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.
Bibliographical noteFunding Information:
This research was supported by a National Research Foundation of Korea (NRF) grant (No. 2019R1A2B5B01070488, 2018M3A9H6081483, 2018R1A2B6009076) funded by the Ministry of Science, ICT and Future Planning (MSIP). The authors thank Hong Seon Lee, Inha Jung and Ok Kyu Song for their detailed analysis as musculoskeletal experts.
This research was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No. 2019R1A2B5B01070488, 2018M3A9H6081483, 2018R1A2B6009076)
© 2020 International Society for Magnetic Resonance in Medicine
All Science Journal Classification (ASJC) codes
- Radiology Nuclear Medicine and imaging