Magnetic resonance imaging (MRI) involves a trade-off between imaging time, signal-to-noise ratio (SNR), and spatial resolution. Reducing the imaging time often leads to a lower SNR or resolution. Deep-learning-based reconstruction (DLR) methods have been introduced to address these limitations. Image-domain super-resolution DLR enables high resolution without additional image scans. High-quality images can be obtained within a shorter timeframe by appropriately configuring DLR parameters. It is necessary to maximize the performance of super-resolution DLR to enable efficient use in MRI. We evaluated the performance of a vendor-provided super-resolution DLR method (PIQE) on a Canon 3 T MRI scanner using an edge phantom and clinical brain images from eight patients. Quantitative assessment included structural similarity index (SSIM), peak SNR (PSNR), root mean square error (RMSE), and full width at half maximum (FWHM). FWHM was used to quantitatively assess spatial resolution and image sharpness. Visual evaluation using a five-point Likert scale was also performed to assess perceived image quality. Image domain super-resolution DLR reduced scan time by up to 70 % while preserving the structural image quality. Acquisition matrices of 0.87 mm/pixel or finer with a zoom ratio of ×2 yielded SSIM ≥0.80, PSNR ≥35 dB, and non-significant FWHM differences compared to full-resolution references. In contrast, aggressive downsampling (zoom ratio 3 from low-resolution matrices) led to image degradation including truncation artifacts and reduced sharpness. These results clarify the optimal use of PIQE as an image-domain super-resolution method and provide practical guidance for its application in clinical MRI workflows.

Parkinson's disease (PD) and progressive supranuclear palsy (PSP) present similar clinical symptoms, but their treatment options and clinical prognosis differ significantly. Therefore, we aimed to discriminate between PD and PSP based on multi-level indices of resting-state functional magnetic resonance imaging (rs-fMRI) via the machine learning approach. A total of 58 PD and 52 PSP patients were prospectively enrolled in this study. Participants were randomly allocated to a training set and a validation set in a 7:3 ratio. Various rs-fMRI indices were extracted, followed by a comprehensive feature screening for each index. We constructed fifteen distinct combinations of indices and selected four machine learning algorithms for model development. Subsequently, different validation templates were employed to assess the classification results and investigate the relationship between the most significant features and clinical assessment scales. The classification performance of logistic regression (LR) and support vector machine (SVM) models, based on multiple index combinations, was significantly superior to that of other machine learning models and combinations when utilizing automatic anatomical labeling (AAL) templates. This has been verified across different templates. The utilization of multiple rs-fMRI indices significantly enhances the performance of machine learning models and can effectively achieve the automatic identification of PD and PSP at the individual level.

Thyroid eye disease (TED) is a common, complex orbital disorder characterized by soft-tissue changes visible on imaging. Artificial intelligence (AI) offers promises for improving TED diagnosis and treatment; however, no systematic review has yet characterized the research landscape, key challenges, and future directions. We followed PRISMA guidelines to search multiple databases until January, 2025, for studies applying AI to computed tomography (CT), magnetic resonance imaging, and nuclear, facial or retinal imaging in TED patients. Using the APPRAISE-AI tool, we assessed study quality and included 41 studies covering various AI applications. Sample sizes ranged from 33 to 2,288 participants, predominantly East Asian. CT and facial imaging were the most common modalities, reported in 16 and 13 articles, respectively. Studies addressed clinical tasks-diagnosis, activity assessment, severity grading, and treatment prediction-and technical tasks-classification, segmentation, and image generation-with classification being the most frequent. Researchers primarily employed deep-learning models, such as residual network (ResNet) and Visual Geometry Group (VGG). Overall, the majority of the studies were of moderate quality. Image-based AI shows strong potential to improve diagnostic accuracy and guide personalized treatment strategies in TED. Future research should prioritize robust study designs, the creation of public datasets, multimodal imaging integration, and interdisciplinary collaboration to accelerate clinical translation.

Limited-angle computed tomography (LACT) offers patient-friendly benefits, such as rapid scanning and reduced radiation exposure. However, the incompleteness of data in LACT often causes notable artifacts, posing challenges for precise medical interpretation. Although numerous approaches have been introduced to reconstruct LACT images into complete computed tomography (CT) scans, they focus on improving image quality and operate separately from lesion segmentation models, often overlooking essential lesion-specific information. This is because reconstruction models are primarily optimized to satisfy overall image quality rather than local lesion-specific regions, in a non-end-to-end setup where each component is optimized independently and may not contribute to reaching the global minimum of the overall objective function. To address this problem, we propose LA-Seg, a transformer-based segmentation model using the sinogram domain of LACT data. The LA-Seg method uses an auxiliary reconstruction task to estimates incomplete sinogram regions to enhance segmentation robustness. Applying transformers adapted from video prediction models captures the spatial structure and sequential patterns in sinograms and reconstructs features in incomplete regions using a disentangled representation guided by distinctive patterns. We propose contrastive abnormal feature loss to distinguish between normal and abnormal regions better. The experimental results demonstrate that LA-Seg consistently surpasses existing medical segmentation approaches in diverse LACT conditions. The source code is provided at https://github.com/jhyoon964/LA-Seg.

