Robust universal nonrigid motion correction framework for first‐pass cardiac MR perfusion imaging

• Published in: Journal of magnetic resonance imaging Vol. 46; no. 4; pp. 1060 - 1072
• Main Authors: Benovoy, Mitchel, Jacobs, Matthew, Cheriet, Farida, Dahdah, Nagib, Arai, Andrew E., Hsu, Li‐Yueh
• Format: Journal Article
• Language: English
• Published: United States Wiley Subscription Services, Inc 01.10.2017
• Subjects: Breath Holding; cardiac magnetic resonance; contrast enhancement; Contrast Media; Correlation coefficient; Correlation coefficients; Heart; Heart - diagnostic imaging; Heart diseases; Humans; Image acquisition; Image contrast; Image Enhancement - methods; Image Processing, Computer-Assisted - methods; Image registration; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Motion; motion correction; myocardial perfusion; Myocardial Perfusion Imaging - methods; NMR; nonrigid image registration; Nuclear magnetic resonance; Optical flow (image analysis); Perfusion; Precession; Proton density (concentration); quantitative perfusion; Respiration; Sequences; myocardial perfusion; motion correction; nonrigid image registration; quantitative perfusion; cardiac magnetic resonance; contrast enhancement
• ISSN: 1053-1807; 1522-2586
• DOI: 10.1002/jmri.25659

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 (R2 = 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 (R2 = 0.998 ± 3.217E‐3 vs. 0.990 ± 7.527E‐3) and FB (R2 = 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 T1 series (9.70% max increase in R2 vs. no registration, P < 0.001) and also corrects motion in low‐resolution AIF series (R2 = 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 (R2 = 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.