Acceleration of Simultaneous Multislice Magnetic Resonance Fingerprinting With Spatiotemporal Convolutional Neural Network.

Abstract

Magnetic Resonance Fingerprinting (MRF) can be accelerated with simultaneous multislice (SMS) imaging for joint T₁ and T₂ quantification. However, the high inter-slice and in-plane acceleration in SMS-MRF causes severe aliasing artifacts, limiting the multiband (MB) factors to typically 2 or 3. Deep learning has demonstrated superior performance compared to the conventional dictionary matching approach for single-slice MRF, but its effectiveness in SMS-MRF remains unexplored. In this paper, we introduced a new deep learning approach with decoupled spatiotemporal feature learning for SMS-MRF to achieve high MB factors for accurate and volumetric T₁ and T₂ quantification in neuroimaging. The proposed method leverages information from both spatial and temporal domains to mitigate the significant aliasing in SMS-MRF. Neural networks, trained using either acquired SMS-MRF data or simulated data generated from single-slice MRF acquisitions, were evaluated. The performance was further compared with both dictionary matching and a deep learning approach based on residual channel attention U-Net. Experimental results demonstrated that the proposed method, trained with acquired SMS-MRF data, achieves the best performance in brain T₁ and T₂ quantification, outperforming dictionary matching and residual channel attention U-Net. With a MB factor of 4, rapid T₁ and T₂ mapping was achieved with 1.5 s per slice for quantitative brain imaging.