High-resolution computed tomography (CT) imaging is essential for medical diagnosis but requires increased radiation exposure, creating a critical trade-off between image quality and patient safety. While deep learning methods have shown promise in CT super-resolution, they face challenges with complex degradations and limited medical training data. Meanwhile, large-scale pre-trained diffusion models, particularly Stable Diffusion, have demonstrated remarkable capabilities in synthesizing fine details across various vision tasks. Motivated by this, we propose a novel framework that adapts Stable Diffusion for CT blind super-resolution. We employ a practical degradation model to synthesize realistic low-quality images and leverage a pre-trained vision-language model to generate corresponding descriptions. Subsequently, we perform super-resolution using Stable Diffusion with a specialized controlling strategy, conditioned on both low-resolution inputs and the generated text descriptions. Extensive experiments show that our method outperforms existing approaches, demonstrating its potential for achieving high-quality CT imaging at reduced radiation doses. Our code will be made publicly available.

Three-dimensional visualization is increasingly used in dentistry for diagnostics, education, and treatment design. The accurate replication of geometry and color is crucial for these applications. Image-based rendering, which uses 2-dimensional photos to generate photo-realistic 3-dimensional representations, provides an affordable and practical option, aiding both regular and remote health care. This study explores an advanced novel view synthesis (NVS) method called Gaussian splatting (GS), a differentiable image-based rendering approach, to assess its feasibility for dental scene capturing. The rendering quality and resource usage were compared with representative NVS methods. In addition, the linear measurement trueness of extracted craniofacial meshes was evaluated against a commercial facial scanner and 3 smartphone facial scanning apps, while teeth meshes were assessed against 2 intraoral scanners and a desktop scanner. GS-based representation demonstrated superior rendering quality, achieving the highest visual quality, fastest rendering speed, and lowest resource usage. The craniofacial measurements showed similar trueness to commercial facial scanners. The dental measurements had larger deviations than intraoral and desktop scanners did, although all deviations remained within clinically acceptable limits. The GS-based representation shows great potential for developing a convenient and cost-effective method of capturing dental scenes, offering a balance between color fidelity and trueness suitable for clinical applications.

This study introduces a motion-based learning network with a global-local self-attention module (MoGLo-Net) to enhance 3D reconstruction in handheld photoacoustic and ultrasound (PAUS) imaging. Standard PAUS imaging is often limited by a narrow field of view (FoV) and the inability to effectively visualize complex 3D structures. The 3D freehand technique, which aligns sequential 2D images for 3D reconstruction, faces significant challenges in accurate motion estimation without relying on external positional sensors. MoGLo-Net addresses these limitations through an innovative adaptation of the self-attention mechanism, which effectively exploits the critical regions, such as fully-developed speckle areas or high-echogenic tissue regions within successive ultrasound images to accurately estimate the motion parameters. This facilitates the extraction of intricate features from individual frames. Additionally, we employ a patch-wise correlation operation to generate a correlation volume that is highly correlated with the scanning motion. A custom loss function was also developed to ensure robust learning with minimized bias, leveraging the characteristics of the motion parameters. Experimental evaluations demonstrated that MoGLo-Net surpasses current state-of-the-art methods in both quantitative and qualitative performance metrics. Furthermore, we expanded the application of 3D reconstruction technology beyond simple B-mode ultrasound volumes to incorporate Doppler ultrasound and photoacoustic imaging, enabling 3D visualization of vasculature. The source code for this study is publicly available at: https://github.com/pnu-amilab/US3D.

Dynamic Magnetic Resonance Imaging (MRI) exhibits transformation symmetries, including spatial rotation symmetry within individual frames and temporal symmetry along the time dimension. Explicit incorporation of these symmetry priors in the reconstruction model can significantly improve image quality, especially under aggressive undersampling scenarios. Recently, Equivariant convolutional neural network (ECNN) has shown great promise in exploiting spatial symmetry priors. However, existing ECNNs critically fail to model temporal symmetry, arguably the most universal and informative structural prior in dynamic MRI reconstruction. To tackle this issue, we propose a novel Deep Unrolling Network with Spatiotemporal Rotation Equivariance (DUN-SRE) for Dynamic MRI Reconstruction. The DUN-SRE establishes spatiotemporal equivariance through a (2+1)D equivariant convolutional architecture. In particular, it integrates both the data consistency and proximal mapping module into a unified deep unrolling framework. This architecture ensures rigorous propagation of spatiotemporal rotation symmetry constraints throughout the reconstruction process, enabling more physically accurate modeling of cardiac motion dynamics in cine MRI. In addition, a high-fidelity group filter parameterization mechanism is developed to maintain representation precision while enforcing symmetry constraints. Comprehensive experiments on Cardiac CINE MRI datasets demonstrate that DUN-SRE achieves state-of-the-art performance, particularly in preserving rotation-symmetric structures, offering strong generalization capability to a broad range of dynamic MRI reconstruction tasks.

