MC2‐Net: motion correction network for multi‐contrast brain MRI. Issue 2 (15th March 2021)

• Record Type: Journal Article
• Authors: Lee, Jongyeon; Kim, Byungjai; Park, HyunWook
• Is Part Of: Magnetic resonance in medicine. Volume 86:Issue 2(2021)
• Page Start: 1077
• Page End: 1092
• Publication Date: 2021-03-15
• Subjects: deep learning -- motion correction -- multi‐contrast MRI -- registration; Nuclear magnetic resonance -- Periodicals; Electron paramagnetic resonance -- Periodicals; 616.07548
• DOI: 10.1002/mrm.28719
• Languages: English
• ISSNs: 0740-3194

Abstract

Abstract : Purpose: A motion‐correction network for multi‐contrast brain MRI is proposed to correct in‐plane rigid motion artifacts in brain MR images using deep learning. Method: The proposed method consists of 2 parts: image alignment and motion correction. Alignment of multi‐contrast MR images is performed in an unsupervised manner by a CNN work, yielding transformation parameters to align input images in order to minimize the normalized cross‐correlation loss among multi‐contrast images. Then, fine‐tuning for image alignment is performed by maximizing the normalized mutual information. The motion correction network corrects motion artifacts in the aligned multi‐contrast images. The correction network is trained to minimize the structural similarity loss and the VGG loss in a supervised manner. All datasets of motion‐corrupted images are generated using motion simulation based on MR physics. Results: A motion‐correction network for multi‐contrast brain MRI successfully corrected artifacts of simulated motion for 4 test subjects, showing 0.96%, 7.63%, and 5.03% increases in the average structural simularity and 5.19%, 10.2%, and 7.48% increases in the average normalized mutual information for T1 ‐weighted, T2 ‐weighted, and T2 ‐weighted fluid‐attenuated inversion recovery images, respectively. The experimental setting with image alignment and artifact‐free input images for other contrasts shows better performances in correction of simulated motion artifacts. Furthermore, the proposed method quantitatively outperforms recent deep learning motion correction and synthesis methods. Real motion experiments from 5 healthy subjects demonstrate the potential of the proposed method for use in a clinical environment. Conclusion: A deep learning‐based motion correction method for multi‐contrast MRI was successfully developed, and experimental results demonstrate the validity of the proposed method.