Radiomics is transforming medical imaging by extracting complex features that enhance disease diagnosis, prognosis, and treatment evaluation. However, traditional approaches face significant challenges, such as the need for manual feature engineering, high dimensionality, and limited sample sizes. This paper presents Radio DINO, a novel family of deep learning foundation models that leverage self-supervised learning (SSL) techniques from DINO and DINOV2, pretrained on the RadImageNet dataset. The novelty of our approach lies in (1) developing Radio DINO to capture rich semantic embeddings, enabling robust feature extraction without manual intervention, (2) demonstrating superior performance across various clinical tasks on the MedMNISTv2 dataset, surpassing existing models, and (3) enhancing the interpretability of the model by providing visualizations that highlight its focus on clinically relevant image regions. Our results show that Radio DINO has the potential to democratize advanced radiomics tools, making them accessible to healthcare institutions with limited resources and ultimately improving diagnostic and prognostic outcomes in radiology.

This study aimed to develop a machine learning model using breast ultrasound images to improve the non-invasive differential diagnosis between Sclerosing Adenosis (SA) and Invasive Ductal Carcinoma (IDC). 2046 ultrasound images from 772 SA and IDC patients were collected, Regions of Interest (ROI) were delineated, and features were extracted. The dataset was split into training and test cohorts, and feature selection was performed by correlation coefficients and Recursive Feature Elimination. 10 classifiers with Grid Search and 5-fold cross-validation were applied during model training. Receiver Operating Characteristic (ROC) curve and Youden index were used to model evaluation. SHapley Additive exPlanations (SHAP) was employed for model interpretation. Another 224 ROIs of 84 patients from other hospitals were used for external validation. For the ROI-level model, XGBoost with 18 features achieved an area under the curve (AUC) of 0.9758 (0.9654-0.9847) in the test cohort and 0.9906 (0.9805-0.9973) in the validation cohort. For the patient-level model, logistic regression with 9 features achieved an AUC of 0.9653 (0.9402-0.9859) in the test cohort and 0.9846 (0.9615-0.9978) in the validation cohort. The feature "Original shape Major Axis Length" was identified as the most important, with its value positively correlated with a higher likelihood of the sample being IDC. Feature contributions for specific ROIs were visualized as well. We developed explainable, ultrasound-based machine learning models with high performance for differentiating SA and IDC, offering a potential non-invasive tool for improved differential diagnosis. Question Accurately distinguishing between sclerosing adenosis (SA) and invasive ductal carcinoma (IDC) in a non-invasive manner has been a diagnostic challenge. Findings Explainable, ultrasound-based machine learning models with high performance were developed for differentiating SA and IDC, and validated well in external validation cohort. Critical relevance These models provide non-invasive tools to reduce misdiagnoses of SA and improve early detection for IDC.

ObjectiveTo develop and validate machine learning (ML) models for objective and comprehensive quantification of sagittal craniosynostosis (SCS) severity, enhancing clinical assessment, management, and research.DesignA cross-sectional study that combined the analysis of computed tomography (CT) scans and expert ratings.SettingThe study was conducted at a children's hospital and a major computer imaging institution. Our survey collected expert ratings from participating surgeons.ParticipantsThe study included 195 patients with nonsyndromic SCS, 221 patients with nonsyndromic metopic craniosynostosis (CS), and 178 age-matched controls. Fifty-four craniofacial surgeons participated in rating 20 patients head CT scans.InterventionsComputed tomography scans for cranial morphology assessment and a radiographic diagnosis of nonsyndromic SCS.Main OutcomesAccuracy of the proposed Sagittal Severity Score (SSS) in predicting expert ratings compared to cephalic index (CI). Secondary outcomes compared Likert ratings with SCS status, the predictive power of skull-based versus skin-based landmarks, and assessments of an unsupervised ML model, the Cranial Morphology Deviation (CMD), as an alternative without ratings.ResultsThe SSS achieved significantly higher accuracy in predicting expert responses than CI (<i>P</i> < .05). Likert ratings outperformed SCS status in supervising ML models to quantify within-group variations. Skin-based landmarks demonstrated equivalent predictive power as skull landmarks (<i>P</i> < .05, threshold 0.02). The CMD demonstrated a strong correlation with the SSS (Pearson coefficient: 0.92, Spearman coefficient: 0.90, <i>P</i> < .01).ConclusionsThe SSS and CMD can provide accurate, consistent, and comprehensive quantification of SCS severity. Implementing these data-driven ML models can significantly advance CS care through standardized assessments, enhanced precision, and informed surgical planning.

