Three-dimensional (3D) ultrasound (US) reconstruction is of significant value in clinical diagnosis, characterized by its safety, portability, low cost, and high real-time capabilities. 3D freehand ultrasound reconstruction aims to eliminate the need for tracking devices, relying solely on image data to infer the spatial relationships between frames. However, inherent jitter during handheld scanning introduces significant inaccuracies, making current methods ineffective in precisely predicting the spatial motions of ultrasound image frames. This leads to substantial cumulative errors over long sequence modeling, resulting in deformations or artifacts in the reconstructed volume. To address these challenges, we proposed UltrasOM, a 3D ultrasound reconstruction network designed for spatial relative motion estimation. Initially, we designed a video embedding module that integrates optical flow dynamics with original static information to enhance motion change features between frames. Next, we developed a Mamba-based spatiotemporal attention module, utilizing multi-layer stacked Space-Time Blocks to effectively capture global spatiotemporal correlations within video frame sequences. Finally, we incorporated correlation loss and motion speed loss to prevent overfitting related to scanning speed and pose, enhancing the model's generalization capability. Experimental results on a dataset of 200 forearm cases, comprising 58,011 frames, demonstrated that the proposed method achieved a final drift rate (FDR) of 10.24 %, a frame-to-frame distance error (DE) of 7.34 mm, a symmetric Hausdorff distance error (HD) of 10.81 mm, and a mean angular error (MEA) of 2.05°, outperforming state-of-the-art methods by 13.24 %, 15.11 %, 3.57 %, and 6.32 %, respectively. By integrating optical flow features and deeply exploring contextual spatiotemporal dependencies, the proposed network can directly predict the relative motions between multiple frames of ultrasound images without the need for tracking, surpassing the accuracy of existing methods.

To evaluate the predictive ability of AI HIP in determining the size and position of prostheses during complex total hip arthroplasty (THA). Additionally, it investigates the factors influencing the accuracy of preoperative planning predictions. From April 2021 to December 2023, patients with complex hip joint diseases were divided into the AI preoperative planning group (n = 29) and the X-ray preoperative planning group (n = 27). Postoperative X-rays were used to measure acetabular anteversion angle, abduction angle, tip-to-sternum distance, intraoperative duration, blood loss, planning time, postoperative Harris Hip Scores (at 2 weeks, 3 months, and 6 months), and visual analogue scale (VAS) pain scores (at 2 weeks and at final follow-up) to analyze clinical outcomes. On the acetabular side, the accuracy of AI preoperative planning was higher compared to X-ray preoperative planning (75.9% vs. 44.4%, P = 0.016). On the femoral side, AI preoperative planning also showed higher accuracy compared to X-ray preoperative planning (85.2% vs. 59.3%, P = 0.033). The AI preoperative planning group showed superior outcomes in terms of reducing bilateral leg length discrepancy (LLD), decreasing operative time and intraoperative blood loss, early postoperative recovery, and pain control compared to the X-ray preoperative planning group (P < 0.05). No significant differences were observed between the groups regarding bilateral femoral offset (FO) differences, bilateral combined offset (CO) differences, abduction angle, anteversion angle, or tip-to-sternum distance. Factors such as gender, age, affected side, comorbidities, body mass index (BMI) classification, bone mineral density did not affect the prediction accuracy of AI HIP preoperative planning. Artificial intelligence-based 3D planning can be effectively utilized for preoperative planning in complex THA. Compared to X-ray templating, AI demonstrates superior accuracy in prosthesis measurement and provides significant clinical benefits, particularly in early postoperative recovery.

Adaptive optics scanning light ophthalmoscopy (AOSLO) reveals individual retinal cells and their function, microvasculature, and micropathologies in vivo. As compared to the single-channel offset pinhole and two-channel split-detector nonconfocal AOSLO designs, by providing multidirectional imaging capabilities, a recent generation of multidetector and (multi-)offset aperture AOSLO modalities has been demonstrated to provide critical information about retinal microstructures. However, increasing detection channels requires expensive optical components and/or critically increases imaging time. To address this issue, we present an innovative combination of machine learning and optics as an integrated technology to compressively capture 12 nonconfocal channel AOSLO images simultaneously. Imaging of healthy participants and diseased subjects using the proposed deep compressed multichannel AOSLO showed enhanced visualization of rods, cones, and mural cells with over an order-of-magnitude improvement in imaging speed as compared to conventional offset aperture imaging. To facilitate the adaptation and integration with other in vivo microscopy systems, we made optical design, acquisition, and computational reconstruction codes open source.

