DeepHarmony: A deep learning approach to contrast harmonization across scanner changes
DeepHarmony: A deep learning approach to contrast harmonization across scanner changes
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DOI：
10.1016/j.mri.2019.05.041
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发表时间：
2019-12-01
影响因子：
2.5
通讯作者：
Prince, Jerry L.
中科院分区：
文献类型：
--
作者：
Dewey, Blake E.;Zhao, Can;Prince, Jerry L.
磁共振成像(MRI)是一种灵活的医学成像方式，在方案和扫描仪之间往往缺乏重复性。事实证明，即使在注意标准化采集时，硬件、软件或协议设计的任何变化都可能导致量化结果的差异。这极大地影响了MRI在多部位或长期研究中的定量应用，在这些研究中，一致性往往比图像质量更受重视。我们提出了一种对比度协调的方法，称为深度协调，它使用基于U网的深度学习结构来产生具有一致对比度的图像。为了提供训练数据，使用两种不同的方案扫描了一个小的重叠队列(n=8)。与DeepHarmony协调的图像显示，在扫描方案之间的体积量化一致性方面有显著改善。多发性硬化症患者的纵向MRI数据集也被用来评估临床研究环境中方案改变对萎缩计算的影响。结果表明，萎缩计算受到协议更改的显著和显著影响，而当使用DeepHarmony时，此类更改的影响较小，并显著减小了总体差异。这确立了DeepHarmony可以与重叠队列一起使用，以减少由于扫描仪协议变化导致的分段不一致，从而允许在长期研究中实现硬件和协议设计的现代化，而不会使以前获得的数据无效。
Magnetic resonance imaging (MRI) is a flexible medical imaging modality that often lacks reproducibility between protocols and scanners. It has been shown that even when care is taken to standardize acquisitions, any changes in hardware, software, or protocol design can lead to differences in quantitative results. This greatly impacts the quantitative utility of MRI in multi-site or long-term studies, where consistency is often valued over image quality. We propose a method of contrast harmonization, called DeepHarmony, which uses a U-Net-based deep learning architecture to produce images with consistent contrast. To provide training data, a small overlap cohort (n = 8) was scanned using two different protocols. Images harmonized with DeepHarmony showed significant improvement in consistency of volume quantification between scanning protocols. A longitudinal MRI dataset of patients with multiple sclerosis was also used to evaluate the effect of a protocol change on atrophy calculations in a clinical research setting. The results show that atrophy calculations were substantially and significantly affected by protocol change, whereas such changes have a less significant effect and substantially reduced overall difference when using DeepHarmony. This establishes that DeepHarmony can be used with an overlap cohort to reduce inconsistencies in segmentation caused by changes in scanner protocol, allowing for modernization of hardware and protocol design in long-term studies without invalidating previously acquired data.