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

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

Phase contrast MRI in intracranial aneurysms

Intracranial aneurysms are outpouchings of intracranial arteries that cause brain hemorrhage after rupture. Unruptured aneurysms can be treated but the risk of treatment may outweigh the risk of rupture. Local intra-aneurysmal hemodynamics can contribute substantially to the rupture risk estimation of the individual aneurysm. The only technique capable of measuring three-dimensional flow patterns over time in vivo is time-resolved three-dimensional phase contrast MRI (PC-MRI). In this thesis in vitro PC-MRI was compared with particle image velocimetry and computational fluid dynamics (CFD). In vivo PC-MRI was compared with patient-specific CFD in eight aneurysms. Two acquisition strategies to improve PC-MRI were tested: 1) PC MRI in combination with k-t BLAST was compared with PC-MRI in combination with parallel imaging. 2) PC-MRI at 7T was compared with PC-MRI at 3T. Furthermore, a novel algorithm to calculate wall shear stress from PC-MRI data is presented, tested i This thesis demonstrates that PC-MRI can be used to quantify and visualize intra-aneurysmal flow patterns and wall shear stress