To investigate the feasibility of accelerated MRI with artificial intelligence-assisted compressed sensing (ACS) technique in the temporomandibular joint (TMJ) and compare its performance with parallel imaging (PI) protocol and standard (STD) protocol. Participants with TMJ-related symptoms were prospectively enrolled from April 2023 to May 2024, and underwent bilateral TMJ imaging examinations using ACS protocol (6:08 min), PI protocol (10:57 min), and STD protocol (13:28 min). Overall image quality and visibility of TMJ relevant structures were qualitatively evaluated by a 4-point Likert scale. Quantitative analysis of signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of TMJ disc, condyle, and lateral pterygoid muscle (LPM) was performed. Diagnostic agreement of joint effusion and disc displacement among protocols and investigators was assessed by Fleiss' kappa analysis. A total of 51 participants (16 male and 35 female) with 102 TMJs were included. The overall image quality and most structures of the ACS protocol were significantly higher than the STD protocol (all p < 0.05), and similar to the PI protocol. For quantitative analysis, the ACS protocol demonstrated significantly higher SNR and CNR than the STD protocol in the TMJ disc, condyle, and LPM (all p < 0.05), and the ACS protocol showed comparable SNR to the PI protocol in most sequences. Good to excellent inter-protocol and inter-observer agreement was observed for diagnosing TMJ abnormalities (κ = 0.699-1.000). Accelerated MRI with ACS technique can significantly reduce the acquisition time of TMJ, while providing superior or equivalent image quality and great diagnostic agreement with PI and STD protocols. Question Patients with TMJ disorders often cannot endure long MRI examinations due to orofacial pain, necessitating accelerated MRI to improve patient comfort. Findings ACS technique can significantly reduce acquisition time in TMJ imaging while providing superior or equivalent image quality. Clinical relevance The time-saving ACS technique improves image quality and achieves excellent diagnostic agreement in the evaluation of joint effusion and disc displacement. It helps optimize clinical MRI workflow in patients with TMJ disorders.

Prolonged imaging times and motion sensitivity at 7T necessitate advancements in image acceleration techniques. This study evaluates a 7T deep learning (DL)-based image reconstruction by using a deep neural network trained on 7T data, applied to T2-weighted turbo spin-echo imaging. Raw <i>k</i>-space data from 30 consecutive clinical 7T brain MRI patients was reconstructed by using both DL and standard methods. Qualitative assessments included overall image quality, artifacts, sharpness, structural conspicuity, and noise level, while quantitative metrics evaluated contrast-to-noise ratio (CNR) and image noise. DL-based reconstruction consistently outperformed standard methods across all qualitative metrics (<i>P</i> < .001), with a mean CNR increase of 50.8% (95% CI: 43.0%-58.6%) and a mean noise reduction of 35.1% (95% CI: 32.7%-37.6%). These findings demonstrate that DL-based reconstruction at 7T significantly enhances image quality without introducing adverse effects, offering a promising tool for addressing the challenges of ultra-high-field MRI.

