This study aims to determine the diagnostic performance of sub-5-min combined sixfold parallel imaging (PIx3)-simultaneous multislice (SMSx2)-accelerated deep learning (DL) super-resolution 3T knee MRI in children and adolescents. Children with painful knee conditions who underwent PIx3-SMSx2-accelerated DL super-resolution 3T knee MRI and arthroscopy between October 2022 and December 2023 were retrospectively included. Nine fellowship-trained musculoskeletal radiologists independently scored the MRI studies for image quality and the presence of artifacts (Likert scales, range: 1 = very bad/severe, 5 = very good/absent), as well as structural abnormalities. Interreader agreements and diagnostic performance testing was performed. Forty-four children (mean age: 15 ± 2 years; range: 9-17 years; 24 boys) who underwent knee MRI and arthroscopic surgery within 22 days (range, 2-133) were evaluated. Overall image quality was very good (median rating: 5 [IQR: 4-5]). Motion artifacts (5 [5-5]) and image noise (5 [4-5]) were absent. Arthroscopy-verified abnormalities were detected with good or better interreader agreement (κ ≥ 0.74). Sensitivity, specificity, accuracy, and AUC values were 100%, 84%, 93%, and 0.92, respectively, for anterior cruciate ligament tears; 71%, 97%, 93%, and 0.84 for medial meniscus tears; 65%, 100%, 86%, and 0.82 for lateral meniscus tears; 100%, 100%, 100%, and 1.00 for discoid lateral menisci; 100%, 95%, 96%, and 0.98 for medial patellofemoral ligament tears; and 55%, 100%, 98%, and 0.77 for articular cartilage defects. Clinical sub-5-min PIx3-SMSx2-accelerated DL super-resolution 3T knee MRI provides excellent image quality and high diagnostic performance for diagnosing internal derangement in children and adolescents.

Chronic obstructive pulmonary disease (COPD) is one of the leading causes of morbidity and mortality in China, highlighting the importance of early diagnosis and ongoing monitoring for effective management. In recent years, hyperpolarized 129Xe MRI technology has gained significant clinical attention due to its ability to non-invasively and visually assess lung ventilation, microstructure, and gas exchange function. Its recent clinical approval in China, the United States and several European countries, represents a significant advancement in pulmonary imaging. This review provides an overview of the latest developments in hyperpolarized 129Xe MRI technology for COPD assessment in China. It covers the progress in instrument development, advanced imaging techniques, artificial intelligence-driven reconstruction methods, molecular imaging, and the application of this technology in both COPD patients and animal models. Furthermore, the review explores potential technical innovations in 129Xe MRI and discusses future directions for its clinical applications, aiming to address existing challenges and expand the technology's impact in clinical practice.

Magnetic resonance continues to evolve and advance as a critical imaging modality for disease diagnosis and monitoring. Hardware and software advances continue to propel this modality to the forefront of the field of diagnostic imaging. Next generation MR contrast media, specifically gadolinium chelates with improved relaxivity and stability (relative to the provided contrast effect), have emerged providing a further boost to the field. Concern regarding gadolinium deposition in the body with primarily the weaker gadolinium chelates (which have been now removed from the market, at least in Europe) continues to be at the forefront of clinicians' minds. This has driven renewed interest in possible development of manganese-based contrast media. The development of photon counting CT and its clinical introduction have made possible a further major advance in CT image quality, along with the potential for decreasing radiation dose. The possibility of major clinical advances in thoracic, cardiac, and musculoskeletal imaging were first recognized, with its broader impact - across all organ systems - now also recognized. The utility of routine acquisition (without penalty in time or radiation dose) of full spectral multi-energy data is now also being recognized as an additional major advance made possible by photon counting CT. Artificial intelligence is now being used in the background across most imaging platforms and modalities, making possible further advances in imaging technique and image quality, although this field is nowhere yet near to realizing its full potential. And last, but not least, the field of ultrasound is on the cusp of further major advances in availability (with development of very low-cost systems) and a possible new generation of microbubble contrast media.

