This study aimed to compare two deep learning reconstruction (DLR) techniques (AiCE mild; AiCE strong) with two established methods-iterative reconstruction (IR) and filtered back projection (FBP)-for the detection of monosodium urate (MSU) in dual-energy computed tomography (DECT). An ex vivo bio-phantom and a raster phantom were prepared by inserting syringes containing different MSU concentrations and scanned in a 320-rows volume DECT scanner at different tube currents. The scans were reconstructed in a soft tissue kernel using the four reconstruction techniques mentioned above, followed by quantitative assessment of MSU volumes and image quality parameters, i.e., signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). Both DLR techniques outperformed conventional IR and FBP in terms of volume detection and image quality. Notably, unlike IR and FBP, the two DLR methods showed no positive correlation of the MSU detection rate with the CT dose index (CTDIvol) in the bio-phantom. Our study highlights the potential of DLR for DECT imaging in gout, where it offers enhanced detection sensitivity, improved image contrast, reduced image noise, and lower radiation exposure. Further research is needed to assess the clinical reliability of this approach.

<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 estimate the total lung volume (TLV) from real and synthetic frontal chest radiographs (CXR) on a pixel level using lung thickness maps generated by a U-Net deep learning model. Materials and Methods This retrospective study included 5,959 chest CT scans from two public datasets: the lung nodule analysis 2016 (<i>n</i> = 656) and the Radiological Society of North America (RSNA) pulmonary embolism detection challenge 2020 (<i>n</i> = 5,303). Additionally, 72 participants were selected from the Klinikum Rechts der Isar dataset (October 2018 to December 2019), each with a corresponding chest radiograph taken within seven days. Synthetic radiographs and lung thickness maps were generated using forward projection of CT scans and their lung segmentations. A U-Net model was trained on synthetic radiographs to predict lung thickness maps and estimate TLV. Model performance was assessed using mean squared error (MSE), Pearson correlation coefficient <b>(r)</b>, and two-sided Student's t-distribution. Results The study included 72 participants (45 male, 27 female, 33 healthy: mean age 62 years [range 34-80]; 39 with chronic obstructive pulmonary disease: mean age 69 years [range 47-91]). TLV predictions showed low error rates (MSEPublic-Synthetic = 0.16 L², MSEKRI-Synthetic = 0.20 L², MSEKRI-Real = 0.35 L²) and strong correlations with CT-derived reference standard TLV (nPublic-Synthetic = 1,191, r = 0.99, <i>P</i> < .001; nKRI-Synthetic = 72, r = 0.97, <i>P</i> < .001; nKRI-Real = 72, r = 0.91, <i>P</i> < .001). When evaluated on different datasets, the U-Net model achieved the highest performance for TLV estimation on the Luna16 test dataset, with the lowest mean squared error (MSE = 0.09 L²) and strongest correlation (<i>r</i> = 0.99, <i>P</i> <.001) compared with CT-derived TLV. Conclusion The U-Net-generated pixel-level lung thickness maps successfully estimated TLV for both synthetic and real radiographs. ©RSNA, 2025.

Chemical Exchange Saturation Transfer (CEST) MRI has demonstrated its remarkable ability to enhance the detection of macromolecules and metabolites with low concentrations. While CEST mapping is essential for quantifying molecular information, conventional methods face critical limitations: model-based approaches are constrained by limited sensitivity and robustness depending heavily on parameter setups, while data-driven deep learning methods lack generalizability across heterogeneous datasets and acquisition protocols. To overcome these challenges, we propose a Lorentzian-model Informed Neural Representation (LINR) framework for high-quality CEST mapping. LINR employs a self-supervised neural architecture embedding the Lorentzian equation - the fundamental biophysical model of CEST signal evolution - to directly reconstruct high-sensitivity parameter maps from raw z-spectra, eliminating dependency on labeled training data. Convergence of the self-supervised training strategy is guaranteed theoretically, ensuring LINR's mathematical validity. The superior performance of LINR in capturing CEST contrasts is revealed through comprehensive evaluations based on synthetic phantoms and in-vivo experiments (including tumor and Alzheimer's disease models). The intuitive parameter-free design enables adaptive integration into diverse CEST imaging workflows, positioning LINR as a versatile tool for non-invasive molecular diagnostics and pathophysiological discovery.

Liver cirrhosis represents the end stage of chronic liver disease, characterized by extensive fibrosis and nodular regeneration that significantly increases mortality risk. While magnetic resonance imaging (MRI) offers a non-invasive assessment, accurately segmenting cirrhotic livers presents substantial challenges due to morphological alterations and heterogeneous signal characteristics. Deep learning approaches show promise for automating these tasks, but progress has been limited by the absence of large-scale, annotated datasets. Here, we present CirrMRI600+, the first comprehensive dataset comprising 628 high-resolution abdominal MRI scans (310 T1-weighted and 318 T2-weighted sequences, totaling nearly 40,000 annotated slices) with expert-validated segmentation labels for cirrhotic livers. The dataset includes demographic information, clinical parameters, and histopathological validation where available. Additionally, we provide benchmark results from 11 state-of-the-art deep learning experiments to establish performance standards. CirrMRI600+ enables the development and validation of advanced computational methods for cirrhotic liver analysis, potentially accelerating progress toward automated Cirrhosis visual staging and personalized treatment planning.

