Predicting the spatio-temporal progression of brain tumors is essential for guiding clinical decisions in neuro-oncology. We propose a hybrid mechanistic learning framework that combines a mathematical tumor growth model with a guided denoising diffusion implicit model (DDIM) to synthesize anatomically feasible future MRIs from preceding scans. The mechanistic model, formulated as a system of ordinary differential equations, captures temporal tumor dynamics including radiotherapy effects and estimates future tumor burden. These estimates condition a gradient-guided DDIM, enabling image synthesis that aligns with both predicted growth and patient anatomy. We train our model on the BraTS adult and pediatric glioma datasets and evaluate on 60 axial slices of in-house longitudinal pediatric diffuse midline glioma (DMG) cases. Our framework generates realistic follow-up scans based on spatial similarity metrics. It also introduces tumor growth probability maps, which capture both clinically relevant extent and directionality of tumor growth as shown by 95th percentile Hausdorff Distance. The method enables biologically informed image generation in data-limited scenarios, offering generative-space-time predictions that account for mechanistic priors.

Intraoperative tumor imaging is critical to achieving maximal safe resection during neurosurgery, especially for low-grade glioma resection. Given the convenience of ultrasound as an intraoperative imaging modality, but also the limitations of the ultrasound modality and the time-consuming process of manual tumor segmentation, we propose a learning-based model for the accurate segmentation of low-grade gliomas in ultrasound images. We developed a novel U-net-based architecture adopting the block architecture of the ConvNext V2 model, titled U-ConvNext, which also incorporates various architectural improvements including global response normalization, fine-tuned kernel sizes, and inception layers. We also adopted the CutMix data augmentation technique for semantic segmentation, aiming for enhanced texture detection. Conformal segmentation, a novel approach to conformal prediction for binary semantic segmentation, was also developed for uncertainty quantification, providing calibrated measures of model uncertainty in a visual format. The proposed models were trained and evaluated on three subsets of images in the RESECT dataset and achieved hold-out test Dice scores of 84.63%, 74.52%, and 90.82% on the "before," "during," and "after" subsets, respectively, which indicates increases of ~ 13-31% compared to the state of the art. Furthermore, external evaluation on the ReMIND dataset indicated a robust performance (dice score of 79.17% [95% CI: 77.82-81.62] and only a moderate decline of < 3% in expected calibration error. Our approach integrates various innovations in model design, model training, and uncertainty quantification, achieving improved results on the segmentation of low-grade glioma in ultrasound images during neurosurgery.

The diffusion MRI Neurite Exchange Imaging model offers a promising framework for probing gray matter microstructure by estimating parameters such as compartment sizes, diffusivities, and inter-compartmental water exchange time. However, existing protocols require long scan times. This study proposes a reduced acquisition scheme for the Connectome 2.0 scanner that preserves model accuracy while substantially shortening scan duration. We developed a data-driven framework using explainable artificial intelligence with a guided recursive feature elimination strategy to identify an optimal 8-feature subset from a 15-feature protocol. The performance of this optimized protocol was validated in vivo and benchmarked against the full acquisition and alternative reduction strategies. Parameter accuracy, preservation of anatomical contrast, and test-retest reproducibility were assessed. The reduced protocol yielded parameter estimates and cortical maps comparable to the full protocol, with low estimation errors in synthetic data and minimal impact on test-retest variability. Compared to theory-driven and heuristic reduction schemes, the optimized protocol demonstrated superior robustness, reducing the deviation in water exchange time estimates by over two-fold. In conclusion, this hybrid optimization framework enables viable imaging of neurite exchange in 14 minutes without loss of parameter fidelity. This approach supports the broader application of exchange-sensitive diffusion magnetic resonance imaging in neuroscience and clinical research, and offers a generalizable method for designing efficient acquisition protocols in biophysical parameter mapping.

