Quantification of myocardial T1 relaxation has potential value in the diagnosis of both ischemic and non-ischemic cardiomyopathies. Image acquisition using the Modified Look-Locker Inversion Recovery technique is clinically feasible for T1 mapping. However, respiratory motion limits its applicability and degrades the accuracy of T1 estimation. The robust registration of acquired inversion recovery images is particularly challenging due to the large changes in image contrast, especially for those images acquired near the signal null point of the inversion recovery and other inversion times for which there is little tissue contrast. In this paper, we propose a novel motion correction algorithm. This approach is based on estimating synthetic images presenting contrast changes similar to the acquired images. The estimation of synthetic images is formulated as a variational energy minimization problem. Validation on a consecutive patient data cohort shows that this strategy can perform robust non-rigid registration to align inversion recovery images experiencing significant motion and lead to suppression of motion induced artifacts in the T1 map.

The assessment of myocardial fibrosis and extra-cellular volume requires accurate estimation of myocardial T1s. While image acquisition using the Modified Look-Locker Inversion Recovery technique is clinically feasible for myocardial T1 mapping, respiratory motion can limit its applicability. Moreover, the conventional T1 fitting approach using the magnitude inversion recovery images can lead to less stable T1 estimates and increased computational cost. In this paper, we propose a novel T1 mapping scheme which is based on phase sensitive image reconstruction and the restoration of polarity of the MR signal after inversion. The motion correction is achieved by registering the reconstructed images after background phase removal. The restored signal polarity of the inversion recovery signal helps the T1 fitting resulting in an improved quality of the T1 map and reducing the computational cost. Quantitative validation on a data cohort of 45 patients proves the robustness of the proposed method against varying image contrast. Compared to the magnitude T1 fitting, the proposed phase sensitive method leads to less fluctuation in T1 estimates.

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Quantitative T2 mapping was recently shown to be superior to T2-weighted imaging in detecting T2 changes across myocardium. Pixel-wise T2 mapping is sensitive to misregistration between the images used to generate the parameter map. In this study, utility of two motion-compensation strategies—(i) navigator gating with prospective slice correction and (ii) nonrigid registration—was investigated for myocardial T2 mapping in short axis and horizontal long axis views. Navigator gating provides respiratory motion compensation, whereas registration corrects for residual cardiac and respiratory motion between images; thus, the two strategies provided complementary functions. When these were combined, respiratory-motion-induced T2 variability, as measured by both standard deviation and interquartile range, was comparable to that in breath-hold T2 maps. In normal subjects, this combined motion-compensation strategy increased the percentage of myocardium with T2 measured to be within normal range from 60.1% to 92.2% in short axis and 62.3% to 92.7% in horizontal long axis. The new motion-compensated T2 mapping technique, which combines navigator gating, prospective slice correction, and nonrigid registration to provide through-plane and in-plane motion correction, enables a method for fully automatic and robust free-breathing T2 mapping.

Abstract. Conventional cardiac MRI acquisition involves a multi-step approach, requiring a few double-oblique localizers in order to locate the heart and prescribe long-and short-axis views of the heart. This approach is operator-dependent and time-consuming. We propose a new approach to automating and accelerating the acquisition process to improve the clinical workflow. We capture a highly accelerated static 3D full-chest volume through parallel imaging within one breath-hold. The left ventricle is localized and segmented, including left ventricle outflow tract. A number of cardiac landmarks are then detected to anchor the cardiac chambers and calculate standard 2-, 3-, and 4-chamber long-axis views along with a short-axis stack. Learning-based algorithms are applied to anatomy segmentation and anchor detection. The proposed algorithm is evaluated on 173 localizer acquisitions. The entire view planning is fully automatic and takes less than 10 seconds in our experiments.