&#xD;Magnetic resonance imaging-guided adaptive radiotherapy (MRIgART) is a promising technique for long-course RT of large-volume brain metastasis (BM), due to the capacity to track tumor changes throughout treatment course. Contrast-enhanced T1-weighted (T1CE) MRI is essential for BM delineation, yet is often unavailable during online treatment concerning the requirement of contrast agent injection. This study aims to develop a synthetic T1CE (sT1CE) generation method to facilitate accurate online adaptive BM delineation.&#xD;Approach:&#xD;We developed a novel ControlNet-coupled latent diffusion model (CTN-LDM) combined with a personalized transfer learning strategy and a denoising diffusion implicit model (DDIM) inversion method to generate high quality sT1CE images from online T2-weighted (T2) or fluid attenuated inversion recovery (FLAIR) images. Visual quality of sT1CE images generated by the CTN-LDM was compared with classical deep learning models. BM delineation results using the combination of our sT1CE images and online T2/FLAIR images were compared with the results solely using online T2/FLAIR images, which is the current clinical method.&#xD;Main results:&#xD;Visual quality of sT1CE images from our CTN-LDM was superior to classical models both quantitatively and qualitatively. Leveraging sT1CE images, radiation oncologists achieved significant higher precision of adaptive BM delineation, with average Dice similarity coefficient of 0.93 ± 0.02 vs. 0.86 ± 0.04 (p < 0.01), compared with only using online T2/FLAIR images. &#xD;Significance:&#xD;The proposed method could generate high quality sT1CE images and significantly improve accuracy of online adaptive tumor delineation for long-course MRIgART of large-volume BM, potentially enhancing treatment outcomes and minimizing toxicity.

Accurate interpretation of knee MRI scans relies on expert clinical judgment, often with high variability and limited scalability. Existing radiomic approaches use a fixed set of radiomic features (the signature), selected at the population level and applied uniformly to all patients. While interpretable, these signatures are often too constrained to represent individual pathological variations. As a result, conventional radiomic-based approaches are found to be limited in performance, compared with recent end-to-end deep learning (DL) alternatives without using interpretable radiomic features. We argue that the individual-agnostic nature in current radiomic selection is not central to its intepretability, but is responsible for the poor generalization in our application. Here, we propose a novel radiomic fingerprint framework, in which a radiomic feature set (the fingerprint) is dynamically constructed for each patient, selected by a DL model. Unlike the existing radiomic signatures, our fingerprints are derived on a per-patient basis by predicting the feature relevance in a large radiomic feature pool, and selecting only those that are predictive of clinical conditions for individual patients. The radiomic-selecting model is trained simultaneously with a low-dimensional (considered relatively explainable) logistic regression for downstream classification. We validate our methods across multiple diagnostic tasks including general knee abnormalities, anterior cruciate ligament (ACL) tears, and meniscus tears, demonstrating comparable or superior diagnostic accuracy relative to state-of-the-art end-to-end DL models. More importantly, we show that the interpretability inherent in our approach facilitates meaningful clinical insights and potential biomarker discovery, with detailed discussion, quantitative and qualitative analysis of real-world clinical cases to evidence these advantages.

Artificial intelligence, including deep learning models, will play a transformative role in automated medical image analysis for the diagnosis of cardiac disorders and their management. Automated accurate delineation of cardiac images is the first necessary initial step for the quantification and automated diagnosis of cardiac disorders. In this paper, we propose a deep learning based enhanced UNet model, U-R-Veda, which integrates convolution transformations, vision transformer, residual links, channel-attention, and spatial attention, together with edge-detection based skip-connections for an accurate fully-automated semantic segmentation of cardiac magnetic resonance (CMR) images. The model extracts local-features and their interrelationships using a stack of combination convolution blocks, with embedded channel and spatial attention in the convolution block, and vision transformers. Deep embedding of channel and spatial attention in the convolution block identifies important features and their spatial localization. The combined edge information with channel and spatial attention as skip connection reduces information-loss during convolution transformations. The overall model significantly improves the semantic segmentation of CMR images necessary for improved medical image analysis. An algorithm for the dual attention module (channel and spatial attention) has been presented. Performance results show that U-R-Veda achieves an average accuracy of 95.2%, based on DSC metrics. The model outperforms the accuracy attained by other models, based on DSC and HD metrics, especially for the delineation of right-ventricle and left-ventricle-myocardium.

