Reducing MRI scan times can improve patient care and lower healthcare costs. Many acceleration methods are designed to reconstruct diagnostic-quality images from sparse k-space data, via an ill-posed or ill-conditioned linear inverse problem (LIP). To address the resulting ambiguities, it is crucial to incorporate prior knowledge into the optimization problem, e.g., in the form of regularization. Another form of prior knowledge less commonly used in medical imaging is the readily available auxiliary data (a.k.a. side information) obtained from sources other than the current acquisition. In this paper, we present the Trust-Guided Variational Network (TGVN), an end-to-end deep learning framework that effectively and reliably integrates side information into LIPs. We demonstrate its effectiveness in multi-coil, multi-contrast MRI reconstruction, where incomplete or low-SNR measurements from one contrast are used as side information to reconstruct high-quality images of another contrast from heavily under-sampled data. TGVN is robust across different contrasts, anatomies, and field strengths. Compared to baselines utilizing side information, TGVN achieves superior image quality while preserving subtle pathological features even at challenging acceleration levels, drastically speeding up acquisition while minimizing hallucinations. Source code and dataset splits are available on github.com/sodicksonlab/TGVN.

The rapid acceleration of digital transformation and artificial intelligence (AI) is fundamentally reshaping medicine. Much like previous technological revolutions, AI-driven by advances in computer technology and software including machine learning, computer vision, and generative models-is redefining cognitive work in healthcare. Radiology, as one of the first fully digitized medical specialties, is at the forefront of this transformation. AI is automating workflows, enhancing image acquisition and interpretation, and improving diagnostic precision, which collectively boost efficiency, reduce costs, and elevate patient care. Global data networks and AI-powered platforms are enabling borderless collaboration, empowering radiologists to focus on complex decision-making and patient interaction. Despite these profound opportunities, widespread AI adoption in radiology remains limited, often confined to specific use cases, such as chest, neuro, and musculoskeletal imaging. Concerns persist regarding transparency, explainability, and the ethical use of AI systems, while unresolved questions about workload, liability, and reimbursement present additional hurdles. Psychological and cultural barriers, including fears of job displacement and diminished professional autonomy, also slow acceptance. However, history shows that disruptive innovations often encounter initial resistance. Just as the discovery of X-rays over a century ago ushered in a new era, today, digitalization and artificial intelligence will drive another paradigm shift-this time through cognitive automation. To realize AI's full potential, radiologists must maintain clinical oversight and safeguard their professional identity, viewing AI as a supportive tool rather than a threat. Embracing AI will allow radiologists to elevate their profession, enhance interdisciplinary collaboration, and help shape the future of medicine. Achieving this vision requires not only technological readiness but also early integration of AI education into medical training. Ultimately, radiology will not be replaced by AI, but by radiologists who effectively harness its capabilities.

To explore the potential role of generative artificial intelligence (GenAI) in enhancing patient counseling following prenatal ultrasound diagnosis of fetal malformations, with an emphasis on clinical utility, patient comprehension, and ethical implementation. The detection of fetal anomalies during the mid-trimester ultrasound is emotionally distressing for patients and presents significant challenges in communication and decision-making. Generative AI tools, such as GPT-4 and similar models, offer novel opportunities to support clinicians in delivering accurate, empathetic, and accessible counseling while preserving the physician's central role. We present a narrative review and applied framework illustrating how GenAI can assist obstetricians before, during, and after the fetal anomaly scan. Use cases include lay summaries, visual aids, anticipatory guidance, multilingual translation, and emotional support. Tables and sample prompts demonstrate practical applications across a range of anomalies.

Sports injuries are a significant concern for athletes at all levels of competition, ranging from acute traumas to chronic conditions. Prompt diagnosis and effective treatment are crucial for an athlete's recovery and quality of life. Traditionally, sports injury diagnosis has relied on clinical assessments, patient history, and basic imaging techniques such as X-rays, ultrasound, and magnetic resonance imaging (MRI). However, recent technological advancements have revolutionized the field of sports medicine, offering more accurate diagnoses and targeted treatment strategies. High-resolution MRI and CT scans provide detailed images of deep tissue injuries, while advanced ultrasound technology enables on-field diagnostics. Wearable sensor devices and machine learning algorithms allow real-time monitoring of an athlete's movements and physical loads, facilitating early intervention and injury risk prediction. Regenerative medicine, including stem cell therapy and tissue engineering, has emerged as a transformative approach to healing damaged tissues and reducing treatment time. Despite the challenges of high costs, lack of skilled personnel, and ethical considerations, the integration of artificial intelligence and machine learning into sports medicine holds immense potential for revolutionizing injury prevention and management. As these advancements continue to evolve, they are expected to extend athletes' careers and enhance their overall quality of life. This review summarizes conventional methods to diagnose and manage injuries and provides insights into the recent advancements in the field of sports science and medicine. It also states future outlook on the diagnosis and treatment of sports injuries.