Early detection of glaucoma represents a vital factor in securing vision while the disease retains its position as one of the central causes of blindness worldwide. The current glaucoma screening strategies with expert interpretation depend on complex and time-consuming procedures which slow down both diagnosis processes and intervention timing. This research adopts a complex automated glaucoma diagnostic system that combines optimized segmentation solutions together with classification platforms. The proposed segmentation approach implements an enhanced version of U-Net++ using dynamic parameter control provided by GWO to segment optic disc and cup regions in retinal fundus images. Through the implementation of GWO the algorithm uses wolf-pack hunting strategies to adjust parameters dynamically which enables it to locate diverse textural patterns inside images. The system uses a CapsNet capsule network for classification because it maintains visual spatial organization to detect glaucoma-related patterns precisely. The developed system secures an evaluation accuracy of 95.1% in segmentation and classification tasks better than typical approaches. The automated system eliminates and enhances clinical diagnostic speed as well as diagnostic precision. The tool stands out because of its supreme detection accuracy and reliability thus making it an essential clinical early-stage glaucoma diagnostic system and a scalable healthcare deployment solution. To develop an advanced automated glaucoma diagnostic system by integrating an optimized U-Net++ segmentation model with a Capsule Network (CapsNet) classifier, enhanced through Grey Wolf Optimization Algorithm (GWOA), for precise segmentation of optic disc and cup regions and accurate glaucoma classification from retinal fundus images. This study proposes a two-phase computer-assisted diagnosis (CAD) framework. In the segmentation phase, an enhanced U-Net++ model, optimized by GWOA, is employed to accurately delineate the optic disc and cup regions in fundus images. The optimization dynamically tunes hyperparameters based on grey wolf hunting behavior for improved segmentation precision. In the classification phase, a CapsNet architecture is used to maintain spatial hierarchies and effectively classify images as glaucomatous or normal based on segmented outputs. The performance of the proposed model was validated using the ORIGA retinal fundus image dataset, and evaluated against conventional approaches. The proposed GWOA-UNet++ and CapsNet framework achieved a segmentation and classification accuracy of 95.1%, outperforming existing benchmark models such as MTA-CS, ResFPN-Net, DAGCN, MRSNet and AGCT. The model demonstrated robustness against image irregularities, including variations in optic disc size and fundus image quality, and showed superior performance across accuracy, sensitivity, specificity, precision, and F1-score metrics. The developed automated glaucoma detection system exhibits enhanced diagnostic accuracy, efficiency, and reliability, offering significant potential for early-stage glaucoma detection and clinical decision support. Future work will involve large-scale multi-ethnic dataset validation, integration with clinical workflows, and deployment as a mobile or cloud-based screening tool.

To develop a deep learning model based on Residual Networks (ResNet) for the automated and accurate identification of contrast phases in abdominal CT images. A dataset of 1175 abdominal contrast-enhanced CT scans was retrospectively collected for the model development, and another independent dataset of 215 scans from five hospitals was collected for external testing. Each contrast phase was independently annotated by two radiologists. A ResNet-based model was developed to automatically classify phases into the early arterial phase (EAP) or late arterial phase (LAP), portal venous phase (PVP), and delayed phase (DP). Strategy A identified EAP or LAP, PVP, and DP in one step. Strategy B used a two-step approach: first classifying images as arterial phase (AP), PVP, and DP, then further classifying AP images into EAP or LAP. Model performance and strategy comparison were evaluated. In the internal test set, the overall accuracy of the two-step strategy was 98.3% (283/288; p < 0.001), significantly higher than that of the one-step strategy (91.7%, 264/288; p < 0.001). In the external test set, the two-step model achieved an overall accuracy of 99.1% (639/645), with sensitivities of 95.1% (EAP), 99.4% (LAP), 99.5% (PVP), and 99.5% (DP). The proposed two-step ResNet-based model provides highly accurate and robust identification of contrast phases in abdominal CT images, outperforming the conventional one-step strategy. Automated and accurate identification of contrast phases in abdominal CT images provides a robust tool for improving image quality control and establishes a strong foundation for AI-driven applications, particularly those leveraging contrast-enhanced abdominal imaging data. Accurate identification of contrast phases is crucial in abdominal CT imaging. The two-step ResNet-based model achieved superior accuracy across internal and external datasets. Automated phase classification strengthens imaging quality control and supports precision AI applications.