Diffusion MRI tractography technique enables non-invasive visualization of the white matter pathways in the brain. It plays a crucial role in neuroscience and clinical fields by facilitating the study of brain connectivity and neurological disorders. However, the accuracy of reconstructed tractograms has been a longstanding challenge. Recently, deep learning methods have been applied to improve tractograms for better white matter coverage, but often comes at the expense of generating excessive false-positive connections. This is largely due to their reliance on local information to predict long-range streamlines. To improve the accuracy of streamline propagation predictions, we introduce a novel deep learning framework that integrates image-domain spatial information and anatomical information along tracts, with the former extracted through convolutional layers and the latter modeled via a Transformer-decoder. Additionally, we employ a weighted loss function to address fiber class imbalance encountered during training. We evaluate the proposed method on the simulated ISMRM 2015 Tractography Challenge dataset, achieving a valid streamline rate of 66.2 %, white matter coverage of 63.8 %, and successfully reconstructing 24 out of 25 bundles. Furthermore, on the multi-site Tractoinferno dataset, the proposed method demonstrates its ability to handle various diffusion MRI acquisition schemes, achieving a 5.7 % increase in white matter coverage and a 4.1 % decrease in overreach compared to RNN-based methods.

In recent decades, the use of 4D Flow MRI images has enabled the quantification of velocity fields within a volume of interest and along the cardiac cycle. However, the lack of resolution and the presence of noise in these biomarkers are significant issues. As indicated by recent studies, it appears that biomarkers such as wall shear stress are particularly impacted by the poor resolution of vessel segmentation. The Phase Contrast Magnetic Resonance Angiography (PC-MRA) is the state-of-the-art method to facilitate segmentation. The objective of this work is to introduce a new handcraft feature that provides a novel visualisation of 4D Flow MRI images, which is useful in the segmentation task. This feature, termed Weighted Mean Frequencies (WMF), is capable of revealing the region in three dimensions where a voxel has been passed by pulsatile flow. Indeed, this feature is representative of the hull of all pulsatile velocity voxels. The value of the feature under discussion is illustrated by two experiments. The experiments involved segmenting 4D Flow MRI images using optimal thresholding and deep learning methods. The results obtained demonstrate a substantial enhancement in terms of IoU and Dice, with a respective increase of 0.12 and 0.13 in comparison with the PC-MRA feature, as evidenced by the deep learning task. This feature has the potential to yield valuable insights that could inform future segmentation processes in other vascular regions, such as the heart or the brain.

Recent research has suggested that neuroinflammation may be important in the pathogenesis of neurodegenerative diseases. Free-water diffusion (FWD) has been proposed as a non-invasive neuroimaging-based biomarker for neuroinflammation. Free-water maps were generated using diffusion MRI data in 367 patients from the Ontario Neurodegenerative Disease Research Initiative (108 Alzheimer's Disease/Mild Cognitive Impairment, 42 Frontotemporal Dementia, 37 Amyotrophic Lateral Sclerosis, 123 Parkinson's Disease, and 58 vascular disease-related Cognitive Impairment). The ability of FWD to predict neuroinflammation and neurodegeneration from biofluids was estimated using plasma glial fibrillary-associated protein (GFAP) and neurofilament light chain (NfL), respectively. Recursive Feature Elimination (RFE) performed the strongest out of all feature selection algorithms used and revealed regional specificity for areas that are the most important features for predicting GFAP over NfL concentration. Deep learning models using selected features and demographic information revealed better prediction of GFAP over NfL. Based on feature selection and deep learning methods, FWD was found to be more strongly related to GFAP concentration (measure of astrogliosis) over NfL (measure of neuro-axonal damage), across neurodegenerative disease groups, in terms of predictive performance. Non-invasive markers of neurodegeneration such as MRI structural imaging that can reveal neurodegeneration already exist, while non-invasive markers of neuroinflammation are not available. Our results support the use of FWD as a non-invasive neuroimaging-based biomarker for neuroinflammation.

Detecting brain lesions as abnormalities observed in magnetic resonance imaging (MRI) is essential for diagnosis and treatment. In the search of abnormalities, such as tumors and malformations, radiologists may benefit from computer-aided diagnostics that use computer vision systems trained with machine learning to segment normal tissue from abnormal brain tissue. While supervised learning methods require annotated lesions, we propose a new unsupervised approach (Patch2Loc) that learns from normal patches taken from structural MRI. We train a neural network model to map a patch back to its spatial location within a slice of the brain volume. During inference, abnormal patches are detected by the relatively higher error and/or variance of the location prediction. This generates a heatmap that can be integrated into pixel-wise methods to achieve finer-grained segmentation. We demonstrate the ability of our model to segment abnormal brain tissues by applying our approach to the detection of tumor tissues in MRI on T2-weighted images from BraTS2021 and MSLUB datasets and T1-weighted images from ATLAS and WMH datasets. We show that it outperforms the state-of-the art in unsupervised segmentation. The codebase for this work can be found on our \href{https://github.com/bakerhassan/Patch2Loc}{GitHub page}.

