Robust universal nonrigid motion correction framework for first‐pass cardiac MR perfusion imaging. Issue 4 (15th February 2017)

• Record Type: Journal Article
• Authors: Benovoy, Mitchel; Jacobs, Matthew; Cheriet, Farida; Dahdah, Nagib; Arai, Andrew E.; Hsu, Li‐Yueh
• Is Part Of: Journal of magnetic resonance imaging. Volume 46:Issue 4(2017)
• Page Start: 1060
• Page End: 1072
• Publication Date: 2017-02-15
• Subjects: cardiac magnetic resonance -- myocardial perfusion -- contrast enhancement -- quantitative perfusion -- motion correction -- nonrigid image registration; Magnetic resonance imaging -- Periodicals; 616
• DOI: 10.1002/jmri.25659
• Languages: English
• ISSNs: 1053-1807

Abstract

Abstract : Purpose: To present and assess an automatic nonrigid image registration framework that compensates motion in cardiac magnetic resonance imaging (MRI) perfusion series and auxiliary images acquired under a wide range of conditions to facilitate myocardial perfusion quantification. Materials and Methods: Our framework combines discrete feature matching for large displacement estimation with a dense variational optical flow formulation in a multithreaded architecture. This framework was evaluated on 291 clinical subjects to register 1.5T and 3.0T steady‐state free‐precession (FISP) and fast low‐angle shot (FLASH) dynamic contrast myocardial perfusion images, arterial input function (AIF) images, and proton density (PD)‐weighted images acquired under breath‐hold (BH) and free‐breath (FB) settings. Results: Our method significantly improved frame‐to‐frame appearance consistency compared to raw series, expressed in correlation coefficient (R 2 = 0.996 ± 3.735E‐3 vs. 0.978 ± 2.024E‐2, P < 0.0001) and mutual information (3.823 ± 4.098E‐1 vs. 2.967 ± 4.697E‐1, P < 0.0001). It is applicable to both BH (R 2 = 0.998 ± 3.217E‐3 vs. 0.990 ± 7.527E‐3) and FB (R 2 = 0.995 ± 3.410E‐3 vs. 0.968 ± 2.257E‐3) paradigms as well as FISP and FLASH sequences. The method registers PD images to perfusion T 1 series (9.70% max increase in R 2 vs. no registration, P < 0.001) and also corrects motion in low‐resolution AIF series (R 2 = 0.987 ± 1.180E‐2 vs. 0.964 ± 3.860E‐2, P < 0.001). Finally, we showed the myocardial perfusion contrast dynamic was preserved in the motion‐corrected images compared to the raw series (R 2 = 0.995 ± 6.420E‐3). Conclusion: The critical step of motion correction prior to pixel‐wise cardiac MR perfusion quantification can be performed with the proposed universal system. It is applicable to a wide range of perfusion series and auxiliary images with different acquisition settings. Level of Evidence: 3 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2017;46:1060–1072.