Volume 17 Supplement 1
Fusion of 3D-CMR-perfusion with 3D-MR-coronary angiography
© Gotschy et al; licensee BioMed Central Ltd. 2015
Published: 3 February 2015
The most relevant parameters for the assessment of coronary artery disease (CAD) are myocardial perfusion and the status of the coronary arteries. It has been shown that hybrid imaging strategies to acquire both parameters such as SPECT with CT-angiography provide an added value for clinical decision making in the treatment of CAD1. However, SPECT and CT expose the patient to ionizing radiation and, in large prospective trials, SPECT showed inferior sensitivity to detect CAD when compared with CMR-perfusion2. Therefore, the aim of this study was to investigate the potential additive value of 3D-MR coronary angiography (MRCA) on a 3D-CMR perfusion protocol.
In this study, eleven patients with suspected CAD were scheduled for an invasive X-ray coronary angiography (XA) and a CMR examination. The CMR protocol consisted of adenosine stress-rest 3D-CMR perfusion, late gadolinium enhancement (LGE) and MRCA examinations. All examinations were performed on a 3T clinical scanner. In XA, coronary stenosis ≥50% was classified as significant. In the 3D-CMR perfusion scans, the myocardial ischemic burden (MIB) was measured by determining hypoperfused areas which were not scar tissue as determined from LGE images and normalized to left-ventricular myocardial volume. MRCA scans were evaluated by an experienced reader and each vessel was graded as no/low-grade stenosis or significant stenosis. Additional 3D reconstruction and fusion of 3D-MRCA and 3D-CMR perfusion was performed.
For the assessment of CAD, combined evaluation of 3D-MRCA with 3D-CMR perfusion had superior sensitivity at the cost of a loss in specificity when compared to CMR-MIB/LGE alone in a vessel-based approach. The additional fusion of both modalities can provide information on the position of the coronary arteries to correlate coronary stenoses with non-distinct perfusion deficits.
The authors acknowledge support from the Swiss National Science Foundation, Bayer Healthcare and Philips Healthcare.
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.