Fetal cardiovascular magnetic resonance is an emerging tool for prenatal congenital heart disease assessment, but long acquisition times and fetal movements limit its clinical use. This study evaluates the clinical utility of deep learning super-resolution reconstructions for rapidly acquired, low-resolution fetal cardiovascular magnetic resonance. This prospective study included participants with fetal congenital heart disease undergoing fetal cardiovascular magnetic resonance in the third trimester of pregnancy, with axial cine images acquired at normal resolution and low resolution. Low-resolution cine data was subsequently reconstructed using a deep learning super-resolution framework (cine_DL). Acquisition times, apparent signal-to-noise ratio, contrast-to-noise ratio, and edge rise distance were assessed. Volumetry and functional analysis were performed. Qualitative image scores were rated on a 5-point Likert scale. Cardiovascular structures and pathological findings visible in cine_DL images only were assessed. Statistical analysis included the Student paired <i>t</i> test and the Wilcoxon test. A total of 42 participants were included (median gestational age, 35.9 weeks [interquartile range (IQR), 35.1-36.4]). Cine_DL acquisition was faster than cine images acquired at normal resolution (134±9.6 s versus 252±8.8 s; <i>P</i><0.001). Quantitative image quality metrics and image quality scores for cine_DL were higher or comparable with those of cine images acquired at normal-resolution images (eg, fetal motion, 4.0 [IQR, 4.0-5.0] versus 4.0 [IQR, 3.0-4.0]; <i>P</i><0.001). Nonpatient-related artifacts (eg, backfolding) were more pronounced in Cine_DL compared with cine images acquired at normal-resolution images (4.0 [IQR, 4.0-5.0] versus 5.0 [IQR, 3.0-4.0]; <i>P</i><0.001). Volumetry and functional results were comparable. Cine_DL revealed additional structures in 10 of 42 fetuses (24%) and additional pathologies in 5 of 42 fetuses (12%), including partial anomalous pulmonary venous connection. Deep learning super-resolution reconstructions of low-resolution acquisitions shorten acquisition times and achieve diagnostic quality comparable with standard images, while being less sensitive to fetal bulk movements, leading to additional diagnostic findings. Therefore, deep learning super-resolution may improve the clinical utility of fetal cardiovascular magnetic resonance for accurate prenatal assessment of congenital heart disease.

The accurate segmentation of myocardial scars from cardiac MRI is essential for clinical assessment and treatment planning. In this study, we propose a robust deep-learning pipeline for fully automated myocardial scar detection and segmentation by fine-tuning state-of-the-art models. The method explicitly addresses challenges of label noise from semi-automatic annotations, data heterogeneity, and class imbalance through the use of Kullback-Leibler loss and extensive data augmentation. We evaluate the model's performance on both acute and chronic cases and demonstrate its ability to produce accurate and smooth segmentations despite noisy labels. In particular, our approach outperforms state-of-the-art models like nnU-Net and shows strong generalizability in an out-of-distribution test set, highlighting its robustness across various imaging conditions and clinical tasks. These results establish a reliable foundation for automated myocardial scar quantification and support the broader clinical adoption of deep learning in cardiac imaging.

