Automatic in‐line quantitative myocardial perfusion mapping: Processing algorithm and implementation

Purpose Quantitative myocardial perfusion mapping has advantages over qualitative assessment, including the ability to detect global flow reduction. However, it is not clinically available and remains a research tool. Building upon the previously described imaging sequence, this study presents algor...

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Veröffentlicht in:Magnetic resonance in medicine Jg. 83; H. 2; S. 712 - 730
Hauptverfasser: Xue, Hui, Brown, Louise A.E., Nielles‐Vallespin, Sonia, Plein, Sven, Kellman, Peter
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States Wiley Subscription Services, Inc 01.02.2020
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ISSN:0740-3194, 1522-2594, 1522-2594
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Zusammenfassung:Purpose Quantitative myocardial perfusion mapping has advantages over qualitative assessment, including the ability to detect global flow reduction. However, it is not clinically available and remains a research tool. Building upon the previously described imaging sequence, this study presents algorithm and implementation of an automated solution for inline perfusion flow mapping with step by step performance characterization. Methods Proposed workflow consists of motion correction (MOCO), arterial input function blood detection, intensity to gadolinium concentration conversion, and pixel‐wise mapping. A distributed kinetics model, blood‐tissue exchange model, is implemented, computing pixel‐wise maps of myocardial blood flow (mL/min/g), permeability‐surface‐area product (mL/min/g), blood volume (mL/g), and interstitial volume (mL/g). Results Thirty healthy subjects (11 men; 26.4 ± 10.4 years) were recruited and underwent adenosine stress perfusion cardiovascular MR. Mean MOCO quality score was 3.6 ± 0.4 for stress and 3.7 ± 0.4 for rest. Myocardial Dice similarity coefficients after MOCO were significantly improved (P < 1e‐6), 0.87 ± 0.05 for stress and 0.86 ± 0.06 for rest. Arterial input function peak gadolinium concentration was 4.4 ± 1.3 mmol/L at stress and 5.2 ± 1.5 mmol/L at rest. Mean myocardial blood flow at stress and rest were 2.82 ± 0.47 mL/min/g and 0.68 ± 0.16 mL/min/g, respectively. The permeability‐surface‐area product was 1.32 ± 0.26 mL/min/g at stress and 1.09 ± 0.21 mL/min/g at rest (P < 1e‐3). Blood volume was 12.0 ± 0.8 mL/100 g at stress and 9.7 ± 1.0 mL/100 g at rest (P < 1e‐9), indicating good adenosine vasodilation response. Interstitial volume was 20.8 ± 2.5 mL/100 g at stress and 20.3 ± 2.9 mL/100 g at rest (P = 0.50). Conclusions An inline perfusion flow mapping workflow is proposed and demonstrated on normal volunteers. Initial evaluation demonstrates this fully automated solution for the respiratory MOCO, arterial input function left ventricle mask detection, and pixel‐wise mapping, from free‐breathing myocardial perfusion imaging.
Bibliographie:Funding information
Supported by the National Heart, Lung and Blood Institute, National Institutes of Health.
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Authors' contributions: H.X. and P.K. conceived of the study and developed the algorithms, implemented the inline reconstruction and processing software, performed processing and analysis, and drafted the manuscript. P.K., H.X., and S.N.V. developed the sequence. L.B. and S.P. acquired normal volunteer data. All authors participated in revising the manuscript and read and approved the final manuscript.
ISSN:0740-3194
1522-2594
1522-2594
DOI:10.1002/mrm.27954