Limited-angle computed tomography (LACT) offers improved temporal resolution and reduced radiation dose for cardiac imaging, but suffers from severe artifacts due to truncated projections. To address the ill-posedness of LACT reconstruction, we propose a two-stage diffusion framework guided by structured clinical metadata. In the first stage, a transformer-based diffusion model conditioned exclusively on metadata, including acquisition parameters, patient demographics, and diagnostic impressions, generates coarse anatomical priors from noise. The second stage further refines the images by integrating both the coarse prior and metadata to produce high-fidelity results. Physics-based data consistency is enforced at each sampling step in both stages using an Alternating Direction Method of Multipliers module, ensuring alignment with the measured projections. Extensive experiments on both synthetic and real cardiac CT datasets demonstrate that incorporating metadata significantly improves reconstruction fidelity, particularly under severe angular truncation. Compared to existing metadata-free baselines, our method achieves superior performance in SSIM, PSNR, nMI, and PCC. Ablation studies confirm that different types of metadata contribute complementary benefits, particularly diagnostic and demographic priors under limited-angle conditions. These findings highlight the dual role of clinical metadata in improving both reconstruction quality and efficiency, supporting their integration into future metadata-guided medical imaging frameworks.

Recently, the emergence of multitask deep learning models has enhanced catheterization procedures by providing tactile and visual perception data through an end-to-end architec- ture. This information is derived from a segmentation and force estimation head, which localizes the catheter in X-ray images and estimates the applied pressure based on its deflection within the image. These stereo vision architectures incorporate a CNN- based encoder-decoder that captures the dependencies between X-ray images from two viewpoints, enabling simultaneous 3D force estimation and stereo segmentation of the catheter. With these tasks in mind, this work approaches the problem from a new perspective. We propose a novel encoder-decoder Vision Transformer model that processes two input X-ray images as separate sequences. Given sequences of X-ray patches from two perspectives, the transformer captures long-range dependencies without the need to gradually expand the receptive field for either image. The embeddings generated by both the encoder and decoder are fed into two shared segmentation heads, while a regression head employs the fused information from the decoder for 3D force estimation. The proposed model is a stereo Vision Transformer capable of simultaneously segmenting the catheter from two angles while estimating the generated forces at its tip in 3D. This model has undergone extensive experiments on synthetic X-ray images with various noise levels and has been compared against state-of-the-art pure segmentation models, vision-based catheter force estimation methods, and a multitask catheter segmentation and force estimation approach. It outperforms existing models, setting a new state-of-the-art in both catheter segmentation and force estimation.

Acute aortic dissection (AD) is a life threatening condition that poses considerable challenges for timely diagnosis. Non-contrast computed tomography (CT) is frequently used to diagnose AD in certain clinical settings, but its diagnostic accuracy can vary among radiologists. This study aimed to develop and validate an interpretable YOLOv8 deep learning model based on non-contrast CT to detect AD. This retrospective study included patients from five institutions, divided into training, internal validation, and external validation cohorts. The YOLOv8 deep learning model was trained on annotated non-contrast CT images. Its performance was evaluated using area under the curve (AUC), sensitivity, specificity, and inference time compared with findings from vascular interventional radiologists, general radiologists, and radiology residents. In addition, gradient weighted class activation mapping (Grad-CAM) saliency map analysis was performed. A total of 1 138 CT scans were assessed (569 with AD, 569 controls). The YOLOv8s model achieved an AUC of 0.964 (95% confidence interval [CI] 0.939 - 0.988) in the internal validation cohort and 0.970 (95% CI 0.946 - 0.990) in the external validation cohort. In the external validation cohort, the performance of the three groups of radiologists in detecting AD was inferior to that of the YOLOv8s model. The model's sensitivity (0.976) was slightly higher than that of vascular interventional specialists (0.965; p = .18), and its specificity (0.935) was superior to that of general radiologists (0.835; p < .001). The model's inference time was 3.47 seconds, statistically significantly shorter than the radiologists' mean interpretation time of 25.32 seconds (p < .001). Grad-CAM analysis confirmed that the model focused on anatomically and clinically relevant regions, supporting its interpretability. The YOLOv8s deep learning model reliably detected AD on non-contrast CT and outperformed radiologists, particularly in time efficiency and diagnostic accuracy. Its implementation could enhance AD screening in specific settings, support clinical decision making, and improve diagnostic quality.