Chat Generative Pre-trained Transformer (ChatGPT)-4V, a large language model developed by OpenAI, has been explored for its potential application in radiology. This study assesses ChatGPT-4V's diagnostic performance in identifying various types of intracranial hemorrhages in non-contrast cranial computed tomography (CT) images. Intracranial hemorrhages were presented to ChatGPT using the clearest 2D imaging slices. The first question, "Q1: Which imaging technique is used in this image?" was asked to determine the imaging modality. ChatGPT was then prompted with the second question, "Q2: What do you see in this image and what is the final diagnosis?" to assess whether the CT scan was normal or showed pathology. For CT scans containing hemorrhage that ChatGPT did not interpret correctly, a follow-up question-"Q3: There is bleeding in this image. Which type of bleeding do you see?"-was used to evaluate whether this guidance influenced its response. ChatGPT accurately identified the imaging technique (Q1) in all cases but demonstrated difficulty diagnosing epidural hematoma (EDH), subdural hematoma (SDH), and subarachnoid hemorrhage (SAH) when no clues were provided (Q2). When a hemorrhage clue was introduced (Q3), ChatGPT correctly identified EDH in 16.7% of cases, SDH in 60%, and SAH in 15.6%, and achieved 100% diagnostic accuracy for hemorrhagic cerebrovascular disease. Its sensitivity, specificity, and accuracy for Q2 were 23.6%, 92.5%, and 57.4%, respectively. These values improved substantially with the clue in Q3, with sensitivity rising to 50.9% and accuracy to 71.3%. ChatGPT also demonstrated higher diagnostic accuracy in larger hemorrhages in EDH and SDH images. Although the model performs well in recognizing imaging modalities, its diagnostic accuracy substantially improves when guided by additional contextual information. These findings suggest that ChatGPT's diagnostic performance improves with guided prompts, highlighting its potential as a supportive tool in clinical radiology.

Artificial intelligence (AI) is rapidly advancing in health care, particularly in medical imaging, offering potential for improved efficiency and reduced workload. However, there is little systematic evidence on process factors for successful AI technology implementation into clinical workflows. This study aimed to systematically assess and synthesize the facilitators and barriers to AI implementation reported in studies evaluating AI solutions in routine medical imaging. We conducted a systematic review of 6 medical databases. Using a qualitative content analysis, we extracted the reported facilitators and barriers, outcomes, and moderators in the implementation process of AI. Two reviewers analyzed and categorized the data separately. We then used epistemic network analysis to explore their relationships across different stages of AI implementation. Our search yielded 13,756 records. After screening, we included 38 original studies in our final review. We identified 12 key dimensions and 37 subthemes that influence the implementation of AI in health care workflows. Key dimensions included evaluation of AI use and fit into workflow, with frequency depending considerably on the stage of the implementation process. In total, 20 themes were mentioned as both facilitators and barriers to AI implementation. Studies often focused predominantly on performance metrics over the experiences or outcomes of clinicians. This systematic review provides a thorough synthesis of facilitators and barriers to successful AI implementation in medical imaging. Our study highlights the usefulness of AI technologies in clinical care and the fit of their integration into routine clinical workflows. Most studies did not directly report facilitators and barriers to AI implementation, underscoring the importance of comprehensive reporting to foster knowledge sharing. Our findings reveal a predominant focus on technological aspects of AI adoption in clinical work, highlighting the need for holistic, human-centric consideration to fully leverage the potential of AI in health care. PROSPERO CRD42022303439; https://www.crd.york.ac.uk/PROSPERO/view/CRD42022303439. RR2-10.2196/40485.

Low-dose computed tomography plays a crucial role in reducing radiation exposure in clinical imaging, however, the resultant noise significantly impacts image quality and diagnostic precision. Recent transformer-based models have demonstrated strong denoising capabilities but are often constrained by high computational complexity. To overcome these limitations, we propose AMFA-Net, an adaptive multi-order feature aggregation network that provides a lightweight architecture for enhancing highresolution feature representation in low-dose CT imaging. AMFA-Net effectively integrates local and global contexts within high-resolution feature maps while learning discriminative representations through multi-order context aggregation. We introduce an agent-based self-attention crossshaped window transformer block that efficiently captures global context in high-resolution feature maps, which is subsequently fused with backbone features to preserve critical structural information. Our approach employs multiorder gated aggregation to adaptively guide the network in capturing expressive interactions that may be overlooked in fused features, thereby producing robust representations for denoised image reconstruction. Experiments on two challenging public datasets with 25% and 10% full-dose CT image quality demonstrate that our method surpasses state-of-the-art approaches in denoising performance with low computational cost, highlighting its potential for realtime medical applications.