To construct two machine learning radiomics (MLR) for invasive adenocarcinoma (IVA) prediction using normal-spatial-resolution (NSR) and high-spatial-resolution (HSR) training cohorts, and to validate models (model-NSR and -HSR) in another test cohort while comparing independent radiologists' (R1, R2) performance with and without model-HSR. In this retrospective multicenter study, all CT images were reconstructed using NSR data (512 matrix, 0.5-mm thickness) and HSR data (2048 matrix, 0.25-mm thickness). Nodules were divided into training (n = 61 non-IVA, n = 165 IVA) and test sets (n = 36 non-IVA, n = 203 IVA). Two MLR models were developed with 18 significant factors for the NSR model and 19 significant factors for the HSR model from 172 radiomics features using random forest. Area under the receiver operator characteristic curves (AUC) was analyzed using DeLong's test in the test set. Accuracy (acc), sensitivity (sen), and specificity (spc) of R1 and R2 with and without model-HSR were compared using McNemar test. 437 patients (70 ± 9 years, 203 men) had 465 nodules (n = 368, IVA). Model-HSR AUCs were significantly higher than model-NSR in training (0.839 vs. 0.723) and test sets (0.863 vs. 0.718) (p < 0.05). R1's acc (87.2%) and sen (93.1%) with model-HSR were significantly higher than without (77.0% and 79.3%) (p < 0.0001). R2's acc (83.7%) and sen (86.7%) with model-HSR might be equal or higher than without (83.7% and 85.7%, respectively), but not significant (p > 0.50). Spc of R1 (52.8%) and R2 (66.7%) with model-HSR might be lower than without (63.9% and 72.2%, respectively), but not significant (p > 0.21). HSR-based MLR model significantly increased IVA diagnostic performance compared to NSR, supporting radiologists without compromising accuracy and sensitivity. However, this benefit came at the cost of reduced specificity, potentially increasing false positives, which may lead to unnecessary examinations or overtreatment in clinical settings.

Parkinson's Disease (PD) is a progressive neurodegenerative disorder and the early diagnosis is crucial for managing symptoms and slowing disease progression. This paper proposes a framework named Federated Learning Enabled Waterwheel Shuffled Shepherd Optimization-based Efficient-Fuzzy Deep Maxout Network (FedL_WSSO based Eff-FDMNet) for PD detection and classification. In local training model, the input image from the database "Image and Data Archive (IDA)" is given for preprocessing that is performed using Gaussian filter. Consequently, image augmentation takes place and feature extraction is conducted. These processes are executed for every input image. Therefore, the collected outputs of images are used for PD detection using Shepard Convolutional Neural Network Fuzzy Zeiler and Fergus Net (ShCNN-Fuzzy-ZFNet). Then, PD classification is accomplished using Eff-FDMNet, which is trained using WSSO. At last, based on CAViaR, local updation and aggregation are changed in server. The developed method obtained highest accuracy as 0.927, mean average precision as 0.905, lowest false positive rate (FPR) as 0.082, loss as 0.073, Mean Squared Error (MSE) as 0.213, and Root Mean Squared Error (RMSE) as 0.461. The high accuracy and low error rates indicate that the potent framework can enhance patient outcomes by enabling more reliable and personalized diagnosis.

We developed new deep learning-based hierarchical brain segmentation (DLHBS) method that can segment T1-weighted MR images (T1WI) into 107 brain subregions and calculate the volume of each subregion. This study aimed to evaluate the repeatability and reproducibility of volume estimation using DLHBS and compare them with those of representative brain segmentation tools such as statistical parametric mapping (SPM) and FreeSurfer (FS). Hierarchical segmentation using multiple deep learning models was employed to segment brain subregions within a clinically feasible processing time. The T1WI and brain mask pairs in 486 subjects were used as training data for training of the deep learning segmentation models. Training data were generated using a multi-atlas registration-based method. The high quality of training data was confirmed through visual evaluation and manual correction by neuroradiologists. The brain 3D-T1WI scan-rescan data of the 11 healthy subjects were obtained using three MRI scanners for evaluating the repeatability and reproducibility. The volumes of the eight ROIs-including gray matter, white matter, cerebrospinal fluid, hippocampus, orbital gyrus, cerebellum posterior lobe, putamen, and thalamus-obtained using DLHBS, SPM 12 with default settings, and FS with the "recon-all" pipeline. These volumes were then used for evaluation of repeatability and reproducibility. In the volume measurements, the bilateral thalamus showed higher repeatability with DLHBS compared with SPM. Furthermore, DLHBS demonstrated higher repeatability than FS in across all eight ROIs. Additionally, higher reproducibility was observed with DLHBS in both hemispheres of six ROIs when compared with SPM and in five ROIs compared with FS. The lower repeatability and reproducibility in DLHBS were not observed in any comparisons. Our results showed that the best performance in both repeatability and reproducibility was found in DLHBS compared with SPM and FS.

