Tractography is a challenging process with complex rules, driving continuous algorithmic evolution to address its challenges. Meanwhile, deep learning has tackled similarly difficult tasks, such as mastering the Go board game and animating sophisticated robots. Given its transformative impact in these areas, deep learning has the potential to revolutionize tractography within the framework of existing rules. This work provides a brief summary of recent advances and challenges in deep learning-based tractography computing and analysis.

Brain age has emerged as a powerful tool to understand neuroanatomical aging and its link to health outcomes like cognition. However, there remains a lack of studies investigating the rate of brain aging and its relationship to cognition. Furthermore, most brain age models are trained and tested on cross-sectional data from primarily Caucasian, adult participants. It is thus unclear how well these models generalize to non-Caucasian participants, especially children. Here, we tested a previously published deep learning model on Singaporean elderly participants (55-88 years old) and children (4-11 years old). We found that the model directly generalized to the elderly participants, but model finetuning was necessary for children. After finetuning, we found that the rate of change in brain age gap was associated with future executive function performance in both elderly participants and children. We further found that lateral ventricles and frontal areas contributed to brain age prediction in elderly participants, while white matter and posterior brain regions were more important in predicting brain age of children. Taken together, our results suggest that there is potential for generalizing brain age models to diverse populations. Moreover, the longitudinal change in brain age gap reflects developing and aging processes in the brain, relating to future cognitive function.

Inter reader variability and cross site domain shift challenge the automatic segmentation of prostate anatomy using T2 weighted MRI images. This study investigates whether transformer models can retain precision amid such heterogeneity. We compare the performance of UNETR and SwinUNETR in prostate gland segmentation against our previous 3D UNet model [1], based on 546 MRI (T2weighted) volumes annotated by two independent experts. Three training strategies were analyzed: single cohort dataset, 5 fold cross validated mixed cohort, and gland size based dataset. Hyperparameters were tuned by Optuna. The test set, from an independent population of readers, served as the evaluation endpoint (Dice Similarity Coefficient). In single reader training, SwinUNETR achieved an average dice score of 0.816 for Reader#1 and 0.860 for Reader#2, while UNETR scored 0.8 and 0.833 for Readers #1 and #2, respectively, compared to the baseline UNets 0.825 for Reader #1 and 0.851 for Reader #2. SwinUNETR had an average dice score of 0.8583 for Reader#1 and 0.867 for Reader#2 in cross-validated mixed training. For the gland size-based dataset, SwinUNETR achieved an average dice score of 0.902 for Reader#1 subset and 0.894 for Reader#2, using the five-fold mixed training strategy (Reader#1, n=53; Reader#2, n=87) at larger gland size-based subsets, where UNETR performed poorly. Our findings demonstrate that global and shifted-window self-attention effectively reduces label noise and class imbalance sensitivity, resulting in improvements in the Dice score over CNNs by up to five points while maintaining computational efficiency. This contributes to the high robustness of SwinUNETR for clinical deployment.

Brain metastases are a common complication of systemic cancer, affecting over 20% of patients with primary malignancies. Longitudinal magnetic resonance imaging (MRI) is essential for diagnosing patients, tracking disease progression, assessing therapeutic response, and guiding treatment selection. However, the manual review of longitudinal imaging is time-intensive, especially for patients with multifocal disease. Artificial intelligence (AI) offers opportunities to streamline image evaluation, but developing robust AI models requires comprehensive training data representative of real-world imaging studies. Thus, there is an urgent necessity for a large dataset with heterogeneity in imaging protocols and disease presentation. To address this, we present an open-access dataset of 11,884 longitudinal brain MRI studies from 1,430 patients with clinically confirmed brain metastases, paired with clinical and image metadata. The provided dataset will facilitate the development of AI models to assist in the long-term management of patients with brain metastasis.