Dynamic MRI plays a vital role in clinical practice by capturing both spatial details and dynamic motion, but its high spatiotemporal resolution is often limited by long scan times. Deep learning (DL)-based methods have shown promising performance in accelerating dynamic MRI. However, most existing algorithms rely on large fully-sampled datasets for training, which are difficult to acquire. Recently, implicit neural representation (INR) has emerged as a powerful scan-specific paradigm for accelerated MRI, which models signals as a continuous function over spatiotemporal coordinates. Although this approach achieves efficient continuous modeling of dynamic images and robust reconstruction, it faces challenges in recovering fine details and increasing computational demands for high dimensional data representation. To enhance both efficiency and reconstruction quality, we propose TenF-INR, a novel patch-based unsupervised framework that employs INR to model bases of tensor decomposition, enabling efficient and accurate modeling of dynamic MR images with learnable tensor functions. By exploiting strong correlations in similar spatial image patches and in the temporal direction, TenF-INR enforces multidimensional low-rankness and implements patch-based reconstruction with the benefits of continuous modeling. We compare TenF-INR with state-of-the-art methods, including supervised DL methods and unsupervised approaches. Experimental results demonstrate that TenF-INR achieves high acceleration factors up to 21, outperforming all comparison methods in image quality, temporal fidelity, and quantitative metrics, even surpassing the supervised methods.

Recent advancements in multimodal Large Language Models (LLMs) have significantly enhanced the automation of medical image analysis, particularly in generating radiology reports from chest X-rays (CXR). However, these models still suffer from hallucinations and clinically significant errors, limiting their reliability in real-world applications. In this study, we propose Look & Mark (L&M), a novel grounding fixation strategy that integrates radiologist eye fixations (Look) and bounding box annotations (Mark) into the LLM prompting framework. Unlike conventional fine-tuning, L&M leverages in-context learning to achieve substantial performance gains without retraining. When evaluated across multiple domain-specific and general-purpose models, L&M demonstrates significant gains, including a 1.2% improvement in overall metrics (A.AVG) for CXR-LLaVA compared to baseline prompting and a remarkable 9.2% boost for LLaVA-Med. General-purpose models also benefit from L&M combined with in-context learning, with LLaVA-OV achieving an 87.3% clinical average performance (C.AVG)-the highest among all models, even surpassing those explicitly trained for CXR report generation. Expert evaluations further confirm that L&M reduces clinically significant errors (by 0.43 average errors per report), such as false predictions and omissions, enhancing both accuracy and reliability. These findings highlight L&M's potential as a scalable and efficient solution for AI-assisted radiology, paving the way for improved diagnostic workflows in low-resource clinical settings.

Limited DXA access hinders osteoporosis screening. This proof-of-concept study proposes using widely available knee X-rays for opportunistic Bone Mineral Density (BMD) estimation via deep learning, emphasizing robust uncertainty quantification essential for clinical use. An EfficientNet model was trained on the OAI dataset to predict BMD from bilateral knee radiographs. Two Test-Time Augmentation (TTA) methods were compared: traditional averaging and a multi-sample approach. Crucially, Split Conformal Prediction was implemented to provide statistically rigorous, patient-specific prediction intervals with guaranteed coverage. Results showed a Pearson correlation of 0.68 (traditional TTA). While traditional TTA yielded better point predictions, the multi-sample approach produced slightly tighter confidence intervals (90%, 95%, 99%) while maintaining coverage. The framework appropriately expressed higher uncertainty for challenging cases. Although anatomical mismatch between knee X-rays and standard DXA limits immediate clinical use, this method establishes a foundation for trustworthy AI-assisted BMD screening using routine radiographs, potentially improving early osteoporosis detection.

Lung cancer is the leading cause of cancer mortality worldwide, and non-invasive methods for detecting key mutations and staging are essential for improving patient outcomes. Here, we compare the performance of two machine learning models - FMCIB+XGBoost, a supervised model with domain-specific pretraining, and Dinov2+ABMIL, a self-supervised model with attention-based multiple-instance learning - on 3D lung nodule data from the Stanford Radiogenomics and Lung-CT-PT-Dx cohorts. In the task of KRAS and EGFR mutation detection, FMCIB+XGBoost consistently outperformed Dinov2+ABMIL, achieving accuracies of 0.846 and 0.883 for KRAS and EGFR mutations, respectively. In cancer staging, Dinov2+ABMIL demonstrated competitive generalization, achieving an accuracy of 0.797 for T-stage prediction in the Lung-CT-PT-Dx cohort, suggesting SSL's adaptability across diverse datasets. Our results emphasize the clinical utility of supervised models in mutation detection and highlight the potential of SSL to improve staging generalization, while identifying areas for enhancement in mutation sensitivity.