Gliomas are the most prevalent type of primary brain tumors, and their accurate segmentation from MRI is critical for diagnosis, treatment planning, and longitudinal monitoring. However, the scarcity of high-quality annotated imaging data in Sub-Saharan Africa (SSA) poses a significant challenge for deploying advanced segmentation models in clinical workflows. This study introduces a robust and computationally efficient deep learning framework tailored for resource-constrained settings. We leveraged a 3D Attention UNet architecture augmented with residual blocks and enhanced through transfer learning from pre-trained weights on the BraTS 2021 dataset. Our model was evaluated on 95 MRI cases from the BraTS-Africa dataset, a benchmark for glioma segmentation in SSA MRI data. Despite the limited data quality and quantity, our approach achieved Dice scores of 0.76 for the Enhancing Tumor (ET), 0.80 for Necrotic and Non-Enhancing Tumor Core (NETC), and 0.85 for Surrounding Non-Functional Hemisphere (SNFH). These results demonstrate the generalizability of the proposed model and its potential to support clinical decision making in low-resource settings. The compact architecture, approximately 90 MB, and sub-minute per-volume inference time on consumer-grade hardware further underscore its practicality for deployment in SSA health systems. This work contributes toward closing the gap in equitable AI for global health by empowering underserved regions with high-performing and accessible medical imaging solutions.

Magnetic resonance imaging (MRI) is the gold standard for brain imaging. Deep learning (DL) algorithms have been proposed to aid in the diagnosis of diseases such as Alzheimer's disease (AD) from MRI scans. However, DL algorithms can suffer from shortcut learning, in which spurious features, not directly related to the output label, are used for prediction. When these features are related to protected attributes, they can lead to performance bias against underrepresented protected groups, such as those defined by race and sex. In this work, we explore the potential for shortcut learning and demographic bias in DL based AD diagnosis from MRI. We first investigate if DL algorithms can identify race or sex from 3D brain MRI scans to establish the presence or otherwise of race and sex based distributional shifts. Next, we investigate whether training set imbalance by race or sex can cause a drop in model performance, indicating shortcut learning and bias. Finally, we conduct a quantitative and qualitative analysis of feature attributions in different brain regions for both the protected attribute and AD classification tasks. Through these experiments, and using multiple datasets and DL models (ResNet and SwinTransformer), we demonstrate the existence of both race and sex based shortcut learning and bias in DL based AD classification. Our work lays the foundation for fairer DL diagnostic tools in brain MRI. The code is provided at https://github.com/acharaakshit/ShortMR

The generalizability and reproducibility of AI-assisted detection for cerebral aneurysms on 3D time-of-flight MR angiography remain unclear. We aimed to evaluate physician performance using AI assistance, focusing on inter- and intra-user variability, identifying factors influencing performance and clinical implications. In this retrospective study, four state-of-the-art AI models were hyperparameter-optimized on an in-house dataset (2019-2021) and evaluated via 5-fold cross-validation on a public external dataset. The two best-performing models were selected for evaluation on an expert-revised external dataset. saccular aneurysms without prior treatment. Five physicians, grouped by expertise, each performed two AI-assisted evaluations, one with each model. Lesion-wise sensitivity and false positives per case (FPs/case) were calculated for each physician-AI pair and AI models alone. Agreement was assessed using kappa. Aneurysm size comparisons used the Mann-Whitney U test. The in-house dataset included 132 patients with 206 aneurysms (mean size: 4.0 mm); the revised external dataset, 270 patients with 174 aneurysms (mean size: 3.7 mm). Standalone AI achieved 86.8% sensitivity and 0.58 FPs/case. With AI assistance, non-experts achieved 72.1% sensitivity and 0.037 FPs/case; experts, 88.6% and 0.076 FPs/case; the intermediate-level physician, 78.5% and 0.037 FPs/case. Intra-group agreement was 80% for non-experts (kappa: 0.57, 95% CI: 0.54-0.59) and 77.7% for experts (kappa: 0.53, 95% CI: 0.51-0.55). In experts, false positives were smaller than true positives (2.7 vs. 3.8 mm, p < 0.001); no difference in non-experts (p = 0.09). Missed aneurysm locations were mainly model-dependent, while true- and false-positive locations reflected physician expertise. Non-experts more often rejected AI suggestions and added fewer annotations; experts were more conservative and added more. Evaluating AI models in isolation provides an incomplete view of their clinical applicability. Detection performance and patterns differ between standalone AI and AI-assisted use, and are modulated by physician expertise. Rigorous external validation is essential before clinical deployment.