Methods have been developed that apply image processing to 4-Dimension computed tomography (4DCT) to generate lung ventilation (4DCT-ventilation). Traditional methods for 4DCT-ventilation rely on density-change methods and lack reproducibility and do not provide 4DCT-perfusion data. Novel 4DCT-ventilation/perfusion methods have been developed that are robust and provide 4DCT-perfusion information. The purpose of this study was to use prospective clinical trial data to evaluate the ability of novel 4DCT-based lung function imaging methods to predict pneumonitis. Sixty-three advanced-stage lung cancer patients enrolled in a multi-institutional, phase 2 clinical trial on 4DCT-based functional avoidance radiation therapy were used. 4DCTs were used to generate four lung function images: 1) 4DCT-ventilation using the traditional HU approach ('4DCT-vent-HU'), and 3 methods using the novel statistically robust methods: 2) 4DCT-ventilation based on the Mass Conserving Volume Change ('4DCT-vent-MCVC'), 3) 4DCT-ventilation using the Integrated Jacobian Formulation ('4DCT-vent-IJF') and 4) 4DCT-perfusion. Dose-function metrics including mean functional lung dose (fMLD), and percentage of functional lung receiving ≥ 5 Gy (fV5), and ≥ 20 Gy (fV20) were calculated using various structure-based thresholds. The ability of dose-function metrics to predict for ≥ grade 2 RP was assessed using logistic regression and machine learning. Model performance was evaluated using the area under the curve (AUC) and validated through 10-fold cross-validation. 10/63 (15.9 %) patients developed grade ≥2 RP. Logistic regression yielded mean AUCs of 0.70 ± 0.02 (p = 0.04), 0.64 ± 0.04 (p = 0.13), 0.60 ± 0.03 (p = 0.27), and 0.63 ± 0.03 (p = 0.20) for 4DCT-vent-MCVC, 4DCT-perfusion, 4DCT-vent-IJF, and 4DCT-vent-HU, respectively, compared to 0.65 ± 0.10 (p > 0.05) for standard lung metrics. Machine learning modeling resulted in AUCs 0.83 ± 0.04, 0.82 ± 0.05, 0.76 ± 0.05, 0.74 ± 0.06, and 0.75 ± 0.02 for 4DCT-vent-MCVC, 4DCT-perfusion, 4DCT-vent-IJF, and 4DCT-vent-HU, and standard lung metrics respectively, with an accuracy of 75-85 %. This is the first study to comprehensively evaluate 4DCT-perfusion and robust 4DCT-ventilation in predicting clinical outcomes. The data showed that on the presented 63-patient study and using classis logistic regression and ML methods, 4DCT-vent-MCVC was the best predictors of RP.

Advances in intracoronary imaging have made it possible to distinguish different pathological mechanisms underlying acute coronary syndrome (ACS) in vivo. Accurate identification of these mechanisms is increasingly recognized as essential for enabling tailored therapeutic strategies. ACS pathogenesis is primarily classified into 2 major types: plaque rupture (PR) and plaque erosion (PE). Patients with PR are treated with intracoronary stenting, whereas those with PE may be potentially managed conservatively without stenting. The aim of this study is to develop neural networks capable of distinguishing PR from PE solely using coronary angiography (CAG). A total of 842 videos from 278 ACS patients (PR:172, PE:106) were included. To ensure the reliability of the ground truth for PR/PE classification, the ACS pathogenesis for each patient was confirmed using Optical Coherence Tomography (OCT). To enhance the learning of discriminative features across consecutive frames and improve PR/PE classification performance, we propose Sequence Contrastive Learning (SeqCon), which addresses the limitations inherent in conventional contrastive learning approaches. In the experiments, the external test set consisted of 18 PR patients (46 videos) and 11 PE patients (30 videos). SeqCon achieved an accuracy of 82.8%, sensitivity of 88.9%, specificity of 72.3%, positive predictive value of 84.2%, and negative predictive value of 80.0% at the patient-level. This is the first report to use contrastive learning for diagnosing the underlying mechanism of ACS by CAG. We demonstrated that it can be feasible to distinguish between PR and PE without intracoronary imaging modalities.

