Qu Liangqiong, Wang Shuai, Yap Pew-Thian, Shen Dinggang
Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
Med Image Comput Comput Assist Interv. 2019 Oct;11767:786-794. doi: 10.1007/978-3-030-32251-9_86. Epub 2019 Oct 10.
Ultra-high field 7T magnetic resonance imaging (MRI) scanners produce images with exceptional anatomical details, which can facilitate diagnosis and prognosis. However, 7T MRI scanners are often cost prohibitive and hence inaccessible. In this paper, we propose a novel wavelet-based semi-supervised adversarial learning framework to synthesize 7T MR images from their 3T counterparts. Unlike most learning methods that rely on supervision requiring a significant amount of 3T-7T paired data, our method applies a semi-supervised learning mechanism to leverage unpaired 3T and 7T MR images to learn the 3T-to-7T mapping when 3T-7T paired data are scarce. This is achieved via a cycle generative adversarial network that operates in the joint spatial-wavelet domain for the synthesis of multi-frequency details. Extensive experimental results show that our method achieves better performance than state-of-the-art methods trained using fully paired data.
超高场7T磁共振成像（MRI）扫描仪能够生成具有卓越解剖细节的图像，这有助于疾病的诊断和预后评估。然而，7T MRI扫描仪成本高昂，因此难以普及。在本文中，我们提出了一种基于小波的新型半监督对抗学习框架，用于从3T的MRI图像合成7T的MRI图像。与大多数依赖监督的学习方法不同，这些方法需要大量的3T-7T配对数据，而我们的方法应用半监督学习机制，在3T-7T配对数据稀缺时，利用未配对的3T和7T MRI图像来学习3T到7T的映射。这是通过一个循环生成对抗网络实现的，该网络在联合空间-小波域中运行，以合成多频率细节。大量实验结果表明，我们的方法比使用完全配对数据训练的现有方法具有更好的性能。