Breast cancer is the most frequently diagnosed human cancer in the United States at present. Early detection is crucial for its successful treatment. X-ray mammography and digital breast tomosynthesis are currently the main methods for breast cancer screening. However, both have known limitations in terms of their sensitivity and specificity to breast cancers, while also frequently causing patient discomfort due to the requirement for breast compression. Breast computed tomography is a promising alternative, however, to obtain high-quality images, the X-ray dose needs to be sufficiently high. As the breast is highly radiosensitive, dose reduction is particularly important. Phase-contrast computed tomography (PCT) has been shown to produce higher-quality images at lower doses and has no need for breast compression. It is demonstrated in the present study that, when imaging full fresh mastectomy samples with PCT, deep learning-based image denoising can further reduce the radiation dose by a factor of 16 or more, without any loss of image quality. The image quality has been assessed both in terms of objective metrics, such as spatial resolution and contrast-to-noise ratio, as well as in an observer study by experienced medical imaging specialists and radiologists. This work was carried out in preparation for live patient PCT breast cancer imaging, initially at specialized synchrotron facilities.

Purpose: Deep learning has demonstrated strong potential for MRI reconstruction, but conventional supervised learning methods require high-quality reference images, which are often unavailable in practice. Self-supervised learning offers an alternative, yet its performance degrades at high acceleration rates. To overcome these limitations, we propose hybrid learning, a novel two-stage training framework that combines self-supervised and supervised learning for robust image reconstruction. Methods: Hybrid learning is implemented in two sequential stages. In the first stage, self-supervised learning is employed to generate improved images from noisy or undersampled reference data. These enhanced images then serve as pseudo-ground truths for the second stage, which uses supervised learning to refine reconstruction performance and support higher acceleration rates. We evaluated hybrid learning in two representative applications: (1) accelerated 0.55T spiral-UTE lung MRI using noisy reference data, and (2) 3D T1 mapping of the brain without access to fully sampled ground truth. Results: For spiral-UTE lung MRI, hybrid learning consistently improved image quality over both self-supervised and conventional supervised methods across different acceleration rates, as measured by SSIM and NMSE. For 3D T1 mapping, hybrid learning achieved superior T1 quantification accuracy across a wide dynamic range, outperforming self-supervised learning in all tested conditions. Conclusions: Hybrid learning provides a practical and effective solution for training deep MRI reconstruction networks when only low-quality or incomplete reference data are available. It enables improved image quality and accurate quantitative mapping across different applications and field strengths, representing a promising technique toward broader clinical deployment of deep learning-based MRI.