Vision Transformer (ViT) applied to structural magnetic resonance images has demonstrated success in the diagnosis of Alzheimer's disease (AD) and mild cognitive impairment (MCI). However, three key challenges have yet to be well addressed: 1) ViT requires a large labeled dataset to mitigate overfitting while most of the current AD-related sMRI data fall short in the sample sizes. 2) ViT neglects the within-patch feature learning, e.g., local brain atrophy, which is crucial for AD diagnosis. 3) While ViT can enhance capturing local features by reducing the patch size and increasing the number of patches, the computational complexity of ViT quadratically increases with the number of patches with unbearable overhead. To this end, this paper proposes a 3D-convolutional neural network (CNN) Enhanced Multiscale Progressive ViT (3D-CNN-MPVT). First, a 3D-CNN is pre-trained on sMRI data to extract detailed local image features and alleviate overfitting. Second, an MPVT module is proposed with an inner CNN module to explicitly characterize the within-patch interactions that are conducive to AD diagnosis. Third, a stitch operation is proposed to merge cross-patch features and progressively reduce the number of patches. The inner CNN alongside the stitch operation in the MPTV module enhances local feature characterization while mitigating computational costs. Evaluations using the Alzheimer's Disease Neuroimaging Initiative dataset with 6610 scans and the Open Access Series of Imaging Studies-3 with 1866 scans demonstrated its superior performance. With minimal preprocessing, our approach achieved an impressive 90% accuracy and 80% in AD classification and MCI conversion prediction, surpassing recent baselines.

Sensory experience impacts brain development. In the mouse somatosensory cortex, sensory deprivation via whisker trimming induces reductions in the perineuronal net, the size of neuronal cell bodies, the size and orientation of dendritic arbors, the density of dendritic spines, and the level of myelination, among other effects. Using a custom-developed laboratory diffractometer, we measured the X-ray diffraction patterns of mouse brain tissue to establish a novel method for examining nanoscale brain structures. Two groups of mice were examined: a control group and one that underwent 30 days of whisker-trimming from birth an established method of sensory deprivation that affects the mouse barrel cortex (whisker sensory processing region of the primary somatosensory cortex). Mice were perfused, and primary somatosensory cortices were isolated for immunocytochemistry and X-ray diffraction imaging. X-ray images were characterized using a specially developed machine-learning approach, and the clusters that correspond to the two groups are well separated in principal components space. We obtained the perfect values for sensitivity and specificity, as well as for the receiver operator curve classifier. New machine-learning approaches allow for the first time x-ray diffraction to identify cortex that has undergone sensory deprivation without the use of stains. We hypothesize that our results are related to the alteration of different nanoscale structural components in the brains of sensory deprived mice. The effects of these nanoscale structural formations can be reflective of changes in the micro- and macro-scale structures and assemblies with the neocortex.

To develop a deep learning-based reconstruction method for highly accelerated 3D time-of-flight MRA (TOF-MRA) that achieves high-quality reconstruction with robust generalization using extremely limited acquired raw data, addressing the challenge of time-consuming acquisition of high-resolution, whole-head angiograms. A novel few-shot learning-based reconstruction framework is proposed, featuring a 3D variational network specifically designed for 3D TOF-MRA that is pre-trained on simulated complex-valued, multi-coil raw k-space datasets synthesized from diverse open-source magnitude images and fine-tuned using only two single-slab experimentally acquired datasets. The proposed approach was evaluated against existing methods on acquired retrospectively undersampled in vivo k-space data from five healthy volunteers and on prospectively undersampled data from two additional subjects. The proposed method achieved superior reconstruction performance on experimentally acquired in vivo data over comparison methods, preserving most fine vessels with minimal artifacts with up to eight-fold acceleration. Compared to other simulation techniques, the proposed method generated more realistic raw k-space data for 3D TOF-MRA. Consistently high-quality reconstructions were also observed on prospectively undersampled data. By leveraging few-shot learning, the proposed method enabled highly accelerated 3D TOF-MRA relying on minimal experimentally acquired data, achieving promising results on both retrospective and prospective in vivo data while outperforming existing methods. Given the challenges of acquiring and sharing large raw k-space datasets, this holds significant promise for advancing research and clinical applications in high-resolution, whole-head 3D TOF-MRA imaging.

Alzheimer's disease (AD) is considered to be one of the neurodegenerative diseases with possible cognitive deficits related to dementia in human subjects. High priority should be put on efforts aimed at early detection of AD. Here, images undergo a pre-processing phase that integrates image resizing and the application of median filters. After that, processed images are subjected to data augmentation procedures. Feature extraction from WOA-based ResNet, together with extracted convolutional neural network (CNN) features from pre-processed images, is used to train proposed DL model to classify AD. The process is executed using the proposed Attention Gated-VGG model. The proposed method outperformed normal methodologies when tested and achieved an accuracy of 96.7%, sensitivity of 97.8%, and specificity of 96.3%. The results have proven that Attention Gated-VGG model is a very promising technique for classifying AD.