Adaptive registration of varying contrast-weighted images for improved tissue characterization (ARCTIC): application to T1 mapping.

Abstract

To propose and evaluate a novel nonrigid image registration approach for improved myocardial T1 mapping. Myocardial motion is estimated as global affine motion refined by a novel local nonrigid motion estimation algorithm. A variational framework is proposed, which simultaneously estimates motion field and intensity variations, and uses an additional regularization term to constrain the deformation field using automatic feature tracking. The method was evaluated in 29 patients by measuring the DICE similarity coefficient and the myocardial boundary error in short axis and four chamber data. Each image series was visually assessed as "no motion" or "with motion." Overall T1 map quality and motion artifacts were assessed in the 85 T1 maps acquired in short axis view using a 4-point scale (1-nondiagnostic/severe motion artifact, 4-excellent/no motion artifact). Increased DICE similarity coefficient (0.78 ± 0.14 to 0.87 ± 0.03, P < 0.001), reduced myocardial boundary error (1.29 ± 0.72 mm to 0.84 ± 0.20 mm, P < 0.001), improved overall T1 map quality (2.86 ± 1.04 to 3.49 ± 0.77, P < 0.001), and reduced T1 map motion artifacts (2.51 ± 0.84 to 3.61 ± 0.64, P < 0.001) were obtained after motion correction of "with motion" data (∼56% of data). The proposed nonrigid registration approach reduces the respiratory-induced motion that occurs during breath-hold T1 mapping, and significantly improves T1 map quality.