Fast and accurate organ-at-risk (OAR) and gross tumor volume (GTV) contour propagation methods are needed to improve the efficiency of magnetic resonance (MR) imaging-guided radiotherapy. We trained deformable image registration networks to accurately propagate contours from planning to fraction MR images.&#xD;Approach: Data from 140 stage 1-2 lung cancer patients treated at a 0.35T MR-Linac were split into 102/17/21 for training/validation/testing. Additionally, 18 central lung tumor patients, treated at a 0.35T MR-Linac externally, and 14 stage 3 lung cancer patients from a phase 1 clinical trial, treated at 0.35T or 1.5T MR-Linacs at three institutions, were used for external testing. Planning and fraction images were paired (490 pairs) for training. Two hybrid transformer-convolutional neural network TransMorph models with mean squared error (MSE), Dice similarity coefficient (DSC), and regularization losses (TM_{MSE+Dice}) or MSE and regularization losses (TM_{MSE}) were trained to deformably register planning to fraction images. The TransMorph models predicted diffeomorphic dense displacement fields. Multi-label images including seven thoracic OARs and the GTV were propagated to generate fraction segmentations. Model predictions were compared with contours obtained through B-spline, vendor registration and the auto-segmentation method nnUNet. Evaluation metrics included the DSC and Hausdorff distance percentiles (50th and 95th) against clinical contours.&#xD;Main results: TM_{MSE+Dice} and TM_{MSE} achieved mean OARs/GTV DSCs of 0.90/0.82 and 0.90/0.79 for the internal and 0.84/0.77 and 0.85/0.76 for the central lung tumor external test data. On stage 3 data, TM_{MSE+Dice} achieved mean OARs/GTV DSCs of 0.87/0.79 and 0.83/0.78 for the 0.35 T MR-Linac datasets, and 0.87/0.75 for the 1.5 T MR-Linac dataset. TM_{MSE+Dice} and TM_{MSE} had significantly higher geometric accuracy than other methods on external data. No significant difference between TM_{MSE+Dice} and TM_{MSE} was found.&#xD;Significance: TransMorph models achieved time-efficient segmentation of fraction MRIs with high geometrical accuracy and accurately segmented images obtained at different field strengths.

Diagnosis of tumefactive demyelination can be challenging. The diagnosis of indeterminate brain lesions on MRI often requires tissue confirmation via brain biopsy. Noninvasive methods for accurate diagnosis of tumor and nontumor etiologies allows for tailored therapy, optimal tumor control, and a reduced risk of iatrogenic morbidity and mortality. Tumefactive demyelination has imaging features that mimic <i>isocitrate dehydrogenase</i> wild-type glioblastoma (<i>IDH</i>wt GBM). We hypothesized that deep learning applied to postcontrast T1-weighted (T1C) and T2-weighted (T2) MRI can discriminate tumefactive demyelination from <i>IDH</i>wt GBM. Patients with tumefactive demyelination (<i>n</i> = 144) and <i>IDH</i>wt GBM (<i>n</i> = 455) were identified by clinical registries. A 3D DenseNet121 architecture was used to develop models to differentiate tumefactive demyelination and <i>IDH</i>wt GBM by using both T1C and T2 MRI, as well as only T1C and only T2 images. A 3-stage design was used: 1) model development and internal validation via 5-fold cross validation by using a sex-, age-, and MRI technology-matched set of tumefactive demyelination and <i>IDH</i>wt GBM, 2) validation of model specificity on independent <i>IDH</i>wt GBM, and 3) prospective validation on tumefactive demyelination and <i>IDH</i>wt GBM. Stratified area under the receiver operating curves (AUROCs) were used to evaluate model performance stratified by sex, age at diagnosis, MRI scanner strength, and MRI acquisition. The deep learning model developed by using both T1C and T2 images had a prospective validation AUROC of 88% (95% CI: 0.82-0.95). In the prospective validation stage, a model score threshold of 0.28 resulted in 91% sensitivity of correctly classifying tumefactive demyelination and 80% specificity (correctly classifying <i>IDH</i>wt GBM). Stratified AUROCs demonstrated that model performance may be improved if thresholds were chosen stratified by age and MRI acquisition. MRI can provide the basis for applying deep learning models to aid in the differential diagnosis of brain lesions. Further validation is needed to evaluate how well the model generalizes across institutions, patient populations, and technology, and to evaluate optimal thresholds for classification. Next steps also should incorporate additional tumor etiologies such as CNS lymphoma and brain metastases.

