Toward non-clinical validation of fractional flow reserve simulation software: A generic coronary artery geometry.

Medical image-based diagnostic techniques have become increasingly common in the clinic. Estimating fractional flow reserve in coronary stenoses from medical image data is among the most prominent examples. The modeling techniques used in these clinical tools require rigorous experimental validation yet there is currently no standardized, public toolset to help assess model credibility.

Quiescent frame, contrast-enhanced coronary magnetic resonance angiography reconstructed using limited number of physiologic frames from 5D free-running acquisitions.

Background: 5D, free-running imaging resolves sets of 3D whole-heart images in both cardiac and respiratory dimensions. In an application such as coronary imaging when a single, static image is of interest, computationally expensive offline iterative reconstruction is still needed to compute the multiple 3D datasets.

Purpose: Evaluate how the number of physiologic bins included in the reconstruction affects the computational cost and resulting image quality of a single, static volume reconstruction.

Effect of Patient-Specific Coronary Flow Reserve Values on the Accuracy of MRI-Based Virtual Fractional Flow Reserve.

The purpose of this study is to investigate the effect of varying coronary flow reserve (CFR) values on the calculation of computationally-derived fractional flow reserve (FFR). CFR reflects both vessel resistance due to an epicardial stenosis, and resistance in the distal microvascular tissue. Patients may have a wide range of CFR related to the tissue substrate that is independent of epicardial stenosis levels.

In Vivo Quantification of Regional Circumferential Green Strain in the Thoracic and Abdominal Aorta by Two-Dimensional Spiral Cine DENSE MRI.

Regional tissue mechanics play a fundamental role in the patient-specific function and remodeling of the cardiovascular system. Nevertheless, regional in vivo assessments of aortic kinematics remain lacking due to the challenge of imaging the thin aortic wall. Herein, we present a novel application of displacement encoding with stimulated echoes (DENSE) magnetic resonance imaging (MRI) to quantify the regional displacement and circumferential Green strain of the thoracic and abdominal aorta.