The human brain exhibits intricate spatiotemporal dynamics, which can be described and understood through the framework of complex dynamic systems theory. In this study, we leverage functional magnetic resonance imaging (fMRI) data to investigate reaction-diffusion processes in the brain. A reaction-diffusion process refers to the interaction between two or more substances that spread through space and react with each other over time, often resulting in the formation of patterns or waves of activity. Building on this empirical foundation, we apply a reaction-diffusion framework inspired by theoretical physics to simulate the emergence of brain spacetime vortices within the brain. By exploring this framework, we investigate how reaction-diffusion processes can serve as a compelling model to govern the formation and propagation of brain spacetime vortices, which are dynamic, swirling patterns of brain activity that emerge and evolve across both time and space within the brain. Our approach integrates computational modeling with fMRI data to investigate the spatiotemporal properties of these vortices, offering new insights into the fundamental principles of brain organization. This work highlights the potential of reaction-diffusion models as an alternative framework for understanding brain spacetime dynamics.

This study focuses on artificial intelligence-driven classification of glioma and Ki-67 leveling using T2w-FLAIR MRI, exploring the association of Ki-67 biomarkers with deep learning (DL) features through explainable artificial intelligence (XAI) and SHapley Additive exPlanations (SHAP). This IRB-approved study included 101 patients with glioma brain tumor acquired MR images with the T2W-FLAIR sequence. We extracted DL bottleneck features using ResNet50 from glioma MR images. Principal component analysis (PCA) was deployed for dimensionality reduction. XAI was used to identify potential features. The XGBosst classified the histologic grades of the glioma and the level of Ki-67. We integrated potential DL features with patient demographics (age and sex) and Ki-67 biomarkers, utilizing SHAP to determine the model's essential features and interactions. Glioma grade classification and Ki-67 level predictions achieved overall accuracies of 0.94 and 0.91, respectively. It achieved precision scores of 0.92, 0.94, and 0.96 for glioma grades 2, 3, and 4, and 0.88, 0.94, and 0.97 for Ki-67 levels (low: 5%≤Ki-67<10%, moderate: 10%≤Ki-67≤20, and high: Ki-67>20%). Corresponding F1-scores were 0.95, 0.88, and 0.96 for glioma grades and 0.92, 0.93, and 0.87 for Ki-67 levels. SHAP analysis further highlighted a strong association between bottleneck DL features and Ki-67 biomarkers, demonstrating their potential to differentiate glioma grades and Ki-67 levels while offering valuable insights into glioma aggressiveness. This study demonstrates the precise classification of glioma grades and the prediction of Ki-67 levels to underscore the potential of AI-driven MRI analysis to enhance clinical decision-making in glioma management.

Despite the importance of memories in everyday life and the progress made in understanding how they are encoded and retrieved, the neural processes by which declarative memories are maintained or forgotten remain elusive. Part of the problem is that it is empirically difficult to measure the rate at which memories fade, even between repeated presentations of the source of the memory. Without such a ground-truth measure, it is hard to identify the corresponding neural correlates. This study addresses this problem by comparing individual patterns of functional connectivity against behavioral differences in forgetting speed derived from computational phenotyping. Specifically, the individual-specific values of the speed of forgetting in long-term memory (LTM) were estimated for 33 participants using a formal model fit to accuracy and response time data from an adaptive paired-associate learning task. Individual speeds of forgetting were then used to examine participant-specific patterns of resting-state fMRI connectivity, using machine learning techniques to identify the most predictive and generalizable features. Our results show that individual speeds of forgetting are associated with resting-state connectivity within the default mode network (DMN) as well as between the DMN and cortical sensory areas. Cross-validation showed that individual speeds of forgetting were predicted with high accuracy (r = .78) from these connectivity patterns alone. These results support the view that DMN activity and the associated sensory regions are actively involved in maintaining memories and preventing their decline, a view that can be seen as evidence for the hypothesis that forgetting is a result of storage degradation, rather than of retrieval failure.

