Sub-segmental quantification of single (stress)-pass perfusion CMR improves the diagnostic accuracy for detection of obstructive coronary artery disease

  • Background: Myocardial perfusion with cardiovascular magnetic resonance (CMR) imaging is an established diagnostic test for evaluation of myocardial ischaemia. For quantification purposes, the 16 segment American Heart Association (AHA) model poses limitations in terms of extracting relevant information on the extent/severity of ischaemia as perfusion deficits will not always fall within an individual segment, which reduces its diagnostic value, and makes an accurate assessment of outcome data or a result comparison across various studies difficult. We hypothesised that division of the myocardial segments into epi- and endocardial layers and a further circumferential subdivision, resulting in a total of 96 segments, would improve the accuracy of detecting myocardial hypoperfusion. Higher (sub-)subsegmental recording of perfusion abnormalities, which are defined relatively to the normal reference using the subsegment with the highest value, may improve the spatial encoding of myocardial blood flow, based on a single stress perfusion acquisition. Objective: A proof of concept comparison study of subsegmentation approaches based on transmural segments (16 AHA and 48 segments) vs. subdivision into epi- and endocardial (32) subsegments vs. further circumferential subdivision into 96 (sub-)subsegments for diagnostic accuracy against invasively defined obstructive coronary artery disease (CAD). Methods: Thirty patients with obstructive CAD and 20 healthy controls underwent perfusion stress CMR imaging at 3 T during maximal adenosine vasodilation and a dual bolus injection of 0.1mmol/kg gadobutrol. Using Fermi deconvolution for blood flow estimation, (sub-)subsegmental values were expressed relative to the (sub)subsegment with the highest flow. In addition, endo−/epicardial flow ratios were calculated based on 32 and 96 (sub-)subsegments. A receiver operating characteristics (ROC) curve analysis was performed to compare the diagnostic performance of discrimination between patients with CAD and healthy controls. Observer reproducibility was assessed using Bland-Altman approaches. Results: Subdivision into more and smaller segments revealed greater accuracy for #32, #48 and # 96 compared to the standard #16 approach (area under the curve (AUC): 0.937, 0.973 and 0.993 vs 0.820, p<0.05). The #96-based endo−/epicardial ratio was superior to the #32 endo−/epicardial ratio (AUC 0.979, vs. 0.932, p<0.05). Measurements for the #16 model showed marginally better reproducibility compared to #32, #48 and #96 (mean difference± standard deviation: 2.0±3.6 vs. 2.3±4.0 vs 2.5±4.4 vs. 4.1±5.6). Conclusions: Subsegmentation of the myocardium improves diagnostic accuracy and facilitates an objective cutoff-based description of hypoperfusion, and facilitates an objective description of hypoperfusion, including the extent and severity of myocardial ischaemia. Quantification based on a single (stress-only) pass reduces the overall amount of gadolinium contrast agent required and the length of the overall diagnostic study.

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Author:Melanie T. P. Le, Niloufar Zarinabad, Tommaso D’AngeloORCiD, Ibnul Mia, Robert Heinke, Thomas J. VoglORCiDGND, Andreas M. ZeiherORCiDGND, Eike NagelORCiDGND, Valentina O. PuntmannORCiD
URN:urn:nbn:de:hebis:30:3-550499
DOI:https://doi.org/10.1186/s12968-020-0600-1
ISSN:1532-429X
Parent Title (English):Journal of cardiovascular magnetic resonance
Publisher:BioMed Central
Place of publication:London
Document Type:Article
Language:English
Date of Publication (online):2020/02/06
Date of first Publication:2020/02/06
Publishing Institution:Universitätsbibliothek Johann Christian Senckenberg
Release Date:2020/06/23
Tag:16 segment AHA model; Cardiovascular magnetic resonance; Myocardial perfusion; Myocardial segmentation; Sub-segmentation
Volume:22
Issue:14
Page Number:11
First Page:1
Last Page:11
Note:
© The Author(s). 2020 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. 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.
HeBIS-PPN:467256675
Institutes:Medizin / Medizin
Dewey Decimal Classification:6 Technik, Medizin, angewandte Wissenschaften / 61 Medizin und Gesundheit / 610 Medizin und Gesundheit
Sammlungen:Universitätspublikationen
Open-Access-Publikationsfonds:Medizin
Licence (German):License LogoCreative Commons - Namensnennung 4.0