Harmonization of breast MRI data

乳腺 MRI 数据的协调

• 批准号: 10367288
• 负责人: Maciej A. Mazurowski
• 金额: $ 65.71万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-15 至 2026-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10367288
• 关键词: Abdomen; Address; Adoption; Algorithms; Appearance; Brain; Breast; Breast Magnetic Resonance Imaging; Cancer Detection; Clinical; Collaborations; Computational algorithm; Data; Data Set; Diagnosis; Disease; Evaluation; Genomics; Head; Hip region structure; Image; Institution; Journals; Machine Learning; Magnetic Resonance Imaging; Malignant Neoplasms; Mammary Neoplasms; Manufacturer Name; Methodology; Methods; Modeling; Modernization; Modification; Noise; Organ; Paper; Patient-Focused Outcomes; Patients; Publications; Reproducibility; Research; Research Personnel; Scanning; Scientist; Source; Structure; Texture; Thinking; Time; Tissues; Training; Translations; Validation; Work; base; breast imaging; cancer risk; clinical application; clinical implementation; clinical practice; contrast enhanced; convolutional neural network; deep learning; deep learning algorithm; expectation; experience; generative adversarial network; genomic signature; high risk; imaging modality; improved; innovation; linear transformation; novel; novel strategies; outcome prediction; patient prognosis; practical application; predictive modeling; radiologist; radiomics; reconstruction; tumor

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.

抽象的 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不同的结果。 同一患者的不同图像。当尝试使用 MRI 进行定量分析时，这是一个重要的问题。 方式。多项研究表明，通过乳腺 MRI 定量分析来诊断乳腺有希望 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，人们无法利用不同来源的图像来回答问题 研究问题。此外，一个机构开发的预测模型可能无法推广到其他机构 机构。虽然这是一个众所周知的问题，但目前乳腺 MRI 尚无解决方案。一些 为了解决其他器官（主要是大脑）的这个问题，已经做出了有效的努力。 然而，这些器官的问题还没有得到解决，现有的验证也很有限。 实际情况中的方法阻碍了实施。乳房是一个非刚性器官，具有高度可变性 组合使得乳腺 MRI 的协调特别具有挑战性，并且使得几乎所有先前的 为大脑开发的协调方法不适用。鉴于迫切需要协调 定量研究中，我们提出了三种协调方法，可以转换所获取的图像 使用一个扫描仪设置来呈现另一扫描仪设置的外观。我们介绍重要的技术 利用尖端的卷积神经网络来完成这项任务的创新。此外，我们提出了一个新的 尚未引起重大系统性思考的问题的方法：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方法。这种方法在乳房中不切实际 成像，因为它需要理想配对的图像，它不能很好地处理预期的图像噪声，并且它不能 告知评估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（包括放射组学分析）评估协调算法 和深度学习。该研究将由机器学习科学家（杜克大学）合作进行 和耶鲁大学）、乳腺 MRI 物理学家（康奈尔大学）、研究重点是 MRI 的放射科医生（杜克大学）和 生物统计学家（杜克大学）。所提出的协调和评估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，它们将适用于其他器官 一旦使用该器官的适当数据实施。所有协调和评估算法以及 这些数据将公开，以推动对这一重要的未解决研究课题的进一步研究。