The molecular profiling of gliomas for isocitrate dehydrogenase (IDH) mutations currently relies on resected tumor samples, highlighting the need for non-invasive, preoperative biomarkers. We investigated the integration of glioma polygenic risk scores (PRS) and radiographic features for prediction of IDH mutation status. We used 256 radiomic features, a glioma PRS and demographic information in 158 glioma cases within elastic net and neural network models. The integration of glioma PRS with radiomics increased the area under the receiver operating characteristic curve (AUC) for distinguishing IDH-wildtype vs. IDH-mutant glioma from 0.83 to 0.88 (P_ΔAUC = 6.9 × 10^-5) in the elastic net model and from 0.91 to 0.92 (P_ΔAUC = 0.32) in the neural network model. Incorporating age at diagnosis and sex further improved the classifiers (elastic net: AUC = 0.93, neural network: AUC = 0.93). Patients predicted to have IDH-mutant vs. IDH-wildtype tumors had significantly lower mortality risk (hazard ratio (HR) = 0.18, 95% CI: 0.08-0.40, P = 2.1 × 10^-5), comparable to prognostic trajectories for biopsy-confirmed IDH status. The augmentation of imaging-based classifiers with genetic risk profiles may help delineate molecular subtypes and improve the timely, non-invasive clinical assessment of glioma patients.

Electrocardiogram (ECG)-gated cine imaging in breath-hold enables high-quality diagnostics in most patients but can be compromised by arrhythmia and inability to hold breath. Real-time cardiac MRI offers faster and robust exams without these limitations. To achieve sufficient acceleration, advanced reconstruction methods, which transfer data into high-quality images, are required. In this study, undersampled spiral balanced SSFP (bSSFP) real-time data in free-breathing were acquired at 1.5T in 16 healthy volunteers and five arrhythmic patients, with ECG-gated Cartesian cine in breath-hold serving as clinical reference. Image reconstructions were performed using a tailored and specifically trained score-based diffusion model, compared to a variational network and different compressed sensing approaches. The techniques were assessed using an expert reader study, scalar metric calculations, difference images against a segmented reference, and Bland-Altman analysis of cardiac functional parameters. In participants with irregular RR-cycles, spiral real-time acquisitions showed superior image quality compared to the clinical reference. Quantitative and qualitative metrics indicate enhanced image quality of the diffusion model in comparison to the alternative reconstruction methods, although improvements over the variational network were minor. Slightly higher ejection fractions for the real-time diffusion reconstructions were exhibited relative to the clinical references with a bias of 1.1 ± 5.7% for healthy subjects. The proposed real-time technique enables free-breathing acquisitions of spatio-temporal images with high quality, covering the entire heart in less than 1 min. Evaluation of ejection fraction using the ECG-gated reference can be vulnerable to arrhythmia and averaging effects, highlighting the need for real-time approaches. Prolonged inference times and stochastic variability of the diffusion reconstruction represent obstacles to overcome for clinical translation.

This paper proposes a novel clustering method for noisy image segmentation using a kernelized weighted local information approach under the Picture Fuzzy Set (PFS) framework. Existing kernel-based fuzzy clustering methods struggle with noisy environments and non-linear structures, while intuitionistic fuzzy clustering methods face limitations in handling uncertainty in real-world medical images. To address these challenges, we introduce a local picture fuzzy information measure, developed for the first time using Multivariate Coefficient of Variation (MCV) theory, enhancing robustness in segmentation. Additionally, we integrate non-Euclidean distance measures, including kernel distance for local information computation and modified total Bregman divergence (MTBD) measure for improving clustering accuracy. This combination enhances both local spatial consistency and global membership estimation, leading to precise segmentation. The proposed method is extensively evaluated on synthetic images with Gaussian, Salt and Pepper, and mixed noise, along with Brainweb, IBSR, and MRBrainS18 MRI datasets under varying Rician noise levels, and a CT image template. Furthermore, we benchmark our proposed method against two deep learning-based segmentation models, ResNet34-LinkNet and patch-based U-Net. Experimental results demonstrate significant improvements in segmentation accuracy, as validated by metrics such as Dice Score, Fuzzy Performance Index, Modified Partition Entropy, Average Volume Difference (AVD), and the XB index. Additionally, Friedman's statistical test confirms the superior performance of our approach compared to state-of-the-art clustering methods for noisy image segmentation. To facilitate reproducibility, the implementation of our proposed method is made publicly available at: Google Drive Repository.