To evaluate the impact of deep learning-based post-hoc noise reduction (DLNR) on image quality, coronary artery disease reporting and data system (CAD-RADS) assessment, and diagnostic performance in quarter-dose versus full-dose coronary CT angiography (CCTA) on external datasets. We retrospectively reviewed 221 patients who underwent retrospective electrocardiogram-gated CCTA in 2022-2023. Using dose modulation, either mid-diastole or end-systole was scanned at full dose depending on heart rates, and the other phase at quarter dose. Only patients with motion-free coronaries in both phases were included. Images were acquired using iterative reconstruction, and a residual dense network trained on external datasets denoised the quarter-dose images. Image quality was assessed by comparing noise levels using Tukey's test. Two radiologists independently assessed CAD-RADS, with agreement to full-dose images evaluated by Cohen's kappa. Diagnostic performance for significant stenosis referencing full-dose images was compared between quarter-dose and denoised images by the area under the receiver operating characteristic curve (AUC) using the DeLong test. Among 40 cases (age, 71 ± 7 years; 24 males), DLNR reduced noise from 37 to 18 HU (P < 0.001) in quarter-dose CCTA (full-dose images: 22 HU), and improved CAD-RADS agreement from moderate (0.60 [95 % CI: 0.41-0.78]) to excellent (0.82 [95 % CI: 0.66-0.94]). Denoised images demonstrated a superior AUC (0.97 [95 % CI: 0.95-1.00]) for diagnosing significant stenosis compared with original quarter-dose images (0.93 [95 % CI: 0.89-0.98]; P = 0.032). DLNR for quarter-dose CCTA significantly improved image quality, CAD-RADS agreement, and diagnostic performance for detecting significant stenosis referencing full-dose images.

To evaluate the diagnostic performance of lumbar spine CT using deep learning denoising (DLD CT) for detecting disc herniation and spinal stenosis. This retrospective study included 47 patients (229 intervertebral discs from L1/2 to L5/S1; 18 men and 29 women; mean age, 69.1 ± 10.9 years) who underwent lumbar spine CT and MRI within 1 month. CT images were reconstructed using filtered back projection (FBP) and denoised using a deep learning algorithm (ClariCT.AI). Three radiologists independently evaluated standard CT and DLD CT at an 8-week interval for the presence of disc herniation, central canal stenosis, and neural foraminal stenosis. Subjective image quality and diagnostic confidence were also assessed using five-point Likert scales. Standard CT and DLD CT were compared using MRI as a reference standard. DLD CT showed higher sensitivity (60% (70/117) vs. 44% (51/117); p < 0.001) and similar specificity (94% (534/570) vs. 94% (538/570); p = 0.465) for detecting disc herniation. Specificity for detecting spinal canal stenosis and neural foraminal stenosis was higher in DLD CT (90% (487/540) vs. 86% (466/540); p = 0.003, 94% (1202/1272) vs. 92% (1171/1272); p < 0.001), while sensitivity was comparable (81% (119/147) vs. 77% (113/147); p = 0.233, 83% (85/102) vs. 81% (83/102); p = 0.636). Image quality and diagnostic confidence were superior for DLD CT (all comparisons, p < 0.05). Compared to standard CT, DLD CT can improve diagnostic performance in detecting disc herniation and spinal stenosis with superior image quality and diagnostic confidence. Question The accurate diagnosis of disc herniation and spinal stenosis is limited on lumbar spine CT because of the low soft-tissue contrast. Findings Lumbar spine CT using deep learning denoising (DLD CT) demonstrated superior diagnostic performance in detecting disc herniation and spinal stenosis compared to standard CT. Clinical relevance DLD CT can be used as a simple and cost-effective screening test.

Diffusion-weighted imaging (DWI) is widely applied in tumor diagnosis by measuring the diffusion of water molecules. To increase the sensitivity to tumor identification, faithful high b-value DWI images are expected by setting a stronger strength of gradient field in magnetic resonance imaging (MRI). However, high b-value DWI images are heavily affected by reduced signal-to-noise ratio due to the exponential decay of signal intensity. Thus, removing noise becomes important for high b-value DWI images. Here, we propose a Physics-Informed neural Network for high b-value DWI images Denoising (PIND) by leveraging information from physics-informed loss and prior information from low b-value DWI images with high signal-to-noise ratio. Experiments are conducted on a prostate DWI dataset that has 125 subjects. Compared with the original noisy images, PIND improves the peak signal-to-noise ratio from 31.25 dB to 36.28 dB, and structural similarity index measure from 0.77 to 0.92. Our schemes can save 83% data acquisition time since fewer averages of high b-value DWI images need to be acquired, while maintaining 98% accuracy of the apparent diffusion coefficient value, suggesting its potential effectiveness in preserving essential diffusion characteristics. Reader study by 4 radiologists (3, 6, 13, and 18 years of experience) indicates PIND's promising performance on overall quality, signal-to-noise ratio, artifact suppression, and lesion conspicuity, showing potential for improving clinical DWI applications.

