CORADS-AI is a freely accessible deep learning algorithm that automatically assigns CO-RADS and CT severity scores to non-contrast CT scans of patients suspected of COVID-19 with high diagnostic performance.

International challenges have become the de facto standard for comparative assessment of image analysis algorithms. Although segmentation is the most widely investigated medical image processing task, the various challenges have been organized to focus only on specific clinical tasks. We organized the Medical Segmentation Decathlon (MSD)—a biomedical image analysis challenge, in which algorithms compete in a multitude of both tasks and modalities to investigate the hypothesis that a method capable of performing well on multiple tasks will generalize well to a previously unseen task and potentially outperform a custom-designed solution. MSD results confirmed this hypothesis, moreover, MSD winner continued generalizing well to a wide range of other clinical problems for the next two years. Three main conclusions can be drawn from this study: (1) state-of-the-art image segmentation algorithms generalize well when retrained on unseen tasks; (2) consistent algorithmic performance across multiple tasks is a strong surrogate of algorithmic generalizability; (3) the training of accurate AI segmentation models is now commoditized to scientists that are not versed in AI model training.

This review article provides an overview of the current status of velocity-selective arterial spin labeling (VSASL) perfusion MRI and is part of a wider effort arising from the International Society for Magnetic Resonance in Medicine (ISMRM) Perfusion Study Group. Since publication of the 2015 consensus paper on arterial spin labeling (ASL) for cerebral perfusion imaging, important advancements have been made in the field. The ASL community has, therefore, decided to provide an extended perspective on various aspects of technical development and application. Because VSASL has the potential to become a principal ASL method because of its unique advantages over traditional approaches, an in-depth discussion was warranted. VSASL labels blood based on its velocity and creates a magnetic bolus immediately proximal to the microvasculature within the imaging volume. VSASL is, therefore, insensitive to transit delay effects, in contrast to spatially selective pulsed and (pseudo-) continuous ASL approaches. Recent technical developments have improved the robustness and the labeling efficiency of VSASL, making it a potentially moreThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

The sym-BIR-8 design performed the most robustly among the existing VS tagging designs, and should benefit studies using VS preparation with improved accuracy and reliability.