For magnetic resonance imaging (MRI), a short acquisition time and good image quality are incompatible. Thus, reconstructing time-resolved volumetric MRI (4D-MRI) to delineate and monitor thoracic and upper abdominal tumor movements is a challenge. Existing MRI sequences have limited applicability to 4D-MRI. A method is proposed for reconstructing high-quality personalized enhanced 4D-MR images. Low-quality 4D-MR images are scanned followed by deep learning-based personalization to generate high-quality 4D-MR images. High-speed multiphase 3D fast spoiled gradient recalled echo (FSPGR) sequences were utilized to generate low-quality enhanced free-breathing 4D-MR images and paired low-/high-quality breath-holding 4D-MR images for 58 liver cancer patients. Then, a personalized model guided by the paired breath-holding 4D-MR images was developed for each patient to cope with patient heterogeneity. The 4D-MR images generated by the personalized model were of much higher quality compared with the low-quality 4D-MRI images obtained by conventional scanning as demonstrated by significant improvements in the peak signal-to-noise ratio, structural similarity, normalized root mean square error, and cumulative probability of blur detection. The introduction of individualized information helped the personalized model demonstrate a statistically significant improvement compared to the general model (p < 0.001). The proposed method can be used to quickly reconstruct high-quality 4D-MR images and is potentially applicable to radiotherapy for liver cancer.

The peripheral immune system is essential for maintaining central nervous system homeostasis. This study investigates the effects of peripheral immune markers on accelerated brain aging and dementia using brain-predicted age difference based on neuroimaging. By leveraging data from the UK Biobank, Cox regression was used to explore the relationship between peripheral immune markers and dementia, and multivariate linear regression to assess associations between peripheral immune biomarkers and brain structure. Additionally, we established a brain age prediction model using Simple Fully Convolutional Network (SFCN) deep learning architecture. Analysis of the resulting brain-Predicted Age Difference (PAD) revealed relationships between accelerated brain aging, peripheral immune markers, and dementia. During the median follow-up period of 14.3 years, 4, 277 dementia cases were observed among 322, 761 participants. Both innate and adaptive immune markers correlated with dementia risk. NLR showed the strongest association with dementia risk (HR = 1.14; 95% CI: 1.11-1.18, P<0.001). Multivariate linear regression revealed significant associations between peripheral immune markers and brain regional structural indices. Utilizing the deep learning-based SFCN model, the estimated brain age of dementia subjects (MAE = 5.63, r2 = - 0.46, R = 0.22) was determined. PAD showed significant correlation with dementia risk and certain peripheral immune markers, particularly in individuals with positive brain age increment. This study employs brain age as a quantitative marker of accelerated brain aging to investigate its potential associations with peripheral immunity and dementia, highlighting the importance of early intervention targeting peripheral immune markers to delay brain aging and prevent dementia.

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.

<i>"Just Accepted" papers have undergone full peer review and have been accepted for publication in <i>Radiology: Artificial Intelligence</i>. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content.</i> Purpose To develop and evaluate an open-source deep learning model for detection and localization of breast cancer on MRI. Materials and Methods In this retrospective study, a deep learning model for breast cancer detection and localization was trained on the largest breast MRI dataset to date. Data included all breast MRIs conducted at a tertiary cancer center in the United States between 2002 and 2019. The model was validated on sagittal MRIs from the primary site (<i>n</i> = 6,615 breasts). Generalizability was assessed by evaluating model performance on axial data from the primary site (<i>n</i> = 7,058 breasts) and a second clinical site (<i>n</i> = 1,840 breasts). Results The primary site dataset included 30,672 sagittal MRI examinations (52,598 breasts) from 9,986 female patients (mean [SD] age, 53 [11] years). The model achieved an area under the receiver operating characteristic curve (AUC) of 0.95 for detecting cancer in the primary site. At 90% specificity (5717/6353), model sensitivity was 83% (217/262), which was comparable to historical performance data for radiologists. The model generalized well to axial examinations, achieving an AUC of 0.92 on data from the same clinical site and 0.92 on data from a secondary site. The model accurately located the tumor in 88.5% (232/262) of sagittal images, 92.8% (272/293) of axial images from the primary site, and 87.7% (807/920) of secondary site axial images. Conclusion The model demonstrated state-of-the-art performance on breast cancer detection. Code and weights are openly available to stimulate further development and validation. ©RSNA, 2025.

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.

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.