Sparse-view computed tomography (CT) is a practical solution to reduce radiation dose, but the resulting ill-posed inverse problem poses significant challenges for accurate image reconstruction. Although deep learning and diffusion-based methods have shown promising results, they often lack physical interpretability or suffer from high computational costs due to iterative sampling starting from random noise. Recent advances in generative modeling, particularly Poisson Flow Generative Models (PFGM), enable high-fidelity image synthesis by modeling the full data distribution. In this work, we propose Residual Poisson Flow (ResPF) Generative Models for efficient and accurate sparse-view CT reconstruction. Based on PFGM++, ResPF integrates conditional guidance from sparse measurements and employs a hijacking strategy to significantly reduce sampling cost by skipping redundant initial steps. However, skipping early stages can degrade reconstruction quality and introduce unrealistic structures. To address this, we embed a data-consistency into each iteration, ensuring fidelity to sparse-view measurements. Yet, PFGM sampling relies on a fixed ordinary differential equation (ODE) trajectory induced by electrostatic fields, which can be disrupted by step-wise data consistency, resulting in unstable or degraded reconstructions. Inspired by ResNet, we introduce a residual fusion module to linearly combine generative outputs with data-consistent reconstructions, effectively preserving trajectory continuity. To the best of our knowledge, this is the first application of Poisson flow models to sparse-view CT. Extensive experiments on synthetic and clinical datasets demonstrate that ResPF achieves superior reconstruction quality, faster inference, and stronger robustness compared to state-of-the-art iterative, learning-based, and diffusion models.

Correcting motion artifacts in MRI is important, as they can hinder accurate diagnosis. However, evaluating deep learning-based and classical motion correction methods remains fundamentally difficult due to the lack of accessible ground-truth target data. To address this challenge, we study three evaluation approaches: real-world evaluation based on reference scans, simulated motion, and reference-free evaluation, each with its merits and shortcomings. To enable evaluation with real-world motion artifacts, we release PMoC3D, a dataset consisting of unprocessed Paired Motion-Corrupted 3D brain MRI data. To advance evaluation quality, we introduce MoMRISim, a feature-space metric trained for evaluating motion reconstructions. We assess each evaluation approach and find real-world evaluation together with MoMRISim, while not perfect, to be most reliable. Evaluation based on simulated motion systematically exaggerates algorithm performance, and reference-free evaluation overrates oversmoothed deep learning outputs.

This study investigates the use of list-mode (LM) maximum a posteriori (MAP) expectation maximization (EM) incorporating prior information predicted by a convolutional neural network for image reconstruction in fast neutron (FN)-based proton therapy range verification.&#xD;Approach. A conditional generative adversarial network (pix2pix) was trained on progressively noisier data, where detector resolution effects were introduced gradually to simulate realistic conditions. FN data were generated using Monte Carlo simulations of an 85 MeV proton pencil beam in a computed tomography (CT)-based lung cancer patient model, with range shifts emulating weight gain and loss. The network was trained to estimate the expected two-dimensional (2D) ground truth FN production distribution from simple back-projection images. Performance was evaluated using mean squared error (MSE), structural similarity index (SSIM), and the correlation between shifts in predicted distributions and true range shifts. &#xD;Main results. Our results show that pix2pix performs well on noise-free data but suffers from significant degradation when detector resolution effects are introduced. Among the LM-MAP-EM approaches tested, incorporating a mean prior estimate into the reconstruction process improved performance, with LM-MAP-EM using a mean prior estimate outperforming naïve LM maximum likelihood EM (LM-MLEM) and conventional LM-MAP-EM with a smoothing quadratic energy function in terms of SSIM. &#xD;Significance. Findings suggest that deep learning techniques can enhance iterative reconstruction for range verification in proton therapy. However, the effectiveness of the model is highly dependent on data quality, limiting its robustness in high-noise scenarios.&#xD.

Self-supervised methods for magnetic resonance imaging (MRI) reconstruction have garnered significant interest due to their ability to address the challenges of slow data acquisition and scarcity of fully sampled labels. Current regularization-based self-supervised techniques merge the theoretical foundations of regularization with the representational strengths of deep learning and enable effective reconstruction under higher acceleration rates, yet often fall short in interpretability, leaving their theoretical underpinnings lacking. In this paper, we introduce a novel self-supervised approach that provides stringent theoretical guarantees and interpretable networks while circumventing the need for fully sampled labels. Our method exploits the intrinsic relationship between convolutional neural networks and the null space within structural low-rank models, effectively integrating network parameters into an iterative reconstruction process. Our network learns gradient descent steps of the projected gradient descent algorithm without changing its convergence property, which implements a fully interpretable unfolded model. We design a non-expansive mapping for the network architecture, ensuring convergence to a fixed point. This well-defined framework enables complete reconstruction of missing <math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mi>k</mi> <annotation>$k$</annotation></semantics> </math> -space data grounded in matrix completion theory, independent of fully sampled labels. Qualitative and quantitative experimental results on multi-coil MRI reconstruction demonstrate the efficacy of our self-supervised approach, showing marked improvements over existing self-supervised and traditional regularization methods, achieving results comparable to supervised learning in selected scenarios. Our method surpasses existing self-supervised approaches in reconstruction quality and also delivers competitive performance under supervised settings. This work not only advances the state-of-the-art in MRI reconstruction but also enhances interpretability in deep learning applications for medical imaging.