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大的差异。 同一个病人有不同的影像这是一个重要的问题，当试图使用MRI在定量 方式多项研究表明，定量分析乳腺MRI诊断乳腺癌的前景 这些技术可以用于预测肿瘤，预测患者结果，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 落实这些调查结果。在许多情况下，一个人不能利用来自不同来源的图像来回答一个问题。 研究问题。此外，在一个机构开发的预测模型可能无法推广到其他机构。 机构职能体系虽然这是一个公认的问题，但目前在乳腺MRI中没有解决方案。一些 为解决其他器官，主要是大脑的这一问题，已经作出了有效的努力。 然而，这些机构的问题也没有得到解决，对现有机构的确认有限， 实际情况中的方法阻碍了执行。乳房是一个非刚性器官， 这使得乳腺MRI的协调特别具有挑战性，并且使得几乎所有先前的 为大脑制定的协调方法不适用。鉴于迫切需要统一 定量研究，我们提出了三种协调方法，允许转换图像采集 使用一个扫描仪设置来呈现另一个扫描仪设置的外观。我们引进重要的技术 创新利用尖端的卷积神经网络来完成这项任务。此外，我们还提出了一个新的 一个尚未引起重大系统考虑的问题的方法：是什么使一个 协调算法是否成功或有用？我们不评估像素之间的像素匹配， 协调图像和参考图像，这是典型的方法。这种方法在乳腺癌中不切实际。 成像，因为它需要理想配对的图像，所以它不能很好地处理预期的图像噪声，并且它不能 告知所评价的协调方法的具体局限性。我们提出一个评估框架 根据不同的实际应用评估协调算法，包括放射组学分析 和深度学习。这项研究将由一位机器学习科学家（杜克 和耶鲁大学），乳腺MRI物理学家（康奈尔大学），放射科医生，其研究重点是MRI（杜克），和一个 生物统计学家（杜克）。拟议的协调和评价方法不需要完全配对的数据 并且不对组织成分做出假设。因此，它们将适用于其他机构 一旦用器官的适当数据实现。所有协调和评估算法沿着 这些数据将被公开，以推动对这一关键的未解决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.