Advances in machine learning applications for cardiovascular 4D flow MRI

Four-dimensional flow magnetic resonance imaging (MRI) has evolved as a non-invasive imaging technique to visualize and quantify blood flow in the heart and vessels. Hemodynamic parameters derived from 4D flow MRI, such as net flow and peak velocities, but also kinetic energy, turbulent kinetic energy, viscous energy loss, and wall shear stress have shown to be of diagnostic relevance for cardiovascular diseases. 4D flow MRI, however, has several limitations. Its long acquisition times and its limited spatio-temporal resolutions lead to inaccuracies in velocity measurements in small and low-flow vessels and near the vessel wall. Additionally, 4D flow MRI requires long post-processing times, since inaccuracies due to the measurement process need to be corrected for and parameter quantification requires 2D and 3D contour drawing. Several machine learning (ML) techniques have been proposed to overcome these limitations. Existing scan acceleration methods have been extended using ML for image reconstruction and ML based super-resolution methods have been used to assimilate high-resolution computational fluid dynamic simulations and 4D flow MRI, which leads to more realistic velocity results. ML efforts have also focused on the automation of other post-processing steps, by learning phase corrections and anti-aliasing. To automate contour drawing and 3D segmentation, networks such as the U-Net have been widely applied. This review summarizes the latest ML advances in 4D flow MRI with a focus on technical aspects and applications. It is divided into the current status of fast and accurate 4D flow MRI data generation, ML based post-processing tools for phase correction and vessel delineation and the statistical evaluation of blood flow

Hemodynamic Parameters in the Parent Arteries of Unruptured Intracranial Aneurysms Depend on Aneurysm Size and Are Different Compared to Contralateral Arteries: A 7 Tesla 4D Flow MRI Study

Background: Different Circle of Willis (CoW) variants have variable prevalences of aneurysm development, but the hemodynamic variation along the CoW and its relation to presence and size of unruptured intracranial aneurysms (UIAs) are not well known. Purpose: Gain insight into hemodynamic imaging markers of the CoW for UIA development by comparing these outcomes to the corresponding contralateral artery without an UIA using 4D flow magnetic resonance imaging (MRI). Study Type: Retrospective, cross-sectional study. Subjects: Thirty-eight patients with an UIA, whereby 27 were women and a mean age of 62 years old. Field Strength/Sequence: Four-dimensional phase-contrast (PC) MRI with a 3D time-resolved velocity encoded gradient echo sequence at 7 T. Assessment: Hemodynamic parameters (blood flow, velocity pulsatility index [vPI], mean velocity, distensibility, and wall shear stress [peak systolic (WSSMAX), and time-averaged (WSSMEAN)]) in the parent artery of the UIA were compared to the corresponding contralateral artery without an UIA and were related to UIA size. Statistical Tests: Paired t-tests and Pearson Correlation tests. The threshold for statistical significance was P < 0.05 (two-tailed). Results: Blood flow, mean velocity, WSSMAX, and WSSMEAN were significantly higher, while vPI was lower, in the parent artery relative to contralateral artery. The WSSMAX of the parent artery significantly increased linearly while the WSSMEAN decreased linearly with increasing UIA size. Conclusions: Hemodynamic parameters and WSS differ between parent vessels of UIAs and corresponding contralateral vessels. WSS correlates with UIA size, supporting a potential hemodynamic role in aneurysm pathology. Level of Evidence: 2. Technical Efficacy: Stage 2

4D Flow cardiovascular magnetic resonance consensus statement: 2023 update

Hemodynamic assessment is an integral part of the diagnosis and management of cardiovascular disease. Four-dimensional cardiovascular magnetic resonance flow imaging (4D Flow CMR) allows comprehensive and accurate assessment of flow in a single acquisition. This consensus paper is an update from the 2015 '4D Flow CMR Consensus Statement'. We elaborate on 4D Flow CMR sequence options and imaging considerations. The document aims to assist centers starting out with 4D Flow CMR of the heart and great vessels with advice on acquisition parameters, post-processing workflows and integration into clinical practice. Furthermore, we define minimum quality assurance and validation standards for clinical centers. We also address the challenges faced in quality assurance and validation in the research setting. We also include a checklist for recommended publication standards, specifically for 4D Flow CMR. Finally, we discuss the current limitations and the future of 4D Flow CMR. This updated consensus paper will further facilitate widespread adoption of 4D Flow CMR in the clinical workflow across the globe and aid consistently high-quality publication standards