Deep-learning-based image quality enhancement of compressed sensing magnetic resonance imaging of vessel wall: comparison of self-supervised and unsupervised approaches
Deep-learning-based image quality enhancement of compressed sensing magnetic resonance imaging of vessel wall: comparison of self-supervised and unsupervised approaches
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DOI：
10.1038/s41598-020-69932-w
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发表时间：
2020-08-18
影响因子：
4.6
通讯作者：
Kim, Namkug
中科院分区：
文献类型：
--
作者：
Eun, Da-In;Jang, Ryoungwoo;Kim, Namkug
虽然高分辨率磁共振血管壁质子密度加权成像(MRI)对准确诊断颅内动脉疾病具有重要意义，但其较长的采集时间是临床负担。压缩传感MRI是一种具有加速因子的有前景的技术，可以潜在地减少扫描时间。然而，较高的加速系数会导致图像质量下降。尽管基于深度学习的图像恢复算法的最新进展可以缓解这一问题，但用于深度学习训练的临床图像对通常不会在像素上对齐。因此，本文提出了两种不同的基于深度学习的去噪算法--自监督学习和无监督学习，这两种算法适用于像素方向不一致的临床数据集。对这两种方法进行了定性和定量的比较。这两种方法在图像去噪和视觉分级方面都取得了令人满意的结果。虽然自我监督学习的图像噪声和信噪比优于非监督学习，但就放射学特征的重复性而言，非监督学习优于自我监督学习。
While high-resolution proton density-weighted magnetic resonance imaging (MRI) of intracranial vessel walls is significant for a precise diagnosis of intracranial artery disease, its long acquisition time is a clinical burden. Compressed sensing MRI is a prospective technology with acceleration factors that could potentially reduce the scan time. However, high acceleration factors result in degraded image quality. Although recent advances in deep-learning-based image restoration algorithms can alleviate this problem, clinical image pairs used in deep learning training typically do not align pixel-wise. Therefore, in this study, two different deep-learning-based denoising algorithms-self-supervised learning and unsupervised learning-are proposed; these algorithms are applicable to clinical datasets that are not aligned pixel-wise. The two approaches are compared quantitatively and qualitatively. Both methods produced promising results in terms of image denoising and visual grading. While the image noise and signal-to-noise ratio of self-supervised learning were superior to those of unsupervised learning, unsupervised learning was preferable over self-supervised learning in terms of radiomic feature reproducibility.