Simultaneous multi-slab (SMSlab) is a 3D acquisition method that can achieve optimal SNR efficiency for isotropic high-resolution DWI. However, boundary artifacts restrain its application. Nonlinear inversion for slab profile encoding (NPEN) seems to be inadequate for boundary artifacts correction in SMSlab. In this study, we proposed to use a model-based convolutional neural network (referred as CPEN) for this problem. According to the results, it outperforms NPEN in images with different resolutions, and the computation is much faster. Using CPEN, small oversampling factors can be used to reduced the acqsuition time, which is of great meaning for high-resolution whole-brain DWI.
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