Sven Petersson

Assessment of the accuracy of MRI wall shear stress estimation using numerical simulations

To investigate the accuracy of wall shear stress (WSS) estimation using MRI. Specifically, to investigate the impact of different parameters and if MRI WSS estimates are monotonically related to actual WSS.

Four-Dimensional Flow MRI Using Spiral Acquisition

Time‐resolved three‐dimensional phase‐contrast MRI is an important tool for physiological as well as clinical studies of blood flow in the heart and vessels. The application of the technique is, however, limited by the long scan times required. In this work, we investigate the feasibility of using spiral readouts to reduce the scan time of four‐dimensional flow MRI without sacrificing quality. Three spiral approaches are presented and evaluated in vivo and in vitro against a conventional Cartesian acquisition. In vivo, the performance of each method was assessed in the thoracic aorta in 10 volunteers using pathline‐based analysis and cardiac output analysis. Signal‐to‐noise ratio and background phase errors were investigated in vitro. Using spiral readouts, the scan times of a four‐dimensional flow acquisition of the thoracic aorta could be reduced 2–3‐fold, with no statistically significant difference in pathline validity or cardiac output. The shortened scan time improves the applicability of four‐dimensional flow MRI, which may allow the technique to become a part of a clinical workflow for cardiovascular functional imaging. Magn Reson Med, 2012.

Simulation of phase contrast MRI of turbulent flow

Phase contrast MRI is a powerful tool for the assessment of blood flow. However, especially in the highly complex and turbulent flow that accompanies many cardiovascular diseases, phase contrast MRI may suffer from artifacts. Simulation of phase contrast MRI of turbulent flow could increase our understanding of phase contrast MRI artifacts in turbulent flows and facilitate the development of phase contrast MRI methods for the assessment of turbulent blood flow. We present a method for the simulation of phase contrast MRI measurements of turbulent flow. The method uses an Eulerian‐Lagrangian approach, in which spin particle trajectories are computed from time‐resolved large eddy simulations. The Bloch equations are solved for each spin for a frame of reference moving along the spins trajectory. The method was validated by comparison with phase contrast MRI measurements of velocity and intravoxel velocity standard deviation (IVSD) on a flow phantom consisting of a straight rigid pipe with a stenosis. Turbulence related artifacts, such as signal drop and ghosting, could be recognized in the measurements as well as in the simulations. The velocity and the IVSD obtained from the magnitude of the phase contrast MRI simulations agreed well with the measurements. Magn Reson Med, 2010.

Automatic multi-vessel volume flow calculation with 4D flow CMR

Volume flow analysis is essential in the assessment of many cardiovascular diseases such as valvular regurgitation, intra-cardiac shunt, and complex congenital heart diseases. Clinically, CMR-based volume flow analysis is performed using 2D flow CMR. This requires user-dependent and time-consuming positioning of 2D planes in each vessel while the patient is still in the scanner. Previous studies have demonstrated that 4D flow CMR permits accurate volume flow assessment. However, retrospective plane-positioning and region-of-interest delineation requires time-consuming user interaction. The aim of this study was to develop an automatic method for volume flow analysis in the great thoracic vessels using 4D flow CMR. Methods The automatic multi-vessel volume flow calculation method is illustrated in Figure 1. An atlas (reference vessel segmentation) was created by manual segmentation of the great thoracic vessels in one healthy volunteer. The segmentation was done on a 3D PC-MRA which was derived from the 4D flow CMR data. Analysis planes for volume flow determination were positioned in the proximal ascending aorta and pulmonary trunk. For each subject, the atlas’ PC-MRA was registered to the subject’s PC-MRA. In this way, the atlas’ vessels and analysis planes were transformed into the subject’s vessels. The transformed atlas was transferred to all timeframes using the 4D flow CMR magnitude image, resulting in a time-resolved segmentation that follows the motion of the vessels over the cardiac cycle. Finally, the volume flow was automatically calculated for each plane using the time-resolved atlas as a mask to account for vessel location, shape and movement. The method was evaluated in a group of subjects composed of 10 healthy volunteers and 11 patients with heart failure of different etiologies. Results in the proximal ascending aorta were compared against volume flow values obtained by manual segmentation. Additionally, the pulmonary-to-aortic flow ratio (Qp/Qs) was assessed. Results

Atlas-based analysis of 4D flow CMR: Automated vessel segmentation and flow quantification

