机构：[1]Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, People’s Republic of China[2]Department of Radiology, Stanford University, Stanford, California, USA[3]Center for Nano and Micro Mechanics, Tsinghua University, Beijing, People’s Republic of China[4]Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing, People’s Republic of China首都医科大学宣武医院放射与核医学科医技科室
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.
基金：
National Key Research and Development
Program (2016YFC0103905,
2017YFA0205904, and 2017YFC0108905)
and the National Natural Science
Foundation of China (61971258)
第一作者机构：[1]Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, People’s Republic of China
通讯作者：
通讯机构：[1]Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, People’s Republic of China[*1]Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, People’s Republic of China.
推荐引用方式(GB/T 7714)：
Zhang Jieying,Liu Simin,Dai Erpeng,et al.Slab boundary artifact correction in multislab imaging using convolutional-neural-network-enabled inversion for slab profile encoding[J].MAGNETIC RESONANCE IN MEDICINE.2022,87(3):1546-1560.doi:10.1002/mrm.29047.
APA：
Zhang, Jieying,Liu, Simin,Dai, Erpeng,Ye, Xinyu,Shi, Diwei...&Guo, Hua.(2022).Slab boundary artifact correction in multislab imaging using convolutional-neural-network-enabled inversion for slab profile encoding.MAGNETIC RESONANCE IN MEDICINE,87,(3)
MLA：
Zhang, Jieying,et al."Slab boundary artifact correction in multislab imaging using convolutional-neural-network-enabled inversion for slab profile encoding".MAGNETIC RESONANCE IN MEDICINE 87..3(2022):1546-1560
医学