The utility of Magnetic Resonance Imaging (MRI) in anomaly detection and disease diagnosis is well recognized. However, the current imaging protocol is often hindered by long scanning durations and a misalignment between the scanning process and the specific requirements of subsequent clinical assessments. While recent studies have actively explored accelerated MRI techniques, the majority have concentrated on improving overall image quality across all voxel locations, overlooking the attention to specific abnormalities that hold clinical significance. To address this discrepancy, we propose a model-unrolled deep-learning method, guided by weakly supervised lesion attention, for accelerated MRI oriented by downstream clinical needs. In particular, we construct a lesion-focused MRI reconstruction model, which incorporates customized learnable regularizations that can be learned efficiently by using only image-level labels to improve potential lesion reconstruction but preserve overall image quality. We then design a dedicated iterative algorithm to solve this task-driven reconstruction model, which is further unfolded as a cascaded deep network for lesion-focused fast imaging. Comprehensive experiments on two public datasets, i.e., fastMRI and Stanford Knee MRI Multi-Task Evaluation (SKM-TEA), demonstrate that our approach, referred to as Lesion-Focused MRI (LF-MRI), surpassed existing accelerated MRI methods by relatively large margins. Remarkably, LF-MRI led to substantial improvements in areas showing pathology. The source code and pretrained models will be publicly available at https://github.com/ladderlab-xjtu/LF-MRI.

BackgroundBrain atrophy is a hallmark of multiple sclerosis (MS). For clinical translatability and individual-level predictions, brain atrophy needs to be put into context of the broader population, using reference or normative models. MethodsReference models of MRI-derived brain volumes were established from a large healthy control (HC) multi-cohort dataset (N=63 115, 51% females). The reference models were applied to two independent MS cohorts (N=362, T1w-scans=953, follow-up time up to 12 years) to assess deviations from the reference, defined as Z-values. We assessed the overlap of deviation profiles and their stability over time using individual-level transitions towards or out of significant reference deviation states (|Z|>1{middle dot}96). A negative binomial model was used for case-control comparisons of the number of extreme deviations. Linear models were used to assess differences in Z-score deviations between MS and propensity-matched HCs, and associations with clinical scores at baseline and over time. The utilized normative BrainReference models, scripts and usage instructions are freely available. FindingsWe identified a temporally stable, brain morphometric phenotype of MS. The right and left thalami most consistently showed significantly lower-than-reference volumes in MS (25% and 26% overlap across the sample). The number of such extreme smaller-than-reference values was 2{middle dot}70 in MS compared to HC (4{middle dot}51 versus 1{middle dot}67). Additional deviations indicated stronger disability (Expanded Disability Status Scale: {beta}=0{middle dot}22, 95% CI 0{middle dot}12 to 0{middle dot}32), Paced Auditory Serial Addition Test score ({beta}=-0{middle dot}27, 95% CI -0{middle dot}52 to -0{middle dot}02), and Fatigue Severity Score ({beta}=0{middle dot}29, 95% CI 0{middle dot}05 to 0{middle dot}53) at baseline, and over time with EDSS ({beta}=0{middle dot}07, 95% CI 0{middle dot}02 to 0{middle dot}13). We additionally provide detailed maps of reference-deviations and their associations with clinical assessments. InterpretationWe present a heterogenous brain phenotype of MS which is associated with clinical manifestations, and particularly implicating the thalamus. The findings offer potential to aid diagnosis and prognosis of MS. FundingNorwegian MS-union, Research Council of Norway (#223273; #324252); the South-Eastern Norway Regional Health Authority (#2022080); and the European Unions Horizon2020 Research and Innovation Programme (#847776, #802998). Research in contextO_ST_ABSEvidence before this studyC_ST_ABSReference values and normative models have yet to be widely applied to neuroimaging assessments of neurological disorders such as multiple sclerosis (MS). We conducted a literature search in PubMed and Embase (Jan 1, 2000-September 12, 2025) using the terms "MRI" AND "multiple sclerosis", with and without the keywords "normative model*" and "atrophy", without language restrictions. While normative models have been applied in psychiatric and developmental disorders, few studies have addressed their use in neurological conditions. Existing MS research has largely focused on global atrophy and has not provided regional reference charts or established links to clinical and cognitive outcomes. Added value of this studyWe provide regionally detailed brain morphometry maps derived from a heterogeneous MS cohort spanning wide ranges of age, sex, clinical phenotype, disease duration, disability, and scanner characteristics. By leveraging normative modelling, our approach enables individualised brain phenotyping of MS in relation to a population based normative sample. The analyses reveal clinically meaningful and spatially consistent patterns of smaller brain volumes, particularly in the thalamus and frontal cortical regions, which are linked to disability, cognitive impairment, and fatigue. Robustness across scanners, centres, and longitudinal follow-up supports the stability and generalisability of these findings to real-world MS populations. Implications of all the available evidenceNormative modelling offers an individualised, sensitive, and interpretable approach to quantifying brain structure in MS by providing individual-specific reference values, supporting earlier detection of neurodegeneration and improved patient stratification. A consistent pattern of thalamic and fronto-parietal deviations defines a distinct morphometric profile of MS, with potential utility for early and personalised diagnosis and disease monitoring in clinical practice and clinical trials.

