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Harrison.ai Receives FDA Breakthrough Status for Imaging AI Device
Harrison.ai has been awarded three FDA breakthrough device designations for its imaging AI solutions, including a tool for obstructive hydrocephalus triage.
Lunit Acquires Prognosia to Boost AI Mammography Risk Prediction
Lunit has acquired Prognosia, an AI-driven breast cancer risk prediction startup founded by WashU researchers, to accelerate clinical adoption of advanced mammography analytics.
Dana-Farber Showcases AI and Clinical Trial Advances at ESMO 2025
Dana-Farber researchers present major cancer clinical trial results, including AI-driven data analysis, at ESMO Congress 2025.
From the Research Hub
Artificial Intelligence in Abdominal MRI Diagnostics: Current Applications, Challenges, and Future Perspectives.
The increasing availability of large image data sets and technical advances in the field of information technology have also greatly advanced the use of artificial intelligence (AI) in radiology in recent years. Especially in the field of abdominal MRI diagnostics, there are numerous opportunities to use AI applications to provide efficient, objective, and standardized image acquisition and diagnosis.This review summarizes the current state of research and clinical application of AI in abdominal MRI diagnostics with the help of a literature search via PubMed. The focus is on interpretive areas of application such as automatic segmentation of abdominal organs, classification of pathologies, and quantitative analysis of a wide range of abdominal diseases. In addition, the technical requirements, challenges and limitations as well as ethical aspects are systematically examined.AI-based systems show promising preclinical results, for example, in image reconstruction, segmentation, detection and characterization of lesions, as well as in the classification, for example, of PSC-typical bile duct changes based on MRCP. Interestingly, however, compared to other organ-specific applications in radiology, there are only a few clinically usable tools in abdominal imaging. In addition, there are still major challenges due to the often very heterogeneous data quality, the availability of carefully annotated image data, and legal and ethical safeguards. However, the issues of cost structure and profitability, as well as the remuneration of AI-based applications, also play a significant role and need to be clarified.Despite the great potential and promising preclinical work, the integration of AI systems in abdominal MRI is not yet established in everyday clinical practice. Successful clinical implementation requires standardized workflows, transparent model architecture, legally compliant framework conditions, clear reimbursement guidelines, and the active involvement of radiological expertise. In the future, multimodal, predictive systems with the integration of supplementary clinical data and the ethically reflected design of AI-supported decision-making processes will become increasingly important. · Compared to other application areas within radiology, there are still very few dedicated and validated AI applications for abdominal MRI, which is mainly due to the comparatively complex data structure and the high inter-individual variability of the abdomen.. · For successful integration into clinical practice, it is essential to have multi-center training data sets, such as those found in the context of large cohort studies, as well as transparent data protection and competitive remuneration.. · Ragab H, Aydemir DG, Cicek H et al. Artificial Intelligence in Abdominal MRI Diagnostics: Current Applications, Challenges, and Future Perspectives. Rofo 2025; DOI 10.1055/a-2704-7577.
Joint frequency-image domain network for image restoration in magnetic particle imaging.
<i>Objective.</i>Magnetic Particle Imaging (MPI) is a promising medical imaging technique that has been widely applied in preclinical stages. However, when expanding to human body scanning, cases often arise where superparamagnetic iron oxide nanoparticles (SPIOs) are located outside the field of view (FOV). In such cases, signal contributions from SPIOs outside the FOV generate boundary artifacts in the reconstructed images, compromising image accuracy. Therefore, restoring the affected images is crucial for the clinical translation of the MPI technology. Existing methods, such as overlapping scanning trajectories or joint reconstruction, effectively mitigate boundary artifacts but may still require further improvements in real-time imaging capabilities.<i>Approach.</i>In this study, we explore and utilize the spectral differences between SPIO signals inside and outside the FOV to design a dual-domain joint learning network for accurate restoration of MPI images. The network simultaneously takes as input both the affected images and their corresponding time-frequency map. Through feature extraction and adaptive weighted fusion, the network enhances its own ability to restore images.<i>Main results.</i>Our proposed Joint Frequency-Image Domain Network (JFI-Net) outperforms existing methods on the publicly available OpenMPI dataset and simulation datasets. Additionally, the network is applied to an in-house handheld MPI system, improving its imaging accuracy for large-sized vessel phantoms. Ablation experiments confirm the effectiveness of the proposed feature extraction and feature fusion modules within the network.<i>Significance.</i>This study presents an innovative solution to overcome boundary artifacts in MPI, significantly enhancing its quantitative accuracy for clinical applications. The proposed JFI-Net offers an efficient image restoration method that can contribute to the application of MPI technology in clinical practice.
Towards Clinical-Grade Cardiac MRI Segmentation: An Ensemble of Improved UNet Architectures
Accurate cardiac MRI segmentation is essential for quantitative analysis of cardiac structure and function in clinical practice. In this study, we propose an ensemble framework combining several improved UNet-based architectures to achieve robust and clinically reliable segmentation performance. The ensemble integrates multiple models, including variants of standard UNet, Residual UNet, and Attention UNet, optimized through extensive hyperparameter tuning and data augmentation on the CAMUS subject-based dataset. Experimental results demonstrate that our approach achieves a Dice similarity coefficient of 0.91, surpassing several state- of-the-art methods reported in recent literature. Moreover, the proposed ensemble exhibits exceptional stability across subjects and maintains high generalization performance, indicating its strong potential for real-world clinical deployment. This work highlights the effectiveness of ensemble deep learning techniques for cardiac image segmentation and represents a promising step towards clinical-grade automated analysis in cardiac imaging.
From the RadAI FDA Approvals Database
Canon Medical Systems Corporation
Alphenix, INFX-8000V/B, INFX-8000V/S, V9.6 with aEvolve Imaging
The Alphenix INFX-8000V is an interventional fluoroscopic X-ray system designed by Canon Medical Systems Corporation. It provides real-time X-ray imaging to assist clinicians during interventional procedures, allowing enhanced visualization of anatomy and devices, thereby improving procedure accuracy and patient care.
iRay Imaging Technology (Haining) Limited
Lux HD 2530 detector (Lux HD 2530)
The Lux HD 2530 detector is a digital flat panel X-ray imager used in radiology to capture high-quality X-ray images. It helps clinicians obtain detailed radiographic images for diagnosis and treatment planning with improved image quality over traditional detectors.
Philips Ultrasound LLC
EPIQ Series Diagnostic Ultrasound System; Affiniti Series Diagnostic Ultrasound System
The EPIQ Series and Affiniti Series Diagnostic Ultrasound Systems by Philips Ultrasound LLC are advanced ultrasound imaging devices that help clinicians visualize and assess various body regions using pulsed Doppler technology. These systems support medical diagnosis by providing high-quality ultrasound images and measurements, facilitating patient care in many clinical settings.
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