistence can lead to changes in the brain regions responsible for sensory, cognitive, attentional, and emotional processing. Previous neuroimaging studies have identified various structural and functional abnormalities in patients with LBP; however, how the static and dynamic large-scale functional network connectivity (FNC) of the brain is affected in these patients remains unclear. Forty-one patients with chronic low back pain (cLBP) and 42 healthy controls underwent resting-state functional MRI scanning. The independent component analysis method was employed to extract the resting-state networks. Subsequently, we calculate and compare between groups for static intra- and inter-network functional connectivity. In addition, we investigated the differences between dynamic functional network connectivity and dynamic temporal metrics between cLBP patients and healthy controls. Finally, we tried to distinguish cLBP patients from healthy controls by support vector machine method. The results showed that significant reductions in functional connectivity within the network were found within the DMN,DAN, and ECN in cLBP patients. Significant between-group differences were also found in static FNC and in each state of dynamic FNC. In addition, in terms of dynamic temporal metrics, fraction time and mean dwell time were significantly altered in cLBP patients. In conclusion, our study suggests the existence of static and dynamic large-scale brain network alterations in patients with cLBP. The findings provide insights into the neural mechanisms underlying various brain function abnormalities and altered pain experiences in patients with cLBP.

Artificial intelligence (AI) has been proposed to assist radiologists in reporting multiparametric magnetic resonance imaging (mpMRI) of the prostate. We evaluate the diagnostic performance of radiologists with different levels of experience when reporting mpMRI with the support of available AI-based software (Quantib Prostate). This is a single-center study (NCT06298305) involving 110 patients. Those with a positive mpMRI (PI-RADS ≥ 3) underwent targeted plus systematic biopsy (TBx plus SBx), while those with a negative mpMRI but a high clinical suspicion of prostate cancer (PCa) underwent SBx. Three readers with different levels of experience, identified as R1, R2, and R3 reviewed all mpMRI. Inter-reader agreement among the three readers with or without the assistance of Quantib Prostate as well as sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and diagnostic accuracy for the detection of clinically significant PCa (csPCa) were assessed. 102 patients underwent prostate biopsy and the csPCa detection rate was 47%. Using Quantib Prostate resulted in an increased number of lesions identified for R3 (101 vs. 127). Inter-reader agreement slightly increased when using Quantib Prostate from 0.37 to 0.41 without vs. with Quantib Prostate, respectively. PPV, NPV and diagnostic accuracy (measured by the area under the curve [AUC]) of R3 improved (0.51 vs. 0.55, 0.65 vs.0.82 and 0.56 vs. 0.62, respectively). Conversely, no changes were observed for R1 and R2. Using Quantib Prostate did not enhance the detection rate of csPCa for readers with some experience in prostate imaging. However, for an inexperienced reader, this AI-based software is demonstrated to improve the performance. Name of registry: clinicaltrials.gov. NCT06298305. Date of registration: 2022-09.

Cardiovascular diseases are the number one cause of death globally, making cardiac magnetic resonance image segmentation a popular research topic. Existing schemas relying on manual user interaction or semi-automatic segmentation are infeasible when dealing thousands of cardiac MRI studies. Thus, we proposed a full automatic and robust algorithm for large-scale cardiac MRI segmentation by combining the advantages of deep learning localization and 3D-ASM restriction. The proposed method comprises several key techniques: 1) a hybrid network integrating CNNs and Transformer as a encoder with the EFG (Edge feature guidance) module (named as CTr-HNs) to localize the target regions of the cardiac on MRI images, 2) initial shape acquisition by alignment of coarse segmentation contours to the initial surface model of 3D-ASM, 3) refinement of the initial shape to cover all slices of MRI in the short axis by complex transformation. The datasets used are from the UK BioBank and the CAP (Cardiac Atlas Project). In cardiac coarse segmentation experiments on MR images, Dice coefficients (Dice), mean contour distances (MCD), and mean Hausdorff distances (HD95) are used to evaluate segmentation performance. In SPASM experiments, Point-to-surface (P2S) distances, Dice score are compared between automatic results and ground truth. The CTr-HNs from our proposed method achieves Dice coefficients (Dice), mean contour distances (MCD), and mean Hausdorff distances (HD95) of 0.95, 0.10 and 1.54 for the LV segmentation respectively, 0.88, 0.13 and 1.94 for the LV myocardium segmentation, and 0.91, 0.24 and 3.25 for the RV segmentation. The overall P2S errors from our proposed schema is 1.45 mm. For endocardium and epicardium, the Dice scores are 0.87 and 0.91 respectively. Our experimental results show that the proposed schema can automatically analyze large-scale quantification from population cardiac images with robustness and accuracy.