BackgroundFlow volume quantification in the great thoracic vessels is used in the assessment of several cardiovascular diseases. Clinically, it is often based on semi-automatic segmentation of a vessel throughout the cardiac cycle in 2D cine phase-contrast Cardiovascular Magnetic Resonance (CMR) images. Three-dimensional (3D), time-resolved phase-contrast CMR with three-directional velocity encoding (4D flow CMR) permits assessment of net flow volumes and flow patterns retrospectively at any location in a time-resolved 3D volume. However, analysis of these datasets can be demanding. The aim of this study is to develop and evaluate a fully automatic method for segmentation and analysis of 4D flow CMR data of the great thoracic vessels.MethodsThe proposed method utilizes atlas-based segmentation to segment the great thoracic vessels in systole, and registration between different time frames of the cardiac cycle in order to segment these vessels over time. Additionally, net flow volumes are calculated automatically at locations of interest. The method was applied on 4D flow CMR datasets obtained from 11 healthy volunteers and 10 patients with heart failure. Evaluation of the method was performed visually, and by comparison of net flow volumes in the ascending aorta obtained automatically (using the proposed method), and semi-automatically. Further evaluation was done by comparison of net flow volumes obtained automatically at different locations in the aorta, pulmonary artery, and caval veins.ResultsVisual evaluation of the generated segmentations resulted in good outcomes for all the major vessels in all but one dataset. The comparison between automatically and semi-automatically obtained net flow volumes in the ascending aorta resulted in very high correlation (r2=0.926). Moreover, comparison of the net flow volumes obtained automatically in other vessel locations also produced high correlations where expected: pulmonary trunk vs. proximal ascending aorta (r2=0.955), pulmonary trunk vs. pulmonary branches (r2=0.808), and pulmonary trunk vs. caval veins (r2=0.906).ConclusionsThe proposed method allows for automatic analysis of 4D flow CMR data, including vessel segmentation, assessment of flow volumes at locations of interest, and 4D flow visualization. This constitutes an important step towards facilitating the clinical utility of 4D flow CMR.

Retrospectively gated intracardiac 4D flow MRI using spiral trajectories.

To develop and evaluate retrospectively gated spiral readout four‐dimensional (4D) flow MRI for intracardiac flow analysis.

Quantification of turbulence and velocity in stenotic flow using spiral three-dimensional phase-contrast MRI.

Evaluate spiral three‐dimensional (3D) phase contrast MRI for the assessment of turbulence and velocity in stenotic flow.

Spiral readouts for 4D flow MRI

The feasibility of using spiral readouts to reduce the scan time of 4D flow MRI without sacrificing data quality was investigated.

Retrospectively gated intra-cardiac 4D flow CMR using spiral k-space trajectories

Background Time-resolved three-dimensional phase contrast CMR (4D flow) is a powerful tool for hemodynamic assessment in the cardiovascular system. However, long scan times have hindered the application of the method in many cases. By using spiral readout trajectories, improved efficiency provides a means of reducing scan times without decreasing SNR. Furthermore, spiral acquisition offers increased robustness in areas with accelerating flow. Spiral readouts have previously been used for rapid 4D flow measurements in the aorta using prospective gating [1]. Using retrospective gating, the entire cardiac cycle is covered, which allows analysis of late diastole and tracking of blood over a complete cardiac cycle. These are crucial for cardiac 4D flow studies, and allow for pathline based data quality assessment. The aim of this work is to develop a retrospectively gated 4D flow sequence using a stack of spiral readouts for the measurement of intra-cardiac velocities.

Accuracy of MRI wall shear stress estimation

Methods Three methods for WSS estimation were studied. These methods are based on 1) linear extrapolation (LE) of MRI velocity data, 2) MRI velocity data in combination with estimation of location of vessel wall, and 3) Fourier velocity encoding (FVE). Numerical velocity fields representing axisymmetric 2D velocity profiles were generated for WSS values ranging from 1-20 N/m. Based on the numerical velocity fields, phase-contrast MRI data voxels were simulated as follows: A jinc-function was used to model the 2D point spread function (PSF), and this PSF was used to obtain each voxel’s intravoxel velocity distribution. The phasecontrast MRI signal of each voxel was simulated by taking the Fourier transform of this distribution. To account for the fact that voxels cannot be positioned exactly at the wall in an MR-experiment, all simulations were carried out for ten different voxel positions uniformly distributed over one voxel length. In the LE method the spatial velocity derivative was estimated as the velocity difference between the two adjacent nearwall voxels divided by the distance between them. In the wall-based method, WSS was estimated by dividing the linear interpolated velocity at one voxel distance from the wall by the distance to the wall. Errors in segmentations of wall position were accounted for by modeling them as normally distributed with a standard deviation of 1/4 voxel size. In the FVE-based method, the WSS was obtained by first estimating the intravoxel velocity profile via a simulated FVE measurement and then computing the spatial velocity derivative near the wall. Note that the FVE-method uses larger voxels.