Zhang Jieying, Liu Simin, Dai Erpeng, Ye Xinyu, Shi Diwei, Wu Yuhsuan, Lu Jie, Guo Hua
Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, People's Republic of China.
Department of Radiology, Stanford University, Stanford, California, USA.
Magn Reson Med. 2022 Mar;87(3):1546-1560. doi: 10.1002/mrm.29047. Epub 2021 Oct 15.
This study aims to propose a novel algorithm for slab boundary artifact correction in both single-band multislab imaging and simultaneous multislab (SMSlab) imaging.
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]).
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.
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.
本研究旨在提出一种用于单波段多层面成像和同步多层面（SMSlab）成像中层面边界伪影校正的新算法。
在图像域中，层面边界伪影的形成可视为使用层面轮廓对无伪影图像进行调制，并沿层面方向引入混叠。层面边界伪影校正是此过程的逆问题。提出了一种基于卷积神经网络（CNN）的迭代算法来解决该问题，称为用于层面轮廓编码的基于CNN的反演（CPEN）。在7名健康受试者中采集了扩散加权SMSlab图像和无层面边界伪影的参考图像用于训练。采集了5名健康受试者的图像用于测试，包括具有1.3毫米或1毫米各向同性分辨率的单波段多层面图像和SMSlab图像。将用于层面轮廓编码的基于CNN的反演与先前报道的方法（即用于层面轮廓编码的非线性反演[NPEN]）进行比较。
用于层面轮廓编码的基于CNN的反演减少了单波段多层面图像和SMSlab图像中的层面边界伪影。它还抑制了扩散度量图中的层面边界伪影。与NPEN相比，CPEN在不同采集协议中显示出更少的残余伪影，在定量评估中有更显著的改善，并且计算速度加快了35倍以上。
用于层面轮廓编码的基于CNN的反演可减少多层面采集中的层面边界伪影。与NPEN相比，它显示出更好的层面边界伪影校正能力、更高的鲁棒性和计算效率。它有可能提高高分辨率扩散加权成像和功能磁共振成像中多层面采集的准确性。