Chest radiography requires effective quality control (QC) to reduce high retake rates. However, existing QC measures are all retrospective and implemented after exposure, often necessitating retakes when image quality fails to meet standards and thereby increasing radiation exposure to patients. To address this issue, we proposed a 3D human body (3D-HB) reconstruction algorithm to realize prospective QC. Our objective was to investigate the feasibility of using the reconstructed 3D-HB for prospective QC in chest radiography and evaluate its impact on retake rates.&#xD;Approach: This prospective study included patients indicated for posteroanterior (PA) and lateral (LA) chest radiography in May 2024. A 3D-HB reconstruction algorithm integrating the SMPL-X model and the HybrIK-X algorithm was proposed to convert patients' 2D images into 3D-HBs. QC metrics regarding patient positioning and collimation were assessed using chest radiographs (reference standard) and 3D-HBs, with results compared using ICCs, linear regression, and receiver operating characteristic curves. For retake rate evaluation, a real-time 3D-HB visualization interface was developed and chest radiography was conducted in two four-week phases: the first without prospective QC and the second with prospective QC. Retake rates between the two phases were compared using chi-square tests. &#xD;Main results: 324 participants were included (mean age, 42 years±19 [SD]; 145 men; 324 PA and 294 LA examinations). The ICCs for the clavicle and midaxillary line angles were 0.80 and 0.78, respectively. Linear regression showed good relation for clavicle angles (R2: 0.655) and midaxillary line angles (R2: 0.616). In PA chest radiography, the AUCs of 3D-HBs were 0.89, 0.87, 0.91 and 0.92 for assessing scapula rotation, lateral tilt, centered positioning and central X-ray alignment respectively, with 97% accuracy in collimation assessment. In LA chest radiography, the AUCs of 3D-HBs were 0.87, 0.84, 0.87 and 0.88 for assessing arms raised, chest rotation, centered positioning and central X-ray alignment respectively, with 94% accuracy in collimation assessment. In retake rate evaluation, 3995 PA and 3295 LA chest radiographs were recorded. The implementation of prospective QC based on the 3D-HB reduced retake rates from 8.6% to 3.5% (PA) and 19.6% to 4.9% (LA) (p < .001).&#xD;Significance: The reconstructed 3D-HB is a feasible tool for prospective QC in chest radiography, providing real-time feedback on patient positioning and collimation before exposure. Prospective QC based on the reconstructed 3D-HB has the potential to reshape the future of radiography QC by significantly reducing retake rates and improving clinical standardization.

The generalization of deep learning-based low-dose computed tomography (CT) reconstruction models to doses unseen in the training data is important and remains challenging. Previous efforts heavily rely on paired data to improve the generalization performance and robustness through collecting either diverse CT data for re-training or a few test data for fine-tuning. Recently, diffusion models have shown promising and generalizable performance in low-dose CT (LDCT) reconstruction, however, they may produce unrealistic structures due to the CT image noise deviating from Gaussian distribution and imprecise prior information from the guidance of noisy LDCT images. In this paper, we propose a noise-inspired diffusion model for generalizable LDCT reconstruction, termed NEED, which tailors diffusion models for noise characteristics of each domain. First, we propose a novel shifted Poisson diffusion model to denoise projection data, which aligns the diffusion process with the noise model in pre-log LDCT projections. Second, we devise a doubly guided diffusion model to refine reconstructed images, which leverages LDCT images and initial reconstructions to more accurately locate prior information and enhance reconstruction fidelity. By cascading these two diffusion models for dual-domain reconstruction, our NEED requires only normal-dose data for training and can be effectively extended to various unseen dose levels during testing via a time step matching strategy. Extensive qualitative, quantitative, and segmentation-based evaluations on two datasets demonstrate that our NEED consistently outperforms state-of-the-art methods in reconstruction and generalization performance. Source code is made available at https://github.com/qgao21/NEED.