Cardiac perfusion PET is commonly used to assess ischemia and cardiovascular risk, which enables quantitative measurements of myocardial blood flow (MBF) through kinetic modeling. However, the estimation of kinetic parameters is challenging due to the noisy nature of short dynamic frames and limited sample data points. This work aimed to investigate the errors in MBF estimation in PET through a simulation study and to evaluate different parameter estimation approaches, including a deep learning (DL) method. Simulated studies were generated using digital phantoms based on cardiac segmentations from 55 clinical CT images. We employed the irreversible 2-tissue compartmental model and simulated dynamic ¹³N-ammonia PET scans under both rest and stress conditions (220 cases each). The simulations covered a rest K₁ range of 0.6 to 1.2 and a stress K₁ range of 1.2 to 3.6 (unit: mL/min/g) in the myocardium. A transformer-based DL model was trained on the simulated dataset to predict parametric images (PIMs) from noisy PET image frames and was validated using 5-fold cross-validation. We compared the DL method with the voxel-wise nonlinear least squares (NLS) fitting applied to the dynamic images, using either Gaussian filter (GF) smoothing (GF-NLS) or a dynamic nonlocal means (DNLM) algorithm for denoising (DNLM-NLS). Two patients with coronary CT angiography (CTA) and fractional flow reserve (FFR) were enrolled to test the feasibility of applying DL models on clinical PET data. The DL method showed clearer image structures with reduced noise compared to the traditional NLS-based methods. In terms of mean absolute relative error (MARE), as the rest K₁ values increased from 0.6 to 1.2 mL/min/g, the overall bias in myocardium K₁ estimates decreased from approximately 58% to 45% for the NLS-based methods while the DL method showed a reduction in MARE from 42% to 18%. For stress data, as the stress K₁ decreased from 3.6 to 1.2 mL/min/g, the MARE increased from 30% to 70% for the GF-NLS method. In contrast, both the DNLM-NLS (average: 42%) and the DL methods (average: 20%) demonstrated significantly smaller MARE changes as stress K₁ varied. Regarding the regional mean bias (±standard deviation), the GF-NLS method had a bias of 6.30% (±8.35%) of rest K₁, compared to 1.10% (±8.21%) for DNLM-NLS and 6.28% (±14.05%) for the DL method. For the stress K₁, the GF-NLS showed a mean bias of 10.72% (±9.34%) compared to 1.69% (±8.82%) for DNLM-NLS and -10.55% (±9.81%) for the DL method. This study showed that an increase in the tracer uptake rate (K₁) corresponded to improved accuracy and precision in MBF quantification, whereas lower tracer uptake resulted in higher noise in dynamic PET and poorer parameter estimates. Utilizing denoising techniques or DL approaches can mitigate noise-induced bias in PET parametric imaging.

This study presents an unsupervised, motion-resolved reconstruction framework for high-resolution, free-breathing pulmonary magnetic resonance imaging (MRI), utilizing a three-dimensional Gaussian representation (3DGS). The proposed method leverages 3DGS to address the challenges of motion-resolved 3D isotropic pulmonary MRI reconstruction by enabling data smoothing between voxels for continuous spatial representation. Pulmonary MRI data acquisition is performed using a golden-angle radial sampling trajectory, with respiratory motion signals extracted from the center of k-space in each radial spoke. Based on the estimated motion signal, the k-space data is sorted into multiple respiratory phases. A 3DGS framework is then applied to reconstruct a reference image volume from the first motion state. Subsequently, a patient-specific convolutional neural network is trained to estimate the deformation vector fields (DVFs), which are used to generate the remaining motion states through spatial transformation of the reference volume. The proposed reconstruction pipeline is evaluated on six datasets from six subjects and bench-marked against three state-of-the-art reconstruction methods. The experimental findings demonstrate that the proposed reconstruction framework effectively reconstructs high-resolution, motion-resolved pulmonary MR images. Compared with existing approaches, it achieves superior image quality, reflected by higher signal-to-noise ratio and contrast-to-noise ratio. The proposed unsupervised 3DGS-based reconstruction method enables accurate motion-resolved pulmonary MRI with isotropic spatial resolution. Its superior performance in image quality metrics over state-of-the-art methods highlights its potential as a robust solution for clinical pulmonary MR imaging.