ObjectiveThis study aims to enhance breast cancer diagnosis by developing an automated deep learning framework for real-time, quantitative ultrasound imaging. Breast cancer is the second leading cause of cancer-related deaths among women, and early detection is crucial for improving survival rates. Conventional ultrasound, valued for its non-invasive nature and real-time capability, is limited by qualitative assessments and inter-observer variability. Quantitative ultrasound (QUS) methods, including Nakagami imaging--which models the statistical distribution of backscattered signals and lesion morphology--present an opportunity for more objective analysis. MethodsThe proposed framework integrates three convolutional neural networks (CNNs): (1) NakaSynthNet, synthesizing quantitative Nakagami parameter images from B-mode ultrasound; (2) SegmentNet, enabling automated lesion segmentation; and (3) FeatureNet, which combines anatomical and statistical features for classifying lesions as benign or malignant. Training utilized a diverse dataset of 110,247 images, comprising clinical B-mode scans and various simulated examples (fruit, mammographic lesions, digital phantoms). Quantitative performance was evaluated using mean squared error (MSE), structural similarity index (SSIM), segmentation accuracy, sensitivity, specificity, and area under the curve (AUC). ResultsNakaSynthNet achieved real-time synthesis at 21 frames/s, with MSE of 0.09% and SSIM of 98%. SegmentNet reached 98.4% accuracy, and FeatureNet delivered 96.7% overall classification accuracy, 93% sensitivity, 98% specificity, and an AUC of 98%. ConclusionThe proposed multi-parametric deep learning pipeline enables accurate, real-time breast cancer diagnosis from ultrasound data using objective quantitative imaging. SignificanceThis framework advances the clinical utility of ultrasound by reducing subjectivity and providing robust, multi-parametric information for improved breast cancer detection.

Large language models (LLMs) are gaining attention for their potential to enhance radiology workflows by addressing challenges such as increasing workloads and staff shortages. However, limited knowledge among radiologists and concerns about their practical implementation and ethical implications present challenges. This study investigates radiologists' perspectives on the use of LLMs, exploring their potential benefits, challenges, and impact on workflows and professional roles. An exploratory, qualitative study was conducted using 12 semi-structured interviews with radiology experts. Data were analyzed to assess participants' awareness, attitudes, and perceived applications of LLMs in radiology. LLMs were identified as promising tools for reducing workloads by streamlining tasks like summarizing clinical histories and generating standardized reports, improving communication and efficiency. Participants expressed openness to LLM integration but noted concerns about their impact on human interaction, ethical standards, and liability. The role of radiologists is expected to evolve with LLM adoption, with a shift toward data stewardship and interprofessional collaboration. Barriers to implementation included limited awareness, regulatory constraints, and outdated infrastructure. The integration of LLMs is hindered by regulatory challenges, outdated infrastructure, and limited awareness among radiologists. Policymakers should establish clear, practical regulations to address liability and ethical concerns while ensuring compliance with privacy standards. Investments in modernizing clinical infrastructure and expanding training programs are critical to enable radiologists to effectively use these tools. By addressing these barriers, LLMs can enhance efficiency, reduce workloads, and improve patient care, while preserving the central role of radiologists in diagnostic and therapeutic processes.