Deep-learning models for prostate cancer detection typically require large datasets, limiting clinical applicability across institutions due to domain shift issues. This study aimed to develop a few-shot learning deep-learning model for prostate cancer detection on multiparametric MRI that requires minimal training data and to compare its diagnostic performance with experienced radiologists. In this retrospective study, we used 99 cases (80 positive, 19 negative) of biopsy-confirmed prostate cancer (2017-2022), with 20 cases for training, 5 for validation, and 74 for testing. A 2D transformer model was trained on T2-weighted, diffusion-weighted, and apparent diffusion coefficient map images. Model predictions were compared with two radiologists using Matthews correlation coefficient (MCC) and F1 score, with 95% confidence intervals (CIs) calculated via bootstrap method. The model achieved an MCC of 0.297 (95% CI: 0.095-0.474) and F1 score of 0.707 (95% CI: 0.598-0.847). Radiologist 1 had an MCC of 0.276 (95% CI: 0.054-0.484) and F1 score of 0.741; Radiologist 2 had an MCC of 0.504 (95% CI: 0.289-0.703) and F1 score of 0.871, showing that the model performance was comparable to Radiologist 1. External validation on the Prostate158 dataset revealed that ImageNet pretraining substantially improved model performance, increasing study-level ROC-AUC from 0.464 to 0.636 and study-level PR-AUC from 0.637 to 0.773 across all architectures. Our findings demonstrate that few-shot deep-learning models can achieve clinically relevant performance when using pretrained transformer architectures, offering a promising approach to address domain shift challenges across institutions.

The objective of this study is to investigate the impact of deep learning reconstruction and accelerated acquisition on reproducibility and variability of radiomic features in abdominal MRI. Seventeen volunteers were prospectively included to undergo abdominal MRI on a 3-T scanner for axial T2-weighted, axial T2-weighted fat-suppressed, and coronal T2-weighted sequences. Each sequence was scanned for four times using clinical reference acquisition with standard reconstruction, clinical reference acquisition with deep learning reconstruction, accelerated acquisition with standard reconstruction, and accelerated acquisition with deep learning reconstruction, respectively. The regions of interest were drawn for ten anatomical sites with rigid registrations. Ninety-three radiomic features were extracted via PyRadiomics after z-score normalization. The reproducibility was evaluated using clinical reference acquisition with standard reconstruction as reference by intraclass correlation coefficient (ICC) and concordance correlation coefficient (CCC). The variability among four scans was assessed by coefficient of variation (CV) and quartile coefficient of dispersion (QCD). Our study found that the median (first and third quartile) of overall ICC and CCC values were 0.451 (0.305, 0.583) and 0.450 (0.304, 0.582). The overall percentage of radiomic features with ICC > 0.90 and CCC > 0.90 was 8.1% and 8.1%, and was considered acceptable. The median (first and third quartile) of overall CV and QCD values was 9.4% (4.9%, 17.2%) and 4.9% (2.5%, 9.7%). The overall percentage of radiomic features with CV < 10% and QCD < 10% was 51.9% and 75.0%, and was considered acceptable. Without respect to clinical significance, deep learning reconstruction and accelerated acquisition led to a poor reproducibility of radiomic features, but more than a half of the radiomic features varied within an acceptable range.

Obesity is associated with functional alterations in the brain. Although spatial organisation changes in the brains of individuals with obesity have been widely studied, the temporal dynamics in their brains remain poorly understood. Therefore, in this study, we investigated variations in the intrinsic neural timescale (INT) across different degrees of obesity using resting-state functional and diffusion magnetic resonance imaging data from the enhanced Nathan Kline Institute Rockland Sample database. We examined the relationship between the INT and obesity phenotypes using supervised machine learning, controlling for age and sex. To further explore the structure-function characteristics of these regions, we assessed the modular network properties by analysing the participation coefficients and within-module degree derived from the structure-function coupling matrices. Finally, the INT values of the identified regions were used to predict eating behaviour traits. A significant negative correlation was observed, particularly in the default mode, limbic and reward networks. We found a negative association with the participation coefficients, suggesting that shorter INT values in higher-order association areas are related to reduced network integration. Moreover, the INT values of these identified regions moderately predicted eating behaviours, underscoring the potential of the INT as a candidate marker for obesity and eating behaviours. These findings provide insight into the temporal organisation of neural activity in obesity, highlighting the role of specific brain networks in shaping behavioural outcomes.