The STOIC2021 COVID-19 AI challenge: Applying reusable training methodologies to private data.

Challenges drive the state-of-the-art of automated medical image analysis. The quantity of public training data that they provide can limit the performance of their solutions. Public access to the training methodology for these solutions remains absent. This study implements the Type Three (T3) challenge format, which allows for training solutions on private data and guarantees reusable training methodologies. With T3, challenge organizers train a codebase provided by the participants on sequestered training data. T3 was implemented in the STOIC2021 challenge, with the goal of predicting from a computed tomography (CT) scan whether subjects had a severe COVID-19 infection, defined as intubation or death within one month. STOIC2021 consisted of a Qualification phase, where participants developed challenge solutions using 2000 publicly available CT scans, and a Final phase, where participants submitted their training methodologies with which solutions were trained on CT scans of 9724 subjects. The organizers successfully trained six of the eight Final phase submissions. The submitted codebases for training and running inference were released publicly. The winning solution obtained an area under the receiver operating characteristic curve for discerning between severe and non-severe COVID-19 of 0.815. The Final phase solutions of all finalists improved upon their Qualification phase solutions.

The Medical Segmentation Decathlon.

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.

Quantitative blood flow measurement in rat brain with multiphase arterial spin labelling magnetic resonance imaging.

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.

An optimized design to reduce eddy current sensitivity in velocity-selective arterial spin labeling using symmetric BIR-8 pulses.

PURPOSE: Velocity-selective arterial spin labeling (VSASL) tags arterial blood on a velocity-selective (VS) basis and eliminates the tagging/imaging gap and associated transit delay sensitivity observed in other ASL tagging methods. However, the flow-weighting gradient pulses in VS tag preparation can generate eddy currents (ECs), which may erroneously tag the static tissue and create artificial perfusion signal, compromising the accuracy of perfusion quantification. METHODS: A novel VS preparation design is presented using an eight-segment B1 insensitive rotation with symmetric radio frequency and gradient layouts (sym-BIR-8), combined with delays after gradient pulses to optimally reduce ECs of a wide range of time constants while maintaining B0 and B1 insensitivity. Bloch simulation, phantom, and in vivo experiments were carried out to determine robustness of the new and existing pulse designs to ECs, B0 , and B1 inhomogeneity. RESULTS: VSASL with reduced EC sensitivity across a wide range of EC time constants was achieved with the proposed sym-BIR-8 design, and the accuracy of cerebral blood flow measurement was improved. CONCLUSION: 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.

The influence of noise on BOLD-mediated vessel size imaging analysis methods.

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.

An optimized velocity selective arterial spin labeling module with reduced eddy current sensitivity for improved perfusion quantification.

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.