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.

Velocity-selective (VS) arterial spin labeling is a promising method for measuring perfusion in areas of slow or collateral flow by eliminating the bolus arrival delay associated with other spin labeling techniques. However, B(0) and B(1) inhomogeneities and eddy currents during the VS preparation hinder accurate quantification of perfusion with VS arterial spin labeling. In this study, it is demonstrated through simulations and experiments in healthy volunteers that eddy currents cause erroneous tagging of static tissue. Consequently, mean gray matter perfusion is overestimated by up to a factor of 2, depending on the VS preparation used. A novel eight-segment B(1) insensitive rotation VS preparation is proposed to reduce eddy current effects while maintaining the B(0) and B(1) insensitivity of previous preparations. Compared to two previous VS preparations, the eight-segment B(1) insensitive rotation is the most robust to eddy currents and should improve the quality and reliability of VS arterial spin labeling measurements in future studies.

Cerebral blood flow is an important parameter in many diseases and functional studies that can be accurately measured in humans using arterial spin labelling (ASL) MRI. However, although rat models are frequently used for preclinical studies of both human disease and brain function, rat CBF measurements show poor consistency between studies. This lack of reproducibility is due, partly, to the smaller size and differing head geometry of rats compared to humans, as well as the differing analysis methodologies employed and higher field strengths used for preclinical MRI. To address these issues, we have implemented, optimised and validated a multiphase pseudo-continuous ASL technique, which overcomes many of the limitations of rat CBF measurement. Three rat strains (Wistar, Sprague Dawley and Berlin Druckrey IX) were used, and CBF values validated against gold-standard autoradiography measurements. Label positioning was found to be optimal at 45°, while post-label delay was optimised to 0.55 s. Whole brain CBF measures were 109 ± 22, 111 ± 18 and 100 ± 15 mL/100 g/min by multiphase pCASL, and 108 ± 12, 116 ± 14 and 122 ± 16 mL/100 g/min by autoradiography in Wistar, SD and BDIX cohorts, respectively. Tumour model analysis shows that the developed methods also apply in disease states. Thus, optimised multiphase pCASL provides robust, reproducible and non-invasive measurement of CBF in rats.

PurposeNoncontrast 4D‐MR‐angiography (MRA) using arterial spin labeling (ASL) is beneficial because high spatial and temporal resolution can be achieved. However, ASL requires acquisition of labeled and control images for each phase. The purpose of this study is to present a new accelerated 4D‐MRA approach that requires only a single control acquisition, achieving similar image quality in approximately half the scan time.MethodsIn a multi‐phase Look‐Locker sequence, the first phase was used as the control image and the labeling pulse was applied before the second phase. By acquiring the control and labeled images within a single Look‐Locker cycle, 4D‐MRA was generated in nearly half the scan time of conventional ASL. However, this approach potentially could be more sensitive to off‐resonance and magnetization transfer (MT) effects. To counter this, careful optimizations of the labeling pulse were performed by Bloch simulations. In in‐vivo studies arterial visualization was compared between the new and conventional ASL approaches.ResultsOptimization of the labeling pulse successfully minimized off‐resonance effects. Qualitative assessment showed that residual MT effects did not degrade visualization of the peripheral arteries.ConclusionThis study demonstrated that the proposed approach achieved similar image quality as conventional ASL‐MRA approaches in just over half the scan time. Magn Reson Med 79:224–233, 2018. © 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

Vessel size imaging (VSI) is a magnetic resonance imaging (MRI) technique that aims to provide quantitative measurements of tissue microvasculature. An emerging variation of this technique uses the blood oxygenation level-dependent (BOLD) effect as the source of the imaging contrast. Gas challenges have the advantage over contrast injection techniques in that they are noninvasive and easily repeatable because of the fast washout of the contrast. However, initial results from BOLD-VSI studies are somewhat contradictory, with substantially different estimates of the mean vessel radius. Owing to BOLD-VSI being an emerging technique, there is not yet a standard processing methodology, and different techniques have been used to calculate the mean vessel radius and reject uncertain estimates. In addition, the acquisition methodology and signal modeling vary from group to group. Owing to these differences, it is difficult to determine the source of this variation. Here we use computer modeling to assess the impact of noise on the accuracy and precision of different BOLD-VSI calculations. Our results show both potential overestimates and underestimates of the mean vessel radius, which is confirmed with a validation study at 3T.