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3D Whole Heart CSPAMM tagging in a single breath hold using k-t-PCA

Introduction Myocardial tagging techniques [1, 2] have shown great promise in assessing local wall motion abnormalities. Recent work has indicated the value of the method in stratifying patients for cardiac resynchronization therapy [3]. Three-dimensional data acquisition is a prerequisite for a comprehensive analysis of cardiac motion. The combination of reduced field-of-view and line tag preparation has permitted whole-heart 3D tagging in three consecutive breathholds in both healthy subjects and patients with infarcts [4]. In order to improve the robustness and simplify the protocols for 3D whole-heart tagging further, additional reductions in scan time are desired. Ultimately, data acquisition in a single breathhold is preferred removing any inconsistencies due to separate breathholds and varying heart rates. In the present work, we investigate the benefits of the k-t PCA (principle component analysis) framework [5] for accelerating 3D tagging data acquisition. Using in vivo whole-heart 3D CSPAMM data it is demonstrated that up to 3.7-fold net scan accelerations can be achieved without compromising the accuracy of motion quantification. Methods Whole-heart 3D CSPAMM tagging data were obtained in healthy subjects using a Philips 1.5T clinical MR system (Philips Healthcare, Best, The Netherlands) with following imaging parameters: spatial resolution 3.8×7×7mm, 14 slices, TR/TE = 7.0/3.2ms, 23 heart phases and 7mm tag-line spacing. In order to assess reconstruction accuracy, fully sampled data were acquired in three consecutive breathholds of 14 sec each. Pencil beam navigators were employed to ensure consistency among breathholds [4]. In order to simulate accelerated data acquisition, data were decimated using optimized sheared grid sampling patterns with undersampling factors of 5 and 8. Low-resolution training data were derived from an elliptical central area in ky-kz space with different total numbers of profiles ranging from 21-51. Accordingly, net reduction factors between 2.7 and 3.7 were achieved. In image reconstruction, the training data representation in the spatial temporal frequency domain P(x,f) was decomposed into temporal basis functions B(f) corresponding to the principle components (pc) and spatially dependent weighting coefficients W(x) according to: ( , ) ( ) ( ) P x f W x B f =