Harmonization of breast MRI data

摘要 不同的磁共振成像(MRI)扫描仪和不同的采集参数可以产生非常 相同患者的不同图像。当试图定量地使用磁共振成像时，这是一个重要的问题 举止。多项研究表明，定量分析乳房核磁共振成像诊断乳房是有希望的 这项技术可以用来预测肿瘤、预测患者预后、评估癌症风险，甚至识别癌症的基因组特征。 然而，图像的不均匀性问题阻碍了研究和临床的进展。 实施这些调查结果。在许多情况下，人们不能利用来自不同来源的图像来回答 研究问题。此外，一家机构开发的预测模型可能不会推广到另一家机构 机构。虽然这是一个公认的问题，但目前在乳腺MRI中还没有解决方案。一些人 为了解决其他器官，主要是大脑的这一问题，已经作出了有效的努力。 然而，这些器官的问题也没有得到解决，现有的验证有限 实际情况下的方法阻碍了实施。乳房是一个高度可变的非刚性器官 成分使得乳房磁共振成像的协调特别具有挑战性，并使几乎所有的 为大脑开发的协调方法不适用。鉴于以下方面迫切需要统一 定量研究，我们提出了三种协调方法，允许转换采集的图像 使用一个扫描仪设置来呈现另一个扫描仪设置的外观。我们介绍了重要的技术 创新利用尖端的卷积神经网络来完成这项任务。此外，我们还提出了一种新的 对尚未引起重大系统考虑的问题的方法：是什么使 协调算法成功还是有用？我们不评估像素之间的匹配 协调图像和参考图像是典型的处理方法。这种方法在乳房是不切实际的。 成像因为它需要理想的图像配对，所以它不能很好地处理预期的图像噪声，而且它也不能 告知评估的协调方法的具体限制。我们提出了一个评估框架 根据不同的实际应用评估协调算法，包括放射学分析 和深度学习。这项研究将在机器学习科学家(Duke)的合作下进行 和耶鲁)，乳腺核磁共振物理学家(康奈尔大学)，放射科医生，其研究重点是核磁共振(杜克大学)，和一个 生物统计学家(杜克大学)。拟议的统一和评价方法不需要完全配对的数据 而且不要对组织的组成做出假设。因此，它们将适用于其他器官。 一旦与器官的适当数据一起实施。所有协调和评估算法以及 这些数据将公之于众，以引领对这一关键悬而未决的研究课题的进一步研究。

ABSTRACT Different magnetic resonance imaging (MRI) scanners and different acquisition parameters can produce very different images for the same patients. This is a significant issue when attempting to use MRIs in a quantitative manner. Multiple studies have shown promise of quantitative analysis of breast MRIs to diagnose breast tumors, predict patient outcomes, assess cancer risk, and even identify genomic signatures of cancers. However, the issue of inhomogeneity of images hampers the progress of the research and clinical implementation of these findings. In many cases one cannot utilize images from different sources to answer a research question. Furthermore, predictive models developed at one institution may not generalize to other institutions. While this is a well-recognized problem, there is currently no solution to it in breast MRI. Some valid efforts have been undertaken in order to address this issue for other organs, predominantly brain. However, the problem has not been solved for those organs neither and limited validation of the existing methods in practical contexts hampers the implementation. Breast is a non-rigid organ with highly variable composition making the harmonization of breast MRIs particularly challenging and making almost all prior harmonization methods developed for brain not applicable. Given the urgent need for harmonization in quantitative research, we propose three harmonization methods that allow for transforming an image acquired using one scanner setup to assume appearance of another scanner setup. We introduce important technical innovations to utilize cutting-edge convolutional neural networks for this task. Additionally, we propose a new approach to the question that has not yet attracted significant systematic consideration: what makes a harmonization algorithm successful or useful? We do not evaluate pixel-to-pixel match between the harmonized image and a reference image which is the typical approach. This approach is impractical in breast imaging since it requires ideally paired images, it does not deal well with expected image noise, and it does not inform about specific limitations of the evaluated harmonization method. We propose an evaluation framework that assesses harmonization algorithms in terms of different practical applications including radiomic analysis and deep learning. The study will be conducted in collaboration between a machine learning scientists (Duke and Yale), a breast MRI physicist (Cornell), a radiologist whose research focuses on MRI (Duke), and a biostatistician (Duke). The proposed harmonization and evaluation methods do not require fully paired data and do not make assumptions about tissue composition. Therefore, they will be applicable across other organs once implemented with appropriate data for the organ. All harmonization and evaluation algorithms along with the data will be made publicly available to spearhead further research on this crucial unsolved research topic.