Purpose: This study aims to propose a novel algorithm for slab boundary artifact correction in both single-band multislab imaging and simultaneous multislab (SMSlab) imaging.
Theory and methods: In image domain, the formation of slab boundary artifacts can be regarded as modulating the artifact-free images using the slab profiles and introducing aliasing along the slice direction. Slab boundary artifact correction is the inverse problem of this process. An iterative algorithm based on convolutional neural networks (CNNs) is proposed to solve the problem, termed CNN-enabled inversion for slab profile encoding (CPEN). Diffusion-weighted SMSlab images and reference images without slab boundary artifacts were acquired in 7 healthy subjects for training. Images of 5 healthy subjects were acquired for testing, including single-band multislab and SMSlab images with 1.3-mm or 1-mm isotropic resolution. CNN-enabled inversion for slab profile encoding was compared with a previously reported method (i.e., nonlinear inversion for slab profile encoding [NPEN]).
Results: CNN-enabled inversion for slab profile encoding reduces the slab boundary artifacts in both single-band multislab and SMSlab images. It also suppresses the slab boundary artifacts in the diffusion metric maps. Compared with NPEN, CPEN shows fewer residual artifacts in different acquisition protocols and more significant improvements in quantitative assessment, and it also accelerates the computation by more than 35 times.
Conclusion: CNN-enabled inversion for slab profile encoding can reduce the slab boundary artifacts in multislab acquisitions. It shows better slab boundary artifact correction capacity, higher robustness, and computation efficiency when compared with NPEN. It has the potential to improve the accuracy of multislab acquisitions in high-resolution DWI and functional MRI.
Keywords: 3D multislab; deep learning; model-based CNN; simultaneous multislab; slab boundary artifacts.
© 2021 International Society for Magnetic Resonance in Medicine.