3D self-gated cardiac cine imaging at 3 Tesla using stack-of-stars bSSFP with tiny golden angles and compressed sensing

PURPOSE

To develop and evaluate an accelerated 3D self-gated cardiac cine imaging technique at 3 Tesla without the use of external electrocardiogram triggering or respiratory gating.

METHODS

A 3D stack-of-stars balanced steady-state free precession sequence with a tiny golden angle sampling scheme was developed to reduced eddy current effect-related artefacts at 3 Tesla. Respiratory and cardiac motion were derived from a central 5-point self-gating signal extraction approach. The data acquired around the end-expiration phases were then sorted into individual cardiac bins and used for reconstruction with compressed sensing. To evaluate the performance of the proposed method, image quality (1: the best; 4: the worst) was quantitatively compared using both the proposed method and the conventional 3D golden-angle self-gated method. Linear regression and Bland-Altman analysis were used to assess the functional measurements agreement between the proposed method and the routine 2D breath-hold multi-slice technique.

RESULTS

Compared to the conventional 3D golden-angle self-gated method, the proposed method yielded images with much less streaking artifact and higher myocardium edge sharpness (0.50 ± 0.06 vs. 0.45 ± 0.05, P = 0.004). The proposed method provided an inferior image quality score to the routine 2D technique (2.13 ± 0.35 vs. 1.38 ± 0.52, P = 0.063) but a superior one to the conventional self-gated method (2.13 ± 0.35 vs. 3.13 ± 0.64, P = 0.031). Left ventricular functional measurements between the proposed method and routine 2D technique were all well in agreement.

CONCLUSION

This study presents a novel self-gating approach to realize rapid 3D cardiac cine imaging at 3 Tesla.

Accelerating PS model-based dynamic cardiac MRI using compressed sensing

High spatiotemporal resolution MRI is a challenging topic in dynamic MRI field. Partial separability (PS) model has been successfully applied to dynamic cardiac MRI by exploiting data redundancy. However, the model requires substantial preprocessing data to accurately estimate the model parameters before image reconstruction. Since compressed sensing (CS) is a potential technique to accelerate MRI by reducing the number of acquired data, the combination of PS and CS, named as Stepped-SparsePS, was introduced to accelerate the preprocessing data acquisition of PS in this work. The proposed Stepped-SparsePS method sequentially reconstructs a set of aliased dynamic images in each channel based on PS model and then the final dynamic images from the aliased images using CS. The results from numerical simulations and in vivo experiments demonstrate that Stepped-SparsePS could significantly reduce data acquisition time while preserving high spatiotemporal resolution.