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Integrated motion correction and dictionary learning for free‐breathing myocardial T 1 mapping
Author(s) -
Zhu Yanjie,
Kang Jinkyu,
Duan Chong,
Nezafat Maryam,
Neisius Ulf,
Jang Jihye,
Nezafat Reza
Publication year - 2019
Publication title -
magnetic resonance in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.696
H-Index - 225
eISSN - 1522-2594
pISSN - 0740-3194
DOI - 10.1002/mrm.27579
Subject(s) - undersampling , imaging phantom , artificial intelligence , compressed sensing , computer science , motion (physics) , reduction (mathematics) , mathematics , pattern recognition (psychology) , nuclear medicine , medicine , geometry
Purpose To develop and evaluate an integrated motion correction and dictionary learning (MoDic) technique to accelerate data acquisition for myocardial T 1 mapping with improved accuracy. Methods MoDic integrates motion correction with dictionary learning–based reconstruction. A random undersampling scheme was implemented for slice‐interleaved T 1 mapping sequence to allow prospective undersampled data acquisition. Phantom experiments were performed to evaluate the effect of reconstruction on T 1 measurement. In vivo T 1 mappings were acquired in 8 healthy subjects using 6 different acceleration approaches: uniform or randomly undersampled k‐space data with reduction factors (R) of 2, 3, and 4. Uniform undersampled data were reconstructed with SENSE, and randomly undersampled k‐space data were reconstructed using dictionary learning, compressed sensing SENSE, and MoDic methods. Three expert readers subjectively evaluated the quality of T 1 maps using a 4‐point scoring system. The agreement between T 1 values was assessed by Bland‐Altman analysis. Results In the phantom study, the accuracy of T 1 measurements improved with increasing reduction factors ( - 31 ± 35 ms, - 13 ± 18 ms, and - 5 ± 11 ms for reduction factor (R) = 2 to 4, respectively). The image quality of in vivo T 1 maps assessed by subjective scoring using MoDic was similar to that of SENSE at R = 2 ( P = .61) but improved at R = 3 and 4 ( P < .01). The scores of dictionary learning (2.98 ± 0.71, 2.91 ± 0.60, and 2.67 ± 0.71 for R = 2 to 4) and CS‐SENSE (3.32 ± 0.42, 3.05 ± 0.43, and 2.53 ± 0.43) were lower than those of MoDic (3.48 ± 0.46, 3.38 ± 0.52, and 2.9 ± 0.60) for all reduction factors ( P < .05 for all). Conclusion The MoDic method accelerates data acquisition for myocardial T 1 mapping with improved T 1 measurement accuracy.