VESSEL CENTERLINE TRACKING AND BOUNDARY SEGMENTATION IN CORONARY MRA WITH MINIMAL MANUAL INTERACTION

Volumetric coronary endothelial function assessment: a feasibility study exploiting stack-of-stars 3D cine MRI and image-based respiratory self-gating

Abnormal coronary endothelial function (CEF), manifesting as depressed vasoreactive responses to endothelial-specific stressors, occurs early in atherosclerosis, independently predicts cardiovascular events, and responds to cardioprotective interventions. CEF is spatially heterogeneous along a coronary artery in patients with atherosclerosis, and thus recently developed and tested non-invasive 2D MRI techniques to measure CEF may not capture the extent of changes in CEF in a given coronary artery. The purpose of this study was to develop and test the first volumetric coronary 3D MRI cine method for assessing CEF along the proximal and mid-coronary arteries with isotropic spatial resolution and in free-breathing. This approach, called 3D-Stars, combines a 6 min continuous, untriggered golden-angle stack-of-stars acquisition with a novel image-based respiratory self-gating method and cardiac and respiratory motion-resolved reconstruction. The proposed respiratory self-gating method agreed well with respiratory bellows and center-of-k-space methods. In healthy subjects, 3D-Stars vessel sharpness was non-significantly different from that by conventional 2D radial in proximal segments, albeit lower in mid-portions. Importantly, 3D-Stars detected normal vasodilatation of the right coronary artery in response to endothelial-dependent isometric handgrip stress in healthy subjects. Coronary artery cross-sectional areas measured using 3D-Stars were similar to those from 2D radial MRI when similar thresholding was used. In conclusion, 3D-Stars offers good image quality and shows feasibility for non-invasively studying vasoreactivity-related lumen area changes along the proximal coronary artery in 3D during free-breathing.

Non-contrast coronary magnetic resonance angiography: current frontiers and future horizons

Coronary magnetic resonance angiography (coronary MRA) is advantageous in its ability to assess coronary artery morphology and function without ionizing radiation or contrast media. However, technical limitations including reduced spatial resolution, long acquisition times, and low signal-to-noise ratios prevent it from clinical routine utilization. Nonetheless, each of these limitations can be specifically addressed by a combination of novel technologies including super-resolution imaging, compressed sensing, and deep-learning reconstruction. In this paper, we first review the current clinical use and motivations for non-contrast coronary MRA, discuss currently available coronary MRA techniques, and highlight current technical developments that hold unique potential to optimize coronary MRA image acquisition and post-processing. In the final section, we examine the various research-based coronary MRA methods and metrics that can be leveraged to assess coronary stenosis severity, physiological function, and atherosclerotic plaque characterization. We specifically discuss how such technologies may contribute to the clinical translation of coronary MRA into a robust modality for routine clinical use.

Two-Steps Coronary Artery Segmentation Algorithm Based on Improved Level Set Model in Combination with Weighted Shape-Prior Constraints

Due to the complex topological structure of the coronary artery and the uneven distribution of the contrast agent, the angiography images are inevitably blurred and has low contrast, which causes great difficulty in process of segmentation. For this problem, a two-steps segmentation algorithm based on Hessian matrix and level set is proposed in this paper. Firstly, potential blood vessels of coronary images are preliminary extracted via Hessian matrix eigenvalues feature vectors of the geometric features and the response function. Then a novel regularization and area constraint is introduced into the local data energy fitting functional. Finally, the precision of Coronary Artery image is obtained in the evolution of the level set function. Experiments show that our proposed algorithm has better performance to these comparison segmentation algorithms.

Spatially selective implementation of the adiabatic T2Prep sequence for magnetic resonance angiography of the coronary arteries

In coronary magnetic resonance angiography, a magnetization-preparation scheme for T2-weighting (T2Prep) is widely used to enhance contrast between the coronary blood-pool and the myocardium. This prepulse is commonly applied without spatial selection to minimize flow sensitivity, but the nonselective implementation results in a reduced magnetization of the in-flowing blood and a related penalty in signal-to-noise ratio. It is hypothesized that a spatially selective T2Prep would leave the magnetization of blood outside the T2Prep volume unaffected and thereby lower the signal-to-noise ratio penalty. To test this hypothesis, a spatially selective T2Prep was implemented where the user could freely adjust angulation and position of the T2Prep slab to avoid covering the ventricular blood-pool and saturating the in-flowing spins. A time gap of 150 ms was further added between the T2Prep and other prepulses to allow for in-flow of a larger volume of unsaturated spins. Consistent with numerical simulation, the spatially selective T2Prep increased in vivo human coronary artery signal-to-noise ratio (42.3 ± 2.9 vs. 31.4 ± 2.2, n = 22, P < 0.0001) and contrast-to-noise-ratio (18.6 ± 1.5 vs. 13.9 ± 1.2, P = 0.009) as compared to those of the nonselective T2Prep. Additionally, a segmental analysis demonstrated that the spatially selective T2Prep was most beneficial in proximal and mid segments where the in-flowing blood volume was largest compared to the distal segments.