Positron Emission Tomography (PET) is a powerful molecular imaging tool that visualizes radiotracer distribution to reveal physiological processes. Recent advances in total-body PET have enabled low-dose, CT-free imaging; however, accurate organ segmentation using PET-only data remains challenging. This study develops and validates a deep learning model for multi-organ PET segmentation across varied imaging conditions and tracers, addressing critical needs for fully PET-based quantitative analysis. This retrospective study employed a 3D deep learning-based model for automated multi-organ segmentation on PET images acquired under diverse conditions, including low-dose and non-attenuation-corrected scans. Using a dataset of 798 patients from multiple centers with varied tracers, model robustness and generalizability were evaluated via multi-center and cross-tracer tests. Ground-truth labels for 23 organs were generated from CT images, and segmentation accuracy was assessed using the Dice similarity coefficient (DSC). In the multi-center dataset from four different institutions, our model achieved average DSC values of 0.834, 0.825, 0.819, and 0.816 across varying dose reduction factors and correction conditions for FDG PET images. In the cross-tracer dataset, the model reached average DSC values of 0.737, 0.573, 0.830, 0.661, and 0.708 for DOTATATE, FAPI, FDG, Grazytracer, and PSMA, respectively. The proposed model demonstrated effective, fully PET-based multi-organ segmentation across a range of imaging conditions, centers, and tracers, achieving high robustness and generalizability. These findings underscore the model's potential to enhance clinical diagnostic workflows by supporting ultra-low dose PET imaging. Not applicable. This is a retrospective study based on collected data, which has been approved by the Research Ethics Committee of Ruijin Hospital affiliated to Shanghai Jiao Tong University School of Medicine.

To evaluate a deep learning-based time-of-flight (DLToF) model trained to enhance the image quality of non-ToF PET images for different tracers, reconstructed using BSREM algorithm, towards ToF images. A 3D residual U-NET model was trained using 8 different tracers (FDG: 75% and non-FDG: 25%) from 11 sites from US, Europe and Asia. A total of 309 training and 33 validation datasets scanned on GE Discovery MI (DMI) ToF scanners were used for development of DLToF models of three strengths: low (L), medium (M) and high (H). The training and validation pairs consisted of target ToF and input non-ToF BSREM reconstructions using site-preferred regularisation parameters (beta values). The contrast and noise properties of each model were defined by adjusting the beta value of target ToF images. A total of 60 DMI datasets, consisting of a set of 4 tracers (¹⁸F-FDG, ¹⁸F-PSMA, ⁶⁸Ga-PSMA, ⁶⁸Ga-DOTATATE) and 15 exams each, were collected for testing and quantitative analysis of the models based on standardized uptake value (SUV) in regions of interest (ROI) placed in lesions, lungs and liver. Each dataset includes 5 image series: ToF and non-ToF BSREM and three DLToF images. The image series (300 in total) were blind scored on a 5-point Likert score by 4 readers based on lesion detectability, diagnostic confidence, and image noise/quality. In lesion SUV_max quantification with respect to ToF BSREM, DLToF-H achieved the best results among the three models by reducing the non-ToF BSREM errors from -39% to -6% for ¹⁸F-FDG (38 lesions); from -42% to -7% for ¹⁸F-PSMA (35 lesions); from -34% to -4% for ⁶⁸Ga-PSMA (23 lesions) and from -34% to -12% for ⁶⁸Ga-DOTATATE (32 lesions). Quantification results in liver and lung also showed ToF-like performance of DLToF models. Clinical reader resulted showed that DLToF-H results in an improved lesion detectability on average for all four radiotracers whereas DLToF-L achieved the highest scores for image quality (noise level). The results of DLToF-M however showed that this model results in the best trade-off between lesion detection and noise level and hence achieved the highest score for diagnostic confidence on average for all radiotracers. This study demonstrated that the DLToF models are suitable for both FDG and non-FDG tracers and could be utilized for digital BGO PET/CT scanners to provide an image quality and lesion detectability comparable and close to ToF.

