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Deep-learned short tau inversion recovery imaging using multi-contrast MR images

Authors
 Sewon Kim  ;  Hanbyol Jang  ;  Jinseong Jang  ;  Young Han Lee  ;  Dosik Hwang 
Citation
 MAGNETIC RESONANCE IN MEDICINE, Vol.84(6) : 2994-3008, 2020-12 
Journal Title
 MAGNETIC RESONANCE IN MEDICINE 
ISSN
 0740-3194 
Issue Date
2020-12
Keywords
deep learning ; image synthesis ; knee ; magnetic resonance imaging ; neural network ; short tau inversion recovery ; short-TI inversion recovery
Abstract
Purpose: To generate short tau, or short inversion time (TI), inversion recovery (STIR) images from three multi-contrast MR images, without additional scanning, using a deep neural network. Methods: For simulation studies, we used multi-contrast simulation images. For in-vivo studies, we acquired knee MR images including 288 slices of T1 -weighted (T1 -w), T2 -weighted (T2 -w), gradient-recalled echo (GRE), and STIR images taken from 12 healthy volunteers. Our MR image synthesis method generates a new contrast MR image from multi-contrast MR images. We used a deep neural network to identify the complex relationships between MR images that show various contrasts for the same tissues. Our contrast-conversion deep neural network (CC-DNN) is an end-to-end architecture that trains the model to create one image from three (T1 -w, T2 -w, and GRE images). We propose a new loss function to take into account intensity differences, misregistration, and local intensity variations. The CC-DNN-generated STIR images were evaluated with four quantitative evaluation metrics, including mean squared error, peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and multi-scale SSIM (MS-SSIM). Furthermore, a subjective evaluation was performed by musculoskeletal radiologists. Results: Our method showed improved results in all quantitative evaluations compared with other methods and received the highest scores in subjective evaluations by musculoskeletal radiologists. Conclusion: This study suggests the feasibility of our method for generating STIR sequence images without additional scanning that offered a potential alternative to the STIR pulse sequence when additional scanning is limited or STIR artifacts are severe.
Full Text
https://onlinelibrary.wiley.com/doi/10.1002/mrm.28327
DOI
10.1002/mrm.28327
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 1. Journal Papers
Yonsei Authors
Lee, Young Han(이영한) ORCID logo https://orcid.org/0000-0002-5602-391X
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/181436
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