Brain nuclei are clusters of anatomically distinct neurons that serve as important hubs for processing and relaying information in various neural circuits. Fine-scale parcellation of the brain nuclei is vital for a comprehensive understanding of their anatomico-functional correlations. Diffusion MRI tractography is an advanced imaging technique that can estimate the brain's white matter structural connectivity to potentially reveal the topography of the nuclei of interest for studying their subdivisions. In this work, we present a deep clustering pipeline, namely DeepNuParc, to perform automated, fine-scale parcellation of brain nuclei using diffusion MRI tractography. First, we incorporate a newly proposed deep learning approach to enable accurate segmentation of the nuclei of interest directly on the dMRI data. Next, we design a novel streamline clustering-based structural connectivity feature for a robust representation of voxels within the nuclei. Finally, we improve the popular joint dimensionality reduction and k-means clustering approach to enable nuclei parcellation at a finer scale. We demonstrate DeepNuParc on two important brain structures, i.e. the amygdala and the thalamus, that are known to have multiple anatomically and functionally distinct nucleus subdivisions. Experimental results show that DeepNuParc enables consistent parcellation of the nuclei into multiple parcels across multiple subjects and achieves good correspondence with the widely used coarse-scale atlases. Our code is available at https://github.com/HarlandZZC/deep_nuclei_parcellation.

Rotator cuff muscle pathology affects outcomes following total shoulder arthroplasty, yet current assessment methods lack reliability in quantifying muscle atrophy and fat infiltration. We developed a deep learning-based model for automated segmentation of rotator cuff muscles on computed tomography (CT) and propose a T-score classification of volumetric muscle atrophy. We further characterized distinct atrophy phenotypes, 3D fat infiltration percentage (3DFI%), and anterior-posterior (AP) balance, which were compared between healthy controls, anatomic total shoulder arthroplasty (aTSA), and reverse total shoulder arthroplasty (rTSA) patients. 952 shoulder CT scans were included (762 controls, 103 undergoing aTSA for glenohumeral osteoarthritis, and 87 undergoing rTSA for cuff tear arthropathy. A deep learning model was developed to allow automated segmentation of supraspinatus (SS), subscapularis (SC), infraspinatus (IS) and teres minor (TM). Muscle volumes were normalized to scapula volume, and control muscle volumes were referenced to calculate T-scores for each muscle. T-scores were classified as no atrophy (>-1.0), moderate atrophy (-1 to -2.5), and severe atrophy (<-2.5). 3DFI% was quantified as the proportion of fat within each muscle using Hounsfield unit thresholds. The T-scores, 3DFI%, and AP balance were compared between the three cohorts. The aTSA cohort had significantly greater atrophy in all muscles compared to control (p<0.001), whereas the rTSA cohort had significantly greater atrophy in SS, SC, and IS than aTSA (p<0.001). In the aTSA cohort, the most common phenotype was SS_severe/SC_moderate/IS+TM_moderate, while in the rTSA cohort it was SS_severe/SC_moderate/IS+TM_severe. The aTSA group had significantly higher 3DFI% compared to controls for all muscles (p<0.001), while the rTSA cohort had significantly higher 3DFI% than aTSA and control cohorts for all muscles (p<0.001). Additionally, the aTSA cohort had a significantly lower AP muscle volume ratio (1.06 vs. 1.14, p<0.001), whereas the rTSA group had a significantly higher AP muscle volume ratio than the control cohort (1.31 vs. 1.14, p<0.001). Our study demonstrates successful development of a deep learning model for automated volumetric assessment of rotator cuff muscle atrophy, 3DFI% and AP balance on shoulder CT scans. We found that aTSA patients had significantly greater muscle atrophy and 3DFI% than controls, while the rTSA patients had the most severe muscle atrophy and 3DFI%. Additionally, distinct phenotypes of muscle atrophy and AP muscle balance exist in aTSA and rTSA that warrant further investigation with regards to shoulder arthroplasty outcomes.

