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.

Intracerebral Hemorrhage (ICH) is a devastating form of stroke. Hematoma expansion (HE), growth of the hematoma on interval scans, predicts death and disability. Accurate prediction of HE is crucial for targeted interventions to improve patient outcomes. The black hole sign (BHS) on non-contrast computed tomography (CT) scans is a predictive marker for HE. An automated method to recognize the BHS and predict HE could speed precise patient selection for treatment. In. this paper, we presented a novel framework leveraging self-supervised learning (SSL) techniques for BHS identification on head CT images. A ResNet-50 encoder model was pre-trained on over 1.7 million unlabeled head CT images. Layers for binary classification were added on top of the pre-trained model. The resulting model was fine-tuned using the training data and evaluated on the held-out test set to collect AUC and F1 scores. The evaluations were performed on scan and slice levels. We ran different panels, one using two multi-center datasets for external validation and one including parts of them in the pre-training RESULTS: Our model demonstrated strong performance in identifying BHS when compared with the baseline model. Specifically, the model achieved scan-level AUC scores between 0.75-0.89 and F1 scores between 0.60-0.70. Furthermore, it exhibited robustness and generalizability across an external dataset, achieving a scan-level AUC score of up to 0.85 and an F1 score of up to 0.60, while it performed less well on another dataset with more heterogeneous samples. The negative effects could be mitigated after including parts of the external datasets in the fine-tuning process. This study introduced a novel framework integrating SSL into medical image classification, particularly on BHS identification from head CT scans. The resulting pre-trained head CT encoder model showed potential to minimize manual annotation, which would significantly reduce labor, time, and costs. After fine-tuning, the framework demonstrated promising performance for a specific downstream task, identifying the BHS to predict HE, upon comprehensive evaluation on diverse datasets. This approach holds promise for enhancing medical image analysis, particularly in scenarios with limited data availability. ICH = Intracerebral Hemorrhage; HE = Hematoma Expansion; BHS = Black Hole Sign; CT = Computed Tomography; SSL = Self-supervised Learning; AUC = Area Under the receiver operator Curve; CNN = Convolutional Neural Network; SimCLR = Simple framework for Contrastive Learning of visual Representation; HU = Hounsfield Unit; CLAIM = Checklist for Artificial Intelligence in Medical Imaging; VNA = Vendor Neutral Archive; DICOM = Digital Imaging and Communications in Medicine; NIfTI = Neuroimaging Informatics Technology Initiative; INR = International Normalized Ratio; GPU= Graphics Processing Unit; NIH= National Institutes of Health.

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.

Brain tumours continue to be a primary cause of worldwide death, highlighting the critical need for effective and accurate diagnostic tools. This article presents MK-YOLOv8, an innovative lightweight deep learning framework developed for the real-time detection and categorization of brain tumours from MRI images. Based on the YOLOv8 architecture, the proposed model incorporates Ghost Convolution, the C3Ghost module, and the SPPELAN module to improve feature extraction and substantially decrease computational complexity. An x-small object detection layer has been added, supporting precise detection of small and x-small tumours, which is crucial for early diagnosis. Trained on the Figshare Brain Tumour (FBT) dataset comprising (3,064) MRI images, MK-YOLOv8 achieved a mean Average Precision (mAP) of 99.1% at IoU (0.50) and 88.4% at IoU (0.50-0.95), outperforming YOLOv8 (98% and 78.8%, respectively). Glioma recall improved by 26%, underscoring the enhanced sensitivity to challenging tumour types. With a computational footprint of only 96.9 GFLOPs (representing 37.5% of YOYOLOv8x'sFLOPs) and utilizing 12.6 million parameters, a mere 18.5% of YOYOLOv8's parameters, MK-YOLOv8 delivers high efficiency with reduced resource demands. Also, it trained on the Br35H dataset (801 images) to guarantee the model's robustness and generalization; it achieved a mAP of 98.6% at IoU (0.50). The suggested model operates at 62 frames per second (FPS) and is suited for real-time clinical processes. These developments establish MK-YOLOv8 as an innovative framework, overcoming challenges in tiny tumour identification and providing a generalizable, adaptable, and precise detection approach for brain tumour diagnostics in clinical settings.