Background: Conventional cardiovascular magnetic resonance (CMR) in paediatric and congenital heart disease uses 2D, breath-hold, balanced steady state free precession (bSSFP) cine imaging for assessment of function and cardiac-gated, respiratory-navigated, static 3D bSSFP whole-heart imaging for anatomical assessment. Our aim is to concatenate a stack 2D free-breathing real-time cines and use Deep Learning (DL) to create an isotropic a fully segmented 3D cine dataset from these images. Methods: Four DL models were trained on open-source data that performed: a) Interslice contrast correction; b) Interslice respiratory motion correction; c) Super-resolution (slice direction); and d) Segmentation of right and left atria and ventricles (RA, LA, RV, and LV), thoracic aorta (Ao) and pulmonary arteries (PA). In 10 patients undergoing routine cardiovascular examination, our method was validated on prospectively acquired sagittal stacks of real-time cine images. Quantitative metrics (ventricular volumes and vessel diameters) and image quality of the 3D cines were compared to conventional breath hold cine and whole heart imaging. Results: All real-time data were successfully transformed into 3D cines with a total post-processing time of <1 min in all cases. There were no significant biases in any LV or RV metrics with reasonable limits of agreement and correlation. There is also reasonable agreement for all vessel diameters, although there was a small but significant overestimation of RPA diameter. Conclusion: We have demonstrated the potential of creating a 3D-cine data from concatenated 2D real-time cine images using a series of DL models. Our method has short acquisition and reconstruction times with fully segmented data being available within 2 minutes. The good agreement with conventional imaging suggests that our method could help to significantly speed up CMR in clinical practice.

To evaluate the depiction capability of fine lung nodules and airways using high-resolution settings on ultra-high-resolution energy-integrating detector CT (UHR-CT), incorporating large matrix sizes, thin-slice thickness, and iterative reconstruction (IR)/deep-learning reconstruction (DLR), using cadaveric human lungs and corresponding histological images. Images of 20 lungs were acquired using conventional CT (CCT), UHR-CT, and photon-counting detector CT (PCD-CT). CCT images were reconstructed with a 512 matrix and IR (CCT-512-IR). UHR-CT images were reconstructed with four settings by varying the matrix size and the reconstruction method: UHR-512-IR, UHR-1024-IR, UHR-2048-IR, and UHR-1024-DLR. Two imaging settings of PCD-CT were used: PCD-512-IR and PCD-1024-IR. CT images were visually evaluated and compared with histology. Overall, 6769 nodules (median: 1321 µm) and 92 airways (median: 851 µm) were evaluated. For nodules, UHR-2048-IR outperformed CCT-512-IR, UHR-512-IR, and UHR-1024-IR (p < 0.001). UHR-1024-DLR showed no significant difference from UHR-2048-IR in the overall nodule score after Bonferroni correction (uncorrected p = 0.043); however, for nodules > 1000 μm, UHR-2048-IR demonstrated significantly better scores than UHR-1024-DLR (p = 0.003). For airways, UHR-1024-IR and UHR-512-IR showed significant differences (p < 0.001), with no notable differences among UHR-1024-IR, UHR-2048-IR, and UHR-1024-DLR. UHR-2048-IR detected nodules and airways with median diameters of 604 µm and 699 µm, respectively. No significant difference was observed between UHR-512-IR and PCD-512-IR (p > 0.1). PCD-1024-IR outperformed UHR-CTs for nodules > 1000 μm (p ≤ 0.001), while UHR-1024-DLR outperformed PCD-1024-IR for airways > 1000 μm (p = 0.005). UHR-2048-IR demonstrated the highest scores among the evaluated EID-CT images. UHR-CT showed potential for detecting submillimeter nodules and airways. With the 512 matrix, UHR-CT demonstrated performance comparable to PCD-CT. Question There are scarce data evaluating the depiction capabilities of ultra-high-resolution energy-integrating detector CT (UHR-CT) for fine structures, nor any comparisons with photon-counting detector CT (PCD-CT). Findings UHR-CT depicted nodules and airways with median diameters of 604 µm and 699 µm, showing no significant difference from PCD-CT with the 512 matrix. Clinical relevance High-resolution imaging is crucial for lung diagnosis. UHR-CT has the potential to contribute to pulmonary nodule diagnosis and airway disease evaluation by detecting fine opacities and airways.

Multimodal Large Language Models (MLLMs) have emerged as a promising way to automate Radiology Report Generation (RRG). In this work, we systematically investigate the design space of 3D MLLMs, including visual input representation, projectors, Large Language Models (LLMs), and fine-tuning techniques for 3D CT report generation. We also introduce two knowledge-based report augmentation methods that improve performance on the GREEN score by up to 10\%, achieving the 2nd place on the MICCAI 2024 AMOS-MM challenge. Our results on the 1,687 cases from the AMOS-MM dataset show that RRG is largely independent of the size of LLM under the same training protocol. We also show that larger volume size does not always improve performance if the original ViT was pre-trained on a smaller volume size. Lastly, we show that using a segmentation mask along with the CT volume improves performance. The code is publicly available at https://github.com/bowang-lab/AMOS-MM-Solution