Pedicle screw manufacturer identification is crucial for revision surgery planning; however, this information is occasionally unavailable. We developed a deep learning-based algorithm to identify the pedicle screw manufacturer from plain radiographs. We collected anteroposterior (AP) and lateral radiographs from 276 patients who had thoracolumbar spine surgery with pedicle screws from three international manufacturers. The samples were randomly assigned to training sets (178), validation sets (40), and test sets (58). The algorithm incorporated a convolutional neural network (CNN) model to classify the radiograph as AP and lateral, followed by YOLO object detection to locate the pedicle screw. Another CNN classifier model then identified the manufacturer of each pedicle screw in AP and lateral views. The voting scheme determined the final classification. For comparison, two spine surgeons independently evaluated the same test set, and the accuracy was compared. The mean age of the patients was 59.5 years, with 1,887 pedicle screws included. The algorithm achieved a perfect accuracy of 100% for the AP radiograph, 98.9% for the lateral radiograph, and 100% when both views were considered. By comparison, the spine surgeons achieved 97.1% accuracy. Statistical analysis revealed near-perfect agreement between the algorithm and the surgeons. We have successfully developed an algorithm for pedicle screw manufacturer identification, which demonstrated excellent accuracy and was comparable to experienced spine surgeons.

Large Language Models (LLMs) have demonstrated significant potential in Medical Report Generation (MRG), yet their development requires large amounts of medical image-report pairs, which are commonly scattered across multiple centers. Centralizing these data is exceptionally challenging due to privacy regulations, thereby impeding model development and broader adoption of LLM-driven MRG models. To address this challenge, we present FedMRG, the first framework that leverages Federated Learning (FL) to enable privacy-preserving, multi-center development of LLM-driven MRG models, specifically designed to overcome the critical challenge of communication-efficient LLM training under multi-modal data heterogeneity. To start with, our framework tackles the fundamental challenge of communication overhead in federated LLM tuning by employing low-rank factorization to efficiently decompose parameter updates, significantly reducing gradient transmission costs and making LLM-driven MRG feasible in bandwidth-constrained FL settings. Furthermore, we observed the dual heterogeneity in MRG under the FL scenario: varying image characteristics across medical centers, as well as diverse reporting styles and terminology preferences. To address the data heterogeneity, we further enhance FedMRG with (1) client-aware contrastive learning in the MRG encoder, coupled with diagnosis-driven prompts, which capture both globally generalizable and locally distinctive features while maintaining diagnostic accuracy; and (2) a dual-adapter mutual boosting mechanism in the MRG decoder that harmonizes generic and specialized adapters to address variations in reporting styles and terminology. Through extensive evaluation of our established FL-MRG benchmark, we demonstrate the generalizability and adaptability of FedMRG, underscoring its potential in harnessing multi-center data and generating clinically accurate reports while maintaining communication efficiency.

To propose a deep learning (DL) system for the preoperative diagnosis of follicular-like thyroid neoplasms (FNs) using routine ultrasound images. Preoperative diagnosis of malignancy in nodules suspicious for an FN remains challenging. Ultrasound, fine-needle aspiration cytology, and intraoperative frozen section pathology cannot unambiguously distinguish between benign and malignant FNs, leading to unnecessary biopsies and operations in benign nodules. This multicenter, retrospective study included 3634 patients who underwent ultrasound and received a definite diagnosis of FN from 11 centers, comprising thyroid follicular adenoma (n=1748), follicular carcinoma (n=299), and follicular variant of papillary thyroid carcinoma (n=1587). Four DL models including Inception-v3, ResNet50, Inception-ResNet-v2, and DenseNet161 were constructed on a training set (n=2587, 6178 images) and were verified on an internal validation set (n=648, 1633 images) and an external validation set (n=399, 847 images). The diagnostic efficacy of the DL models was evaluated against the ACR TI-RADS regarding the area under the curve (AUC), sensitivity, specificity, and unnecessary biopsy rate. When externally validated, the four DL models yielded robust and comparable performance, with AUCs of 82.2%-85.2%, sensitivities of 69.6%-76.0%, and specificities of 84.1%-89.2%, which outperformed the ACR TI-RADS. Compared to ACR TI-RADS, the DL models showed a higher biopsy rate of malignancy (71.6% -79.9% vs 37.7%, P<0.001) and a significantly lower unnecessary FNAB rate (8.5% -12.8% vs 40.7%, P<0.001). This study provides a noninvasive DL tool for accurate preoperative diagnosis of FNs, showing better performance than ACR TI-RADS and reducing unnecessary invasive interventions.