To compare the utility of thin-slice fat-suppressed single-shot T2-weighted imaging (T2WI) with deep learning image reconstruction (DLIR) and conventional fast spin-echo T2WI with DLIR for evaluating pancreatic protocol. This retrospective study included 42 patients (mean age, 70.2 years) with pancreatic cancer who underwent gadoxetic acid-enhanced MRI. Three fat-suppressed T2WI, including conventional fast-spin echo with 6 mm thickness (FSE 6 mm), single-shot fast-spin echo with 6 mm and 3 mm thickness (SSFSE 6 mm and SSFSE 3 mm), were acquired for each patient. For quantitative analysis, the SNRs of the upper abdominal organs were calculated between images with and without DLIR. The pancreas-to-lesion contrast on DLIR images was also calculated. For qualitative analysis, two abdominal radiologists independently scored the image quality on a 5-point scale in the FSE 6 mm, SSFSE 6 mm, and SSFSE 3 mm with DLIR. The SNRs significantly improved among the three T2-weighted images with DLIR compared to those without DLIR in all patients (P < 0.001). The pancreas-to-lesion contrast of SSFSE 3 mm was higher than those of the FSE 6 mm (P < 0.001) and tended to be higher than SSFSE 6 mm (P = 0.07). SSFSE 3 mm had the highest image qualities regarding pancreas edge sharpness, pancreatic duct clarity, and overall image quality, followed by SSFSE 6 mm and FSE 6 mm (P < 0.0001). SSFSE 3 mm with DLIR demonstrated significant improvements in SNRs of the pancreas, pancreas-to-lesion contrast, and image quality more efficiently than did SSFSE 6 mm and FSE 6 mm. Thin-slice fat-suppressed single-shot T2WI with DLIR can be easily implemented for pancreatic MR protocol.

This study was conducted to evaluate whether thin-slice 2D fat-saturated proton density-weighted images of the shoulder joint in three imaging planes combined with parallel imaging, partial Fourier technique, and denoising approach with deep learning-based reconstruction (dDLR) are more useful than 3D fat-saturated proton density multi-planar voxel images. Eighteen patients who underwent MRI of the shoulder joint at 3T were enrolled. The denoising effect of dDLR in 2D was evaluated using coefficient of variation (CV). Qualitative evaluation of anatomical structures, noise, and artifacts in 2D after dDLR and 3D was performed by two radiologists using a five-point Likert scale. All were analyzed statistically. Gwet's agreement coefficients were also calculated. The CV of 2D after dDLR was significantly lower than that before dDLR (P < 0.05). Both radiologists rated 2D higher than 3D for all anatomical structures and noise (P < 0.05), except for artifacts. Both Gwet's agreement coefficients of anatomical structures, noise, and artifacts in 2D and 3D produced nearly perfect agreement between the two radiologists. The evaluation of 2D tended to be more reproducible than 3D. 2D with parallel imaging, partial Fourier technique, and dDLR was proved to be superior to 3D for depicting shoulder joint structures with lower noise.

Creating a 3D lumbar model and planning a personalized puncture trajectory has an advantage in establishing the working channel for percutaneous endoscopic lumbar discectomy (PELD). However, existing 3D lumbar models, which seldom include lumbar nerves and dural sac reconstructions, primarily depend on CT images for preoperative trajectory planning. Therefore, our study aims to further investigate the relationship between different virtual working channels and the 3D lumbar model, which includes automated MR image segmentation of lumbar bone, nerves, and dural sac at the L4/L5 level. Preoperative lumbar MR images of 50 patients with L4/L5 lumbar disc herniation were collected from a teaching hospital between March 2020 and July 2020. Automated MR image segmentation was initially used to create a 3D model of the lumbar spine, including the L4 vertebrae, L5 vertebrae, intervertebral disc, L4 nerves, dural sac, and skin. Thirty were then randomly chosen from the segmentation results to clarify the relationship between various virtual working channels and the lumbar 3D model. A bivariate Spearman's rank correlation analysis was used in this study. Preoperative MR images of 50 patients (34 males, mean age 45.6 ± 6 years) were used to train and validate the automated segmentation model, which had mean Dice scores of 0.906, 0.891, 0.896, 0.695, 0.892, and 0.892 for the L4 vertebrae, L5 vertebrae, intervertebral disc, L4 nerves, dural sac, and skin, respectively. With an increase in the coronal plane angle (CPA), there was a reduction in the intersection volume involving the L4 nerves and atypical structures. Conversely, the intersection volume encompassing the dural sac, L4 inferior articular process, and L5 superior articular process increased; the total intersection volume showed a fluctuating pattern: it initially decreased, followed by an increase, and then decreased once more. As the cross-section angle (CSA) increased, there was a rise in the intersection volume of both the L4 nerves and the dural sac; the intersection volume involving the L4 inferior articular process grew while that of the L5 superior articular process diminished; the overall intersection volume and the intersection volume of atypical structures initially decreased, followed by an increase. In terms of regularity, the optimal angles for L4/L5 PELD are a CSA of 15° and a CPA of 15°-20°, minimizing harm to the vertebral bones, facet joint, spinal nerves, and dural sac. Additionally, our 3D preoperative planning method could enhance puncture trajectories for individual patients, potentially advancing surgical navigation, robots, and artificial intelligence in PELD procedures.