Efficient evaluation of soft tissues and bony structures following cervical spine trauma is critical. We sought to evaluate the diagnostic validity of magnetic resonance imaging (MRI)-based synthetic CT (sCT) compared with conventional computed tomography (CT) for cervical spine injuries. In a prospective, multicenter study, patients with cervical spine injuries underwent CT and MRI within 48 h after injury. A panel of five clinicians independently reviewed the images for diagnostic accuracy, lesion characterization (AO Spine classification), and soft tissue trauma. Fracture visibility, anterior (AVH) and posterior wall height (PVH), vertebral body angle (VBA), segmental kyphosis (SK), with corresponding interobserver reliability (intraclass correlation coefficients (ICC)) and intermodal differences (Fleiss' Kappa), were recorded. The accuracy of estimating Hounsfield unit (HU) values and mean cortical surface distances were measured. Thirty-seven patients (44 cervical spine fractures) were enrolled. sCT demonstrated a sensitivity of 97.3% for visualizing fractures. Intermodal agreement regarding injury classification indicated almost perfect agreement (κ = 0.922; p < 0.001). Inter-reader ICCs were good to excellent (CT vs. sCT): AVH (0.88, 0.87); PVH (0.87, 0.88); VBA (0.78, 0.76); SK (0.77, 0.93). Intermodal agreement showed a mean absolute difference of 0.3 mm (AVH), 0.3 mm (PVH), 1.15° (VBA) and 0.51° (SK), respectively. MRI visualized additional soft tissue trauma in 56.8% of patients. Voxelwise comparisons of sCT showed good to excellent agreement with CT in terms of HUs (mean absolute error of 20 (SD ± 62)) and a mean absolute cortical surface distance of 0.45 mm (SD ± 0.13). sCT is a promising, radiation-free imaging technique for diagnosing cervical spine injuries with similar accuracy to CT. Question Assessing the accuracy of MRI-based synthetic CT (sCT) for fracture visualization and classification in comparison to the gold standard of CT for cervical spine injuries. Findings sCT demonstrated a 97.3% sensitivity in detecting fractures and exhibited near-perfect intermodal agreement in classifying injuries according to the AO Spine classification system. Clinical relevance sCT is a promising, radiation-free imaging modality that offers comparable accuracy to CT in visualizing and classifying cervical spine injuries. The combination of conventional MRI sequences for soft tissue evaluation with sCT reconstruction for bone visualization provides comprehensive diagnostic information.

Artificial intelligence (AI) is now transforming medical imaging, with extensive ramifications for nearly every aspect of diagnostic imaging, including computed tomography (CT). This current work aims to review, evaluate, and summarise the role of AI in radiation dose optimisation across three fundamental domains in CT: patient positioning, scan range determination, and image reconstruction. A comprehensive scoping review of the literature was performed. Electronic databases including Scopus, Ovid, EBSCOhost and PubMed were searched between January 2018 and December 2024. Relevant articles were identified from their titles had their abstracts evaluated, and those deemed relevant had their full text reviewed. Extracted data from selected studies included the application of AI, radiation dose, anatomical part, and any relevant evaluation metrics based on the CT parameter in which AI is applied. 90 articles met the selection criteria. Included studies evaluated the performance of AI for dose optimisation through patient positioning, scan range determination, and reconstruction across various CT scans, including the abdomen, chest, head, neck, and pelvis, as well as CT angiography. A concise overview of the present state of AI in these three domains, emphasising benefits, limitations, and impact on the transformation of dose reduction in CT scanning, is provided. AI methods can help minimise positioning offsets and over-scanning caused by manual errors and helped to overcome the limitation associated with low-dose CT settings through deep learning image reconstruction algorithms. Further clinical integration of AI will continue to allow for improvements in optimising CT scan protocols and radiation dose. This review underscores the significance of AI in optimizing radiation doses in CT imaging, focusing on three key areas: patient positioning, scan range determination, and image reconstruction.

In compressed sensing (CS) MRI, model-based methods are pivotal to achieving accurate reconstruction. One of the main challenges in model-based methods is finding an effective prior to describe the statistical distribution of the target image. Plug-and-Play (PnP) and REgularization by Denoising (RED) are two general frameworks that use denoisers as the prior. While PnP/RED methods with convolutional neural networks (CNNs) based denoisers outperform classical hand-crafted priors in CS MRI, their convergence theory relies on assumptions that do not hold for practical CNNs. The recently developed gradient-driven denoisers offer a framework that bridges the gap between practical performance and theoretical guarantees. However, the numerical solvers for the associated minimization problem remain slow for CS MRI reconstruction. This paper proposes a complex quasi-Newton proximal method that achieves faster convergence than existing approaches. To address the complex domain in CS MRI, we propose a modified Hessian estimation method that guarantees Hermitian positive definiteness. Furthermore, we provide a rigorous convergence analysis of the proposed method for nonconvex settings. Numerical experiments on both Cartesian and non-Cartesian sampling trajectories demonstrate the effectiveness and efficiency of our approach.