Fast Calculation Method of Average g-Factor for Wave-CAIPI Imaging

Wave-CAIPI MR imaging is a 3D imaging technique which can uniformize the g-factor maps and significantly reduce g-factor penalty at high acceleration factors. But it is time-consuming to calculate the average g-factor penalty for optimizing the parameters of Wave-CAIPI. In this paper, we propose a novel fast calculation method to calculate the average g-factor in Wave-CAIPI imaging. Wherein, the g-factor value in the arbitrary (e.g. the central) position is separately calculated and then approximated to the average g-factor using Taylor linear approximation. The verification experiments have demonstrated that the average g-factors of Wave-CAIPI imaging which are calculated by the proposed method is consistent with the previous time-consuming theoretical calculation method and the conventional pseudo multiple replica method. Comparison experiments show that the proposed method is averagely about 1000 times faster than the previous theoretical calculation method and about 1700 times faster than the conventional pseudo multiple replica method.

💡 Research Summary

The paper addresses a critical bottleneck in the optimization of Wave‑CAIPI magnetic resonance imaging (MRI), namely the computationally intensive evaluation of the average g‑factor, which quantifies noise amplification in parallel imaging. Wave‑CAIPI combines two‑dimensional CAIPIRINHA sampling with Bunched Phase Encoding (BPE) to exploit coil sensitivity variations along the phase‑encoding, slice, and readout directions, thereby achieving near‑unity g‑factor maps even at high acceleration factors. However, existing methods for estimating the average g‑factor—direct theoretical calculation involving large matrix inversions and the pseudo‑multiple‑replica (PMR) approach that simulates many noisy reconstructions—are prohibitively slow for routine parameter sweeps.

The authors propose a novel, fast approximation method. The key insight is that, because Wave‑CAIPI distributes aliasing uniformly and leverages coil sensitivities in three dimensions, the spatial distribution of g‑factors follows a low‑order polynomial relationship. Consequently, the average g‑factor (\bar{g}) can be approximated by a first‑order Taylor expansion around the g‑factor at a single, conveniently chosen voxel (typically the image centre):

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