计算机科学
人工智能
对比度(视觉)
电磁线圈
翻译(生物学)
监督学习
基本事实
机器学习
模式识别(心理学)
深度学习
迭代重建
计算机视觉
人工神经网络
生物化学
化学
信使核糖核酸
电气工程
基因
工程类
作者
Mahmut Yurt,Onat Dalmaz,Salman Ul Hassan Dar,Muzaffer Özbey,Berk Tınaz,Kader K. Oğuz,Tolga Çukur
出处
期刊:IEEE Transactions on Medical Imaging
[Institute of Electrical and Electronics Engineers]
日期:2022-12-01
卷期号:41 (12): 3895-3906
被引量:8
标识
DOI:10.1109/tmi.2022.3199155
摘要
Learning-based translation between MRI contrasts involves supervised deep models trained using high-quality source- and target-contrast images derived from fully-sampled acquisitions, which might be difficult to collect under limitations on scan costs or time. To facilitate curation of training sets, here we introduce the first semi-supervised model for MRI contrast translation (ssGAN) that can be trained directly using undersampled k-space data. To enable semi-supervised learning on undersampled data, ssGAN introduces novel multi-coil losses in image, k-space, and adversarial domains. The multi-coil losses are selectively enforced on acquired k-space samples unlike traditional losses in single-coil synthesis models. Comprehensive experiments on retrospectively undersampled multi-contrast brain MRI datasets are provided. Our results demonstrate that ssGAN yields on par performance to a supervised model, while outperforming single-coil models trained on coil-combined magnitude images. It also outperforms cascaded reconstruction-synthesis models where a supervised synthesis model is trained following self-supervised reconstruction of undersampled data. Thus, ssGAN holds great promise to improve the feasibility of learning-based multi-contrast MRI synthesis.
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