Computed tomography (CT) systems are indispensable for diagnostics but pose risks due to radiation exposure. Low-dose CT (LDCT) mitigates these risks but introduces noise and artifacts that compromise diagnostic accuracy. While deep learning methods, such as convolutional neural networks (CNNs) and generative adversarial networks (GANs), have been applied to LDCT denoising, challenges persist, including difficulties in preserving fine details and risks of model collapse. Recently, the Denoising Diffusion Probabilistic Model (DDPM) has addressed the limitations of traditional methods and demonstrated exceptional performance across various tasks. Despite these advancements, its high computational cost during training and extended sampling time significantly hinder practical clinical applications. Additionally, DDPM's reliance on random Gaussian noise can reduce optimization efficiency and performance in task-specific applications. To overcome these challenges, this study proposes a novel LDCT denoising framework that integrates the Latent Diffusion Model (LDM) with the Cold Diffusion Process. LDM reduces computational costs by conducting the diffusion process in a low-dimensional latent space while preserving critical image features. The Cold Diffusion Process replaces Gaussian noise with a CT denoising task-specific degradation approach, enabling efficient denoising with fewer time steps. Experimental results demonstrate that the proposed method outperforms DDPM in key metrics, including PSNR, SSIM, and RMSE, while achieving up to 2 × faster training and 14 × faster sampling. These advancements highlight the proposed framework's potential as an effective and practical solution for real-world clinical applications.

Three-dimensional ultrasound (US) aims to provide sonographers with the spatial relationships of anatomical structures, playing a crucial role in clinical diagnosis. Recently, deep-learning-based freehand 3-D US has made significant advancements. It reconstructs volumes by estimating transformations between images without external tracking. However, image-only reconstruction poses difficulties in reducing cumulative drift and further improving reconstruction accuracy, particularly in scenarios involving complex motion trajectories. In this context, we propose an enhanced motion network (MoNetV2) to enhance the accuracy and generalizability of reconstruction under diverse scanning velocities and tactics. First, we propose a sensor-based temporal and multibranch structure (TMS) that fuses image and motion information from a velocity perspective to improve image-only reconstruction accuracy. Second, we devise an online multilevel consistency constraint (MCC) that exploits the inherent consistency of scans to handle various scanning velocities and tactics. This constraint exploits scan-level velocity consistency (SVC), path-level appearance consistency (PAC), and patch-level motion consistency (PMC) to supervise interframe transformation estimation. Third, we distill an online multimodal self-supervised strategy (MSS) that leverages the correlation between network estimation and motion information to further reduce cumulative errors. Extensive experiments clearly demonstrate that MoNetV2 surpasses existing methods in both reconstruction quality and generalizability performance across three large datasets.

Speed-of-sound (SoS) is a biomechanical characteristic of tissue, and its imaging can provide a promising biomarker for diagnosis. Reconstructing SoS images from ultrasound acquisitions can be cast as a limited-angle computed-tomography problem, with variational networks being a promising model-based deep learning solution. Some acquired data frames may, however, get corrupted by noise due to, e.g., motion, lack of contact, and acoustic shadows, which in turn negatively affects the resulting SoS reconstructions. We propose to use the uncertainty in SoS reconstructions to attribute trust to each individual acquired frame. Given multiple acquisitions, we then use an uncertainty-based automatic selection among these retrospectively, to improve diagnostic decisions. We investigate uncertainty estimation based on Monte Carlo Dropout and Bayesian Variational Inference. We assess our automatic frame selection method for differential diagnosis of breast cancer, distinguishing between benign fibroadenoma and malignant carcinoma. We evaluate 21 lesions classified as BI-RADS 4, which represents suspicious cases for probable malignancy. The most trustworthy frame among four acquisitions of each lesion was identified using uncertainty-based criteria. Selecting a frame informed by uncertainty achieved an area under curve of 76% and 80% for Monte Carlo Dropout and Bayesian Variational Inference, respectively, superior to any uncertainty-uninformed baselines with the best one achieving 64%. A novel use of uncertainty estimation is proposed for selecting one of multiple data acquisitions for further processing and decision making.

High-resolution sinogram inpainting is essential for computed tomography reconstruction, as missing high-frequency projections can lead to visible artifacts and diagnostic errors. Diffusion models are well-suited for this task due to their robustness and detail-preserving capabilities, but their application to high-resolution inputs is limited by excessive memory and computational demands. To address this limitation, we propose HiSin, a novel diffusion based framework for efficient sinogram inpainting via resolution-guided progressive inference. It progressively extracts global structure at low resolution and defers high-resolution inference to small patches, enabling memory-efficient inpainting. It further incorporates frequency-aware patch skipping and structure-adaptive step allocation to reduce redundant computation. Experimental results show that HiSin reduces peak memory usage by up to 31.25% and inference time by up to 18.15%, and maintains inpainting accuracy across datasets, resolutions, and mask conditions.