Idiopathic normal pressure hydrocephalus (iNPH) is a common and debilitating condition whose diagnosis is made challenging due to the unspecific and common clinical presentation. The aim of our study was to determine if data driven patterns of cerebrospinal fluid (CSF) distribution can be used to predict iNPH diagnosis and response to treatment. We established a cohort of iNPH patients and age/sex-matched controls. We used Non-negative Matrix Factorization (NMF) on CSF probability maps from segmentation of T1-weighted MRI to obtain patterns or components of CSF distribution across participants and a load on each component in each participant. Visual assessment of morphologic phenotype was performed by a neuroradiologist, and clinical symptom improvement was assessed via retrospective chart review. We used the NMF component loads to predict diagnosis and clinical outcome after ventriculoperitoneal shunt placement for treatment of iNPH. Similar models were developed using manual Evan's index and callosal angle measurements. We included 98 iNPH patients and 98 controls split into test (20 %) and train (80 %) sets. The optimal NMF decomposition identified 7 patterns of CSF distribution in our cohort. Accuracy for predicting a clinical diagnosis of iNPH using the automated NMF model was 96 %/97 % in the train/test sets, which was similar to the performance of the manual measure models (92 %/97 %). Visualizing the voxels that contributed most to the NMF models revealed that the voxels most associated with a disproportionately enlarged subarachnoid space hydrocephalus (DESH) were the ones with higher probability of iNPH diagnosis. Neither NMF nor manual metrics performed well for prediction of qualitative clinical outcomes. NMF-generated patterns of CSF distribution showed high accuracy in discerning individuals with iNPH from controls. The patterns most relying on DESH features showed highest potential for independently predicting NPH diagnosis. The algorithm we proposed should not be perceived as a replacement for human expertise but rather as an additional tool to assist clinicians in achieving accurate diagnoses.

To automatically and accurately perform three-dimensional reconstruction of dilated and non-dilated bile ducts based on magnetic resonance cholangiopancreatography (MRCP) data, assisting in the formulation of optimal surgical plans and guiding precise bile duct surgery. A total of 249 consecutive patients who underwent standardized 3D-MRCP scans were randomly divided into a training cohort (n = 208) and a testing cohort (n = 41). Ground truth segmentation was manually delineated by two hepatobiliary surgeons or radiologists following industry certification procedures and reviewed by two expert-level physicians for biliary surgery planning. The deep learning semantic segmentation model was constructed using the nnU-Net framework. Model performance was assessed by comparing model predictions with ground truth segmentation as well as real surgical scenarios. The generalization of the model was tested on a dataset of 10 3D-MRCP scans from other centers, with ground truth segmentation of biliary structures. The evaluation was performed on 41 internal test sets and 10 external test sets, with mean Dice Similarity Coefficient (DSC) values of respectively 0.9403 and 0.9070. The correlation coefficient between the 3D model based on automatic segmentation predictions and the ground truth results exceeded 0.95. The 95 % limits of agreement (LoA) for biliary tract length ranged from -4.456 to 4.781, and for biliary tract volume ranged from -3.404 to 3.650 ml. Furthermore, the intraoperative Indocyanine green (ICG) fluorescence imaging and operation situation validated that this model can accurately reconstruct biliary landmarks. By leveraging a deep learning algorithmic framework, an AI model can be trained to perform automatic and accurate 3D reconstructions of non-dilated bile ducts, thereby providing guidance for the preoperative planning of complex biliary surgeries.

Radiology report generation (RRG) methods often lack sufficient medical knowledge to produce clinically accurate reports. A scene graph provides comprehensive information for describing objects within an image. However, automatically generated radiology scene graphs (RSG) may contain noise annotations and highly overlapping regions, posing challenges in utilizing RSG to enhance RRG. To this end, we propose Scene Graph aided RRG (SGRRG), a framework that leverages an automatically generated RSG and copes with noisy supervision problems in the RSG with a transformer-based module, effectively distilling medical knowledge in an end-to-end manner. SGRRG is composed of a dedicated scene graph encoder responsible for translating the radiography into a RSG, and a scene graph-aided decoder that takes advantage of both patch-level and region-level visual information and mitigates the noisy annotation problem in the RSG. The incorporation of both patch-level and region-level features, alongside the integration of the essential RSG construction modules, enhances our framework's flexibility and robustness, enabling it to readily exploit prior advanced RRG techniques. A fine-grained, sentence-level attention method is designed to better distill the RSG information. Additionally, we introduce two proxy tasks to enhance the model's ability to produce clinically accurate reports. Extensive experiments demonstrate that SGRRG outperforms previous state-of-the-art methods in report generation and can better capture abnormal findings. Code is available at https://github.com/Markin-Wang/SGRRG.