High-Intensity Focused Ultrasound (HIFU) is a non-invasive therapeutic technique widely used for treating various diseases. However, the success and safety of HIFU treatments depend on real-time monitoring, which is often hindered by interference when using ultrasound to guide HIFU treatment. To address these challenges, we developed HIFU-ILDiff, a novel deep learning-based approach leveraging latent diffusion models to suppress HIFU-induced interference in ultrasound images. The HIFU-ILDiff model employs a Vector Quantized Variational Autoencoder (VQ-VAE) to encode noisy ultrasound images into a lower-dimensional latent space, followed by a latent diffusion model that iteratively removes interference. The denoised latent vectors are then decoded to reconstruct high-resolution, interference-free ultrasound images. We constructed a comprehensive dataset comprising 18,872 image pairs from in vitro phantoms, ex vivo tissues, and in vivo animal data across multiple imaging modalities and HIFU power levels to train and evaluate the model. Experimental results demonstrate that HIFU-ILDiff significantly outperforms the commonly used Notch Filter method, achieving a Structural Similarity Index (SSIM) of 0.796 and Peak Signal-to-Noise Ratio (PSNR) of 23.780 compared to SSIM of 0.443 and PSNR of 14.420 for the Notch Filter under in vitro scenarios. Additionally, HIFU-ILDiff achieves real-time processing at 15 frames per second, markedly faster than the Notch Filter's 5 seconds per frame. These findings indicate that HIFU-ILDiff is able to denoise HIFU interference in ultrasound guiding images for real-time monitoring during HIFU therapy, which will greatly improve the treatment precision in current clinical applications.

The nnUNet segmentation framework adeptly adjusts most hyperparameters in training scripts automatically, but it overlooks the tuning of internal hyperparameters within the segmentation network itself, which constrains the model's ability to generalize. Addressing this limitation, this study presents a novel Self-Adaptive Convolution Module that dynamically adjusts the size of the convolution kernels depending on the unique fingerprints of different datasets. This adjustment enables the MSA2-Net, when equipped with this module, to proficiently capture both global and local features within the feature maps. Self-Adaptive Convolution Module is strategically integrated into two key components of the MSA2-Net: the Multi-Scale Convolution Bridge and the Multi-Scale Amalgamation Decoder. In the MSConvBridge, the module enhances the ability to refine outputs from various stages of the CSWin Transformer during the skip connections, effectively eliminating redundant data that could potentially impair the decoder's performance. Simultaneously, the MSADecoder, utilizing the module, excels in capturing detailed information of organs varying in size during the decoding phase. This capability ensures that the decoder's output closely reproduces the intricate details within the feature maps, thus yielding highly accurate segmentation images. MSA2-Net, bolstered by this advanced architecture, has demonstrated exceptional performance, achieving Dice coefficient scores of 86.49\%, 92.56\%, 93.37\%, and 92.98\% on the Synapse, ACDC, Kvasir, and Skin Lesion Segmentation (ISIC2017) datasets, respectively. This underscores MSA2-Net's robustness and precision in medical image segmentation tasks across various datasets.

Ambiguity in medical image segmentation calls for models that capture full conditional distributions rather than a single point estimate. We present Prior-Guided Residual Diffusion (PGRD), a diffusion-based framework that learns voxel-wise distributions while maintaining strong calibration and practical sampling efficiency. PGRD embeds discrete labels as one-hot targets in a continuous space to align segmentation with diffusion modeling. A coarse prior predictor provides step-wise guidance; the diffusion network then learns the residual to the prior, accelerating convergence and improving calibration. A deep diffusion supervision scheme further stabilizes training by supervising intermediate time steps. Evaluated on representative MRI and CT datasets, PGRD achieves higher Dice scores and lower NLL/ECE values than Bayesian, ensemble, Probabilistic U-Net, and vanilla diffusion baselines, while requiring fewer sampling steps to reach strong performance.