An optimization framework to maximize signal-to-noise ratio in simultaneous multi-slice body imaging

Parallel imaging is essential for the acceleration of abdominal and pelvic 2D multi-slice imaging, in order to reduce scan time and mitigate motion artifacts. Controlled Aliasing In Parallel Imaging Results IN Higher Acceleration (CAIPIRINHA) accelerated imaging has been shown to increase the signal-to-noise ratio (SNR) significantly compared with in-plane parallel imaging with similar acceleration. We hypothesize that for CAIPIRINHA-accelerated abdominal imaging the consistency of image quality and SNR is more difficult to achieve due to the subject-specific coil sensitivity profiles, caused by (1) flexible coil placement; (2) variations in anatomy; and (3) variations in scan coverage along the superior-inferior direction. To test this, a mathematical framework is introduced that calculates the (retained) SNR for in-plane and simultaneous multi-slice (SMS)-accelerated acquisitions. Moreover, this framework was used to optimize the sampling pattern by maximizing the local SNR within a region of interest (ROI) through non-linear, RF-induced CAIPIRINHA slice shifts. The framework was evaluated on 14 healthy subjects and the optimized sampling pattern was compared with in-plane acceleration and CAIPIRINHA acceleration with linear slice shifts, which are primarily used in brain imaging. We demonstrate that the field of view (FOV) in the superior-inferior direction, the coil positioning and the individual anatomy have a large impact on the image SNR (changes up to 50% for varying coil positions and 40% differences between subjects) and image artifacts for simultaneous multi-slice acceleration. Consequently, sampling patterns have to be optimized for acquisitions employing different FOVs and ideally on an individual basis. Optimization of the sampling pattern, which exploits non-linear shifts between slices, showed a considerable SNR increase (10-30%) for higher acceleration factors. The framework outlined in this article can be used to optimize sampling patterns for a broad range of accelerated body acquisitions on an individual basis.