PurposeTo develop and validate deep learning (DL)-based models for classifying geographic atrophy (GA) subtypes using Optical Coherence Tomography (OCT) scans across four clinical classification tasks. DesignRetrospective comparative study evaluating three DL architectures on OCT data with two experimental approaches. Subjects455 OCT volumes (258 Central GA [CGA], 74 Non-Central GA [NCGA], 123 no GA [NGA]) from 104 patients at Atrium Health Wake Forest Baptist. For GA versus age-related macular degeneration (AMD) classification, we supplemented our dataset with AMD cases from four public repositories. MethodsWe implemented ResNet50, MobileNetV2, and Vision Transformer (ViT-B/16) architectures using two approaches: (1) utilizing all B-scans within each OCT volume and (2) selectively using B-scans containing foveal regions. Models were trained using transfer learning, standardized data augmentation, and patient-level data splitting (70:15:15 ratio) for training, validation, and testing. Main Outcome MeasuresArea under the receiver operating characteristic curve (AUC-ROC), F1 score, and accuracy for each classification task (CGA vs. NCGA, CGA vs. NCGA vs. NGA, GA vs. NGA, and GA vs. other forms of AMD). ResultsViT-B/16 consistently outperformed other architectures across all classification tasks. For CGA versus NCGA classification, ViT-B/16 achieved an AUC-ROC of 0.728{+/-}0.083 and accuracy of 0.831{+/-}0.006 using selective B-scans. In GA versus NGA classification, ViT-B/16 attained an AUC-ROC of 0.950{+/-}0.002 and accuracy of 0.873{+/-}0.012 with selective B-scans. All models demonstrated exceptional performance in distinguishing GA from other AMD forms (AUC-ROC>0.998). For multi-class classification, ViT-B/16 achieved an AUC-ROC of 0.873{+/-}0.003 and accuracy of 0.751{+/-}0.002 using selective B-scans. ConclusionsOur DL approach successfully classifies GA subtypes with clinically relevant accuracy. ViT-B/16 demonstrates superior performance due to its ability to capture spatial relationships between atrophic regions and the foveal center. Focusing on B-scans containing foveal regions improved diagnostic accuracy while reducing computational requirements, better aligning with clinical practice workflows.

This work aimed to develop an automated method for quantifying the distribution and severity of perfusion changes on CT pulmonary angiography (CTPA) in patients with chronic thromboembolic pulmonary hypertension (CTEPH) and to assess their associations with clinical parameters and expert annotations. Following automated segmentation of the chest, a machine-learning model assuming three distributions of attenuation in the pulmonary parenchyma (hyperemic, normal, and oligemic) was fitted to the attenuation histogram of CTPA images using Bayesian analysis. The proportion of each component, its spatial heterogeneity (entropy), and center-to-periphery distribution of the attenuation were calculated and correlated with the findings on CTPA semi-quantitatively evaluated by radiologists and with clinical function tests. CTPA scans from 52 patients (mean age, 65.2 ± 13.0 years; 27 men) diagnosed with CTEPH were analyzed. An inverse correlation was observed between the proportion of normal parenchyma and brain natriuretic propeptide (proBNP, ρ = -0.485, p = 0.001), mean pulmonary arterial pressure (ρ = -0.417, p = 0.002) and pulmonary vascular resistance (ρ = -0.556, p < 0.0001), mosaic attenuation (ρ = -0.527, p < 0.0001), perfusion centralization (ρ = -0.489, p = < 0.0001), and right ventricular diameter (ρ = -0.451, p = 0.001). The entropy of hyperemic parenchyma showed a positive correlation with the pulmonary wedge pressure (ρ = 0.402, p = 0.003). The slope of center-to-periphery attenuation distribution correlated with centralization (ρ = -0.477, p < 0.0001), and with proBNP (ρ = -0.463, p = 0.002). This study validates an automated system that leverages Bayesian analysis to quantify the severity and distribution of perfusion changes in CTPA. The results show the potential of this method to support clinical evaluations of CTEPH by providing reproducible and objective measures. Question This study introduces an automated method for quantifying the extent and spatial distribution of pulmonary perfusion abnormalities in CTEPH using variational Bayesian estimation. Findings Quantitative measures describing the extent, heterogeneity, and distribution of perfusion changes demonstrate strong correlations with key clinical hemodynamic indicators. Clinical relevance The automated quantification of perfusion changes aligns closely with radiologists' evaluations, delivering a standardized, reproducible measure with clinical relevance.