人工智能
残余物
计算机科学
模式识别(心理学)
磁共振弥散成像
相似性(几何)
特征(语言学)
扩散
扩散成像
图像分辨率
分辨率(逻辑)
噪音(视频)
核磁共振
算法
图像(数学)
磁共振成像
物理
放射科
医学
语言学
哲学
热力学
作者
Fanwen Wang,Hui Zhang,Fei Dai,Weibo Chen,Shuai Xu,Zhengfei Yang,Dinggang Shen,Chengyan Wang,He Wang
出处
期刊:IEEE Journal of Biomedical and Health Informatics
[Institute of Electrical and Electronics Engineers]
日期:2022-09-01
卷期号:26 (9): 4575-4586
被引量:1
标识
DOI:10.1109/jbhi.2022.3193299
摘要
Single-Shot Echo Planar Imaging (SSEPI) based Diffusion Weighted Imaging (DWI) has shortcomings such as low resolution and severe distortions. In contrast, Multi-Shot EPI (MSEPI) provides optimal spatial resolution but increases scan time. This study proposed a Multiple b-value mOdel-based Residual Network (MORN) model to reconstruct multiple b-value high-resolution DWI from undersampled k-space data simultaneously. We incorporated Parallel Imaging (PI) into a residual U-net to reconstruct multiple b-value multi-coil data with the supervision of MUltiplexed Sensitivity-Encoding (MUSE) reconstructed Multi-Shot DWI (MSDWI). Moreover, asymmetric concatenations among different b-values and the combined loss to back propagate helped the feature transfer. After training and validation of the MORN in a dataset of 32 healthy cases, additional assessments were performed on 6 patients with different tumor types. The experimental results demonstrated that the MORN model outperformed conventional PI reconstruction (i.e. SENSE) and two state-of-the-art deep learning methods (SENSE-GAN and VSNet) in terms of PSNR (Peak Signal-to-Noise Ratio), SSIM (Structual SIMilarity) and apparent diffusion coefficient maps. In addition, using the pre-trained model under DWI, the MORN achieved consistent fractional anisotrophy and mean diffusivity reconstructed from multiple diffusion directions. Hence, the proposed method shows potential in clinical application according to the observations on tumor patients as well as images of multiple diffusion directions.
科研通智能强力驱动
Strongly Powered by AbleSci AI