This study introduces a novel evaluation framework, GPTRadScore, to systematically assess the performance of multimodal large language models (MLLMs) in generating clinically accurate findings from CT imaging. Specifically, GPTRadScore leverages LLMs as an evaluation metric, aiming to provide a more accurate and clinically informed assessment than traditional language-specific methods. Using this framework, we evaluate the capability of several MLLMs, including GPT-4 with Vision (GPT-4V), Gemini Pro Vision, LLaVA-Med, and RadFM, to interpret findings in CT scans. This retrospective study leverages a subset of the public DeepLesion dataset to evaluate the performance of several multimodal LLMs in describing findings in CT slices. GPTRadScore was developed to assess the generated descriptions (location, body part, and type) using GPT-4, alongside traditional metrics. RadFM was fine-tuned using a subset of the DeepLesion dataset with additional labeled examples targeting complex findings. Post fine-tuning, performance was reassessed using GPTRadScore to measure accuracy improvements. Evaluations demonstrated a high correlation of GPTRadScore with clinician assessments, with Pearson's correlation coefficients of 0.87, 0.91, 0.75, 0.90, and 0.89. These results highlight its superiority over traditional metrics, such as BLEU, METEOR, and ROUGE, and indicate that GPTRadScore can serve as a reliable evaluation metric. Using GPTRadScore, it was observed that while GPT-4V and Gemini Pro Vision outperformed other models, significant areas for improvement remain, primarily due to limitations in the datasets used for training. Fine-tuning RadFM resulted in substantial accuracy gains: location accuracy increased from 3.41% to 12.8%, body part accuracy improved from 29.12% to 53%, and type accuracy rose from 9.24% to 30%. These findings reinforce the hypothesis that fine-tuning RadFM can significantly enhance its performance. GPT-4 effectively correlates with expert assessments, validating its use as a reliable metric for evaluating multimodal LLMs in radiological diagnostics. Additionally, the results underscore the efficacy of fine-tuning approaches in improving the descriptive accuracy of LLM-generated medical imaging findings.

The objective of this study is to map the global scientific competitive landscape in the field of artificial intelligence (AI) medical devices using scientific data. A bibliometric analysis was conducted using the Web of Science Core Collection to examine global research trends in AI-based medical devices. As of the end of 2023, a total of 55 147 relevant publications were identified worldwide, with 76.6% published between 2018 and 2024. Research in this field has primarily focused on AI-assisted medical image and physiological signal analysis. At the national level, China (17 991 publications) and the United States (14 032 publications) lead in output. China has shown a rapid increase in publication volume, with its 2023 output exceeding twice that of the U.S.; however, the U.S. maintains a higher average citation per paper (China: 16.29; U.S.: 35.99). At the institutional level, seven Chinese institutions and three U.S. institutions rank among the global top ten in terms of publication volume. At the researcher level, prominent contributors include Acharya U Rajendra, Rueckert Daniel and Tian Jie, who have extensively explored AI-assisted medical imaging. Some researchers have specialized in specific imaging applications, such as Yang Xiaofeng (AI-assisted precision radiotherapy for tumors) and Shen Dinggang (brain imaging analysis). Others, including Gao Xiaorong and Ming Dong, focus on AI-assisted physiological signal analysis. The results confirm the rapid global development of AI in the medical device field, with "AI + imaging" emerging as the most mature direction. China and the U.S. maintain absolute leadership in this area-China slightly leads in publication volume, while the U.S., having started earlier, demonstrates higher research quality. Both countries host a large number of active research teams in this domain.

Systemic sclerosis (SSc) is a complex inflammatory vasculopathy with diverse symptoms and variable disease progression. Despite its known impact on body composition (BC), clinical decision-making has yet to incorporate these biomarkers. This study aims to extract quantitative BC imaging biomarkers from CT scans to assess disease severity, define BC phenotypes, track changes over time and predict survival. CT exams were extracted from a prospectively maintained cohort of 452 SSc patients. 128 patients with at least one CT exam were included. An artificial intelligence-based 3D body composition analysis (BCA) algorithm assessed muscle volume, different adipose tissue compartments, and bone mineral density. These parameters were analysed with regard to various clinical, laboratory, functional parameters and survival. Phenotypes were identified performing K-means cluster analysis. Longitudinal evaluation of BCA changes employed regression analyses. A regression model using BCA parameters outperformed models based on Body Mass Index and clinical parameters in predicting survival (area under the curve (AUC)=0.75). Longitudinal development of the cardiac marker enabled prediction of survival with an AUC=0.82. Patients with altered BCA parameters had increased ORs for various complications, including interstitial lung disease (p<0.05). Two distinct BCA phenotypes were identified, showing significant differences in gastrointestinal disease manifestations (p<0.01). This study highlights several parameters with the potential to reshape clinical pathways for SSc patients. Quantitative BCA biomarkers offer a means to predict survival and individual disease manifestations, in part outperforming established parameters. These insights open new avenues for research into the mechanisms driving body composition changes in SSc and for developing enhanced disease management tools, ultimately leading to more personalised and effective patient care.