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.

A cortical hierarchical architecture is vital for encoding and integrating sensorimotor-to-cognitive information. However, whether this gradient structure is disrupted in end-stage renal disease (ESRD) patients and how this disruption provides valuable information for potential clinical symptoms remain unknown. We prospectively enrolled 77 ESRD patients and 48 healthy controls. Using resting-state functional magnetic resonance imaging, we studied ESRD-related hierarchical alterations. The Neurosynth platform and machine-learning models with 10-fold cross-validation were applied. ESRD patients had abnormal gradient metrics in core regions of the default mode network, sensorimotor network, and frontoparietal network. These changes correlated with creatinine, depression, and cognitive functions. A logistic regression classifier achieved a maximum performance of 84.8% accuracy and 0.901 area under the ROC curve (AUC). Our results highlight hierarchical imbalances in ESRD patients that correlate with diverse cognitive deficits, which may be used as potential neuroimaging markers for clinical symptoms.

Cerebral aneurysm is a silent yet prevalent condition that affects a substantial portion of the global population. Aneurysms can develop due to various factors and present differently, necessitating diverse treatment approaches. Choosing the appropriate treatment upon diagnosis is paramount, as the severity of the disease dictates the course of action. The vulnerability of an aneurysm, particularly in the circle of Willis, is a critical concern; rupture can lead to irreversible consequences, including death. The primary objective of this study is to predict the rupture status of cerebral aneurysms using a comprehensive dataset that includes clinical, morphological, and hemodynamic data extracted from blood flow simulations of patients with actual vessels. Our goal is to provide valuable insights that can aid in treatment decision-making and potentially save the lives of future patients. Diagnosing and predicting the rupture status of aneurysms based solely on brain scans poses a significant challenge, often with limited accuracy, even for experienced physicians. However, harnessing statistical and machine learning (ML) techniques can enhance rupture prediction and treatment strategy selection. We employed a diverse set of supervised and unsupervised algorithms, training them on a database comprising over 700 cerebral aneurysms, which included 55 different parameters: 3 clinical, 35 morphological, and 17 hemodynamic features. Two of our models including stochastic gradient descent (SGD) and multi-layer perceptron (MLP) achieved a maximum area under the curve (AUC) of 0.86, a precision rate of 0.86, and a recall rate of 0.90 for prediction of cerebral aneurysm rupture. Given the sensitivity of the data and the critical nature of the condition, recall is a more vital parameter than accuracy and precision; our study achieved an acceptable recall score. Key features for rupture prediction included ellipticity index, low shear area ratio, and irregularity. Additionally, a one-dimensional CNN model predicted rupture status along a continuous spectrum, achieving 0.78 accuracy on the testing dataset, providing nuanced insights into rupture propensity.

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.

<i>Objective</i>. Personalized transcranial magnetic stimulation (TMS) requires individualized head models that incorporate non-uniform conductivity to enable target-specific stimulation. Accurately estimating non-uniform conductivity in individualized head models remains a challenge due to the difficulty of obtaining precise ground truth data. To address this issue, we have developed a novel transfer learning-based approach for automatically estimating non-uniform conductivity in a human head model with limited data.<i>Approach</i>. The proposed method complements the limitations of the previous conductivity network (CondNet) and improves the conductivity estimation accuracy. This method generates a segmentation model from T1- and T2-weighted magnetic resonance images, which is then used for conductivity estimation via transfer learning. To enhance the model's representation capability, a Transformer was incorporated into the segmentation model, while the conductivity estimation model was designed using a combination of Attention Gates and Residual Connections, enabling efficient learning even with a small amount of data.<i>Main results</i>. The proposed method was evaluated using 1494 images, demonstrating a 2.4% improvement in segmentation accuracy and a 29.1% increase in conductivity estimation accuracy compared with CondNet. Furthermore, the proposed method achieved superior conductivity estimation accuracy even with only three training cases, outperforming CondNet, which was trained on an adequate number of cases. The conductivity maps generated by the proposed method yielded better results in brain electrical field simulations than CondNet.<i>Significance</i>. These findings demonstrate the high utility of the proposed method in brain electrical field simulations and suggest its potential applicability to other medical image analysis tasks and simulations.