In myocardial T 1 mapping, undesirable motion poses significant challenges because uncorrected motion can affect T 1 estimation accuracy and cause incorrect diagnosis.In this study, we propose and evaluate a motion correction method for myocardial T 1 mapping using self-supervised deep learning based registration with contrast separation (SDRAP). A sparse coding based method was first proposed to separate the contrast component from T 1 -weighted (T1w) images. Then, a self-supervised deep neural network with cross-correlation (SDRAP-CC) or mutual information as the registration similarity measurement was developed to register contrast separated images, after which signal fitting was performed on the motion corrected T1w images to generate motion corrected T 1 maps. The registration network was trained and tested in 80 healthy volunteers with images acquired using the modified Look-Locker inversion recovery (MOLLI) sequence. The proposed SDRAP was compared with the free form deformation (FFD) registration method regarding (1) Dice similarity coefficient (DSC) and mean boundary error (MBE) of myocardium contours, (2) T 1 value and standard deviation (SD) of T 1 fitting, (3) subjective evaluation score for overall image quality and motion artifact level, and (4) computation time. Results showed that SDRAP-CC achieved the highest DSC of 85.0 ± 3.9% and the lowest MBE of 0.92 ± 0.25 mm among the methods compared. Additionally, SDRAP-CC performed the best by resulting in lower SD value (28.1 ± 17.6 ms) and higher subjective image quality scores (3.30 ± 0.79 for overall quality and 3.53 ± 0.68 for motion artifact) evaluated by a cardiologist. The proposed SDRAP took only 0.52 s to register one slice of MOLLI images, achieving about sevenfold acceleration over FFD (3.7 s/slice).
The purpose of the current study was to develop and validate a three-dimensional (3D) free-breathing cardiac T 1 -mapping sequence using SAturation-recovery and Variable-flip-Angle (SAVA). SAVA sequentially acquires multiple electrocardiogramtriggered volumes using a multishot spoiled gradient-echo sequence. The first volume samples the equilibrium signal of the longitudinal magnetization, where a flip angle of 2 is used to reduce the time for the magnetization to return to equilibrium. The succeeding three volumes are saturation prepared with variable delays, and are acquired using a 15 flip angle to maintain the signal-to-noise ratio. A diaphragmatic navigator is used to compensate the respiratory motion. T 1 is calculated using a saturation-recovery model that accounts for the flip angle. We validated SAVA by simulations, phantom, and human subject experiments at 3 T. SAVA was compared with modified Look-Locker inversion recovery (MOLLI) and saturation-recovery single-shot acquisition (SASHA) in vivo. In phantoms, T 1 by SAVA had good agreement with the reference (R 2 = 0.99). In vivo 3D T 1 mapping by SAVA could achieve an imaging resolution of 1.25 Â 1.25 Â 8 mm 3 . Both global and septal T 1 values by SAVA (1347 ± 37 and 1332 ± 42 ms) were in between those by SASHA (1612 ± 63 and 1618 ± 51 ms) and MOLLI (1143 ± 59 and 1188 ± 65 ms). According to the standard deviation (SD) and coefficient of variation (CV), T 1 precision measured by SAVA
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