Emergency radiology has evolved into a significant subspecialty over the past 2 decades, facing unique challenges including escalating imaging volumes, increasing study complexity, and heightened expectations from clinicians and patients. This review provides a comprehensive overview of the key requirements for an effective emergency radiology unit. Emergency radiologists play a crucial role in real-time decision-making by providing continuous 24/7 support, requiring expertise across various organ systems and close collaboration with emergency physicians and specialists. Beyond image interpretation, emergency radiologists are responsible for organizing staff schedules, planning equipment, determining imaging protocols, and establishing standardized reporting systems. Operational considerations in emergency radiology departments include efficient scheduling models such as circadian-based scheduling, strategic equipment organization with primary imaging modalities positioned near emergency departments, and effective imaging management through structured ordering systems and standardized protocols. Preparedness for mass casualty incidents requires a well-organized workflow process map detailing steps from patient transfer to image acquisition and interpretation, with clear task allocation and imaging pathways. Collaboration between emergency radiologists and physicians is essential, with accurate communication facilitated through various channels and structured reporting templates. Artificial intelligence has emerged as a transformative tool in emergency radiology, offering potential benefits in both interpretative domains (detecting intracranial hemorrhage, pulmonary embolism, acute ischemic stroke) and non-interpretative applications (triage systems, protocol assistance, quality control). Despite implementation challenges including clinician skepticism, financial considerations, and ethical issues, AI can enhance diagnostic accuracy and workflow optimization. Teleradiology provides solutions for staff shortages, particularly during off-hours, with hybrid models allowing radiologists to work both on-site and remotely. This review aims to guide stakeholders in establishing and maintaining efficient emergency radiology services to improve patient outcomes.

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.

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.

Multi-isocentric intensity-modulated (IM) total body irradiation (TBI), total marrow irradiation (TMI), and total marrow and lymphoid irradiation (TMLI) are gaining popularity. A question arises on the impact of the interplay between blood circulation and dynamic delivery on blood dose. This study answers the question by introducing a new whole-body blood circulation modeling technique. A whole-body CT with intravenous contrast was used to develop the blood circulation model. Fifteen organs and tissues, heart chambers, and great vessels were segmented using a deep-learning-based auto-contouring software. The main blood vessels were segmented using an in-house algorithm. Blood density, velocity, time-to-heart, and perfusion distributions were derived for systole, diastole, and portal circulations and used to simulate trajectories of blood particles during delivery. With the same prescription of 12 Gy in 8 fractions, doses to circulating blood were calculated for three plans: (1) an IM-TBI plan prescribing uniform dose to the whole body while reducing lung and kidney doses; (2) a TMI plan treating all bones; and (3) a TMLI plan treating all bones, major lymph nodes, and spleen; TMI and TMLI plans were optimized to reduce doses to non-target tissue. Circulating blood received 1.57 ± 0.43 Gy, 1.04 ± 0.32 Gy, and 1.09 ± 0.32 Gy in one fraction and 12.60 ± 1.21 Gy, 8.34 ± 0.88 Gy, and 8.71 ± 0.92 Gy in 8 fractions in IM-TBI, TMI, and TMLI, respectively. The interplay effect of blood motion with IM delivery did not change the mean dose, but changed the dose heterogeneity of the circulating blood. Fractionation reduced the blood dose heterogeneity. A novel whole-body blood circulating model was developed based on patient-specific anatomy and realistic blood dynamics, concentration, and perfusion. Using the blood circulation model, we developed a dosimetry tool for circulating blood in IM-TBI, TMI, and TMLI.

Medical imaging data employed in research frequently comprises sensitive Protected Health Information (PHI) and Personal Identifiable Information (PII), which is subject to rigorous legal frameworks such as the General Data Protection Regulation (GDPR) or the Health Insurance Portability and Accountability Act (HIPAA). Consequently, these types of data must be de-identified prior to utilization, which presents a significant challenge for many researchers. Given the vast array of medical imaging data, it is necessary to employ a variety of de-identification techniques. To facilitate the de-identification process for medical imaging data, we have developed an open-source tool that can be used to de-identify Digital Imaging and Communications in Medicine (DICOM) magnetic resonance images, computer tomography images, whole slide images and magnetic resonance twix raw data. Furthermore, the implementation of a neural network enables the removal of text within the images. The proposed tool reaches comparable results to current state-of-the-art algorithms at reduced computational time (up to × 265). The tool also manages to fully de-identify image data of various types, such as Neuroimaging Informatics Technology Initiative (NIfTI) or Whole Slide Image (WSI-)DICOMS. The proposed tool automates an elaborate de-identification pipeline for multiple types of inputs, reducing the need for additional tools used for de-identification of imaging data. Question How can researchers effectively de-identify sensitive medical imaging data while complying with legal frameworks to protect patient health information? Findings We developed an open-source tool that automates the de-identification of various medical imaging formats, enhancing the efficiency of de-identification processes. Clinical relevance This tool addresses the critical need for robust and user-friendly de-identification solutions in medical imaging, facilitating data exchange in research while safeguarding patient privacy.