This paper introduces 3D-QTRNet, a novel quantum-inspired neural network for volumetric medical image segmentation. Unlike conventional CNNs, which suffer from slow convergence and high complexity, and QINNs, which are limited to grayscale segmentation, our approach leverages qutrit encoding and tensor ring decomposition. These techniques improve segmentation accuracy, optimize memory usage, and accelerate model convergence. The proposed model demonstrates superior performance on the BRATS19 and Spleen datasets, outperforming state-of-the-art CNN and quantum models in terms of Dice similarity and segmentation precision. This work bridges the gap between quantum computing and medical imaging, offering a scalable solution for real-world applications.

This study investigates the hypothesis that chronic insomnia disorder (CID) is characterized by sex-specific changes in resting-state functional connectivity (rsFC), with certain molecular mechanisms potentially influencing CID's pathophysiology by altering rsFC in relevant networks. Utilizing a resting-state functional magnetic resonance imaging (fMRI) dataset of 395 participants, including 199 CID patients and 196 healthy controls, we examined sex-specific rsFC effects, particularly in the default mode network (DMN) and five insomnia-genetically vulnerable regions of interest (ROIs). By integrating gene expression data from the Allen Human Brain Atlas, we identified genes linked to these sex-specific rsFC alterations and conducted enrichment analysis to uncover underlying molecular mechanisms. Additionally, we simulated the impact of sex differences in rsFC with different sex compositions in our dataset and employed machine learning classifiers to distinguish CID from healthy controls based on sex-specific rsFC data. We identified both shared and sex-specific rsFC changes in the DMN and the five genetically vulnerable ROIs, with gene expression variations associated with these sex-specific connectivity differences. Enrichment analysis highlighted genes involved in synaptic signaling, ion channels, and immune function as potential contributors to CID pathophysiology through their influence on connectivity. Furthermore, our findings demonstrate that different sex compositions significantly affect study outcomes and higher diagnostic performance in sex-specific rsFC data than combined sex. This study uncovered both shared and sex-specific connectivity alterations in CID, providing molecular insights into its pathophysiology and suggesting considering sex differences in future fMRI-based diagnostic and treatment strategies.

The corticospinal tract (CST) is the primary neural pathway responsible for voluntary motor functions, and preoperative CST reconstruction is crucial for preserving nerve functions during neurosurgery. Diffusion magnetic resonance imaging-based tractography is the only noninvasive method to preoperatively reconstruct CST in clinical practice. However, for the largesize bundle CST with complex fiber geometry (fanning fibers), reconstructing its full extent remains challenging with local-derived methods without incorporating global information. Especially in the presence of tumors, the mass effect and partial volume effect cause abnormal diffusion signals. In this work, a CST reconstruction tractography method based on a novel direction filter was proposed, designed to ensure robust CST reconstruction in the clinical dataset with tumors. A direction filter based on a fourth-order differential equation was introduced for global direction estimation. By considering the spatial consistency and leveraging anatomical prior knowledge, the direction filter was computed by minimizing the energy between the target directions and initial fiber directions. On the basis of the new directions corresponding to CST obtained by the direction filter, the fiber tracking method was implemented to reconstruct the fiber trajectory. Additionally, a deep learning-based method along with tractography template prior information was employed to generate the regions of interest (ROIs) and initial fiber directions. Experimental results showed that the proposed method yields higher valid connections and lower no connections and exhibits the fewest broken fibers and short-connected fibers. The proposed method offers an effective tool to enhance CST-related surgical outcomes by optimizing tumor resection and preserving CST.