To investigate the imaging performance of deep-learning reconstruction on multiplexed sensitivity encoding (MUSE DL) compared to single-shot diffusion-weighted imaging (SS-DWI) in the breast. In this prospective, institutional review board-approved study, both single-shot (SS-DWI) and multi-shot MUSE DWI were performed on patients. MUSE DWI was processed using deep-learning reconstruction (MUSE DL). Quantitative analysis included calculating apparent diffusion coefficients (ADCs), signal-to-noise ratio (SNR) within fibroglandular tissue (FGT), adjacent pectoralis muscle, and breast tumors. The Hausdorff distance (HD) was used as a distortion index to compare breast contours between T2-weighted anatomical images, SS-DWI, and MUSE images. Subjective visual qualitative analysis was performed using Likert scale. Quantitative analyses were assessed using Friedman's rank-based analysis with Bonferroni correction. Sixty-one female participants (mean age 49.07 years ± 11.0 [standard deviation]; age range 23-75 years) with 65 breast lesions were included in this study. All data were acquired using a 3 T MRI scanner. The MUSE DL yielded significant improvement in image quality compared with non-DL MUSE in both 2-shot and 4-shot settings (SNR enhancement FGT 2-shot DL 207.8 % [125.5-309.3],4- shot DL 175.1 % [102.2-223.5]). No significant difference was observed in the ADC between MUSE, MUSE DL, and SS-DWI in both benign (P = 0.154) and malignant tumors (P = 0.167). There was significantly less distortion in the 2- and 4-shot MUSE DL images (HD 3.11 mm, 2.58 mm) than in the SS-DWI images (4.15 mm, P < 0.001). MUSE DL enhances SNR, minimizes image distortion, and preserves lesion diagnosis accuracy and ADC values.

Background and ObjectivesIdentifying MS in children early and distinguishing it from other neuroinflammatory conditions of childhood is critical, as early therapeutic intervention can improve outcomes. The anterior visual pathway has been demonstrated to be of central importance in diagnostic considerations for MS and has recently been identified as a fifth topography in the McDonald Diagnostic Criteria for MS. Optical coherence tomography (OCT) provides high-resolution retinal imaging and reflects the structural integrity of the retinal nerve fiber and ganglion cell inner plexiform layers. Whether multimodal deep learning models can use OCT alone to diagnose pediatric MS (POMS) is unknown. MethodsWe analyzed 3D OCT scans collected prospectively through the Neuroinflammatory Registry of the Hospital for Sick Children (REB#1000005356). Raw macular and optic nerve head images, and 52 automatically segmented features were included. We evaluated three classification approaches: (1) deep learning models (e.g. ResNet, DenseNet) for representation learning followed by classical ML classifiers, (2) ML models trained on OCT-derived features, and (3) multimodal models combining both via early and late fusion. ResultsScans from individuals with POMS (onset 16.0 {+/-} 3.1 years, 51.0%F; 211 scans) and 29 children with non-inflammatory neurological conditions (13.1 {+/-} 4.0 years, 69.0%F, 52 scans) were included. The early fusion model achieved the highest performance (AUC: 0.87, F1: 0.87, Accuracy: 90%), outperforming both unimodal and late fusion models. The best unimodal feature-based model (SVC) yielded an AUC of 0.84, F1 of 0.85 and an accuracy of 85%, while the best image-based model (ResNet101 with Random Forest) achieved an AUC of 0.87, F1 of 0.79, and accuracy of 84%. Late fusion underperformed, reaching 82% accuracy but failing in the minority class. DiscussionMultimodal learning with early fusion significantly enhances diagnostic performance by combining spatial retinal information with clinically relevant structural features. This approach captures complementary patterns associated with MS pathology and shows promise as an AI-driven tool to support pediatric neuroinflammatory diagnosis.

Deep learning-based super-resolution (SR) reconstruction can obtain high-quality images with more detailed information. To compare multiparametric normal-resolution (NR) and SR magnetic resonance imaging (MRI) in predicting the histopathologic grade in hepatocellular carcinoma. We retrospectively analyzed a total of 826 patients from two medical centers (training 459; validation 196; test 171). T2-weighted imaging, diffusion-weighted imaging, and portal venous phases were collected. Tumor segmentations were conducted automatically by 3D U-Net. Based on generative adversarial network, we utilized 3D SR reconstruction to produce SR MRI. Radiomics models were developed and validated by XGBoost and Catboost. The predictive efficiency was demonstrated by calibration curves, decision curve analysis, area under the curve (AUC) and net reclassification index (NRI). We extracted 3045 radiomic features from both NR and SR MRI, retaining 29 and 28 features, respectively. For XGBoost models, SR MRI yielded higher AUC value than NR MRI in the validation and test cohorts (0.83 <i>vs</i> 0.79; 0.80 <i>vs</i> 0.78), respectively. Consistent trends were seen in CatBoost models: SR MRI achieved AUCs of 0.89 and 0.80 compared to NR MRI's 0.81 and 0.76. NRI indicated that the SR MRI models could improve the prediction accuracy by -1.6% to 20.9% compared to the NR MRI models. Deep learning-based SR MRI could improve the predictive performance of histopathologic grade in HCC. It may be a powerful tool for better stratification management for patients with operable HCC.