Machine Learning for Heterogeneous Brain Magnetic Resonance Imaging: Bridging the Gap to Generalizable Models

磁共振（MR）成像已成为脑成像的关键技术，产生了大量的数据库，迅速增加了对大数据分析、强大的池化和协调的需求，特别是对于跨不同队列获取的数据。这些技术成功的一个障碍是图像采集协议和不同设备之间的固有差异，导致缺乏可重复的结果。研究表明，即使在采集标准化过程中，硬件、软件或方案设计的变化也会导致定量结果的差异和一致性的丧失。因此，MR在多中心或长期研究中的定量效用受到极大影响。机器学习（ML）已被广泛研究用于具有多个目标的MR成像分析，例如结构或异常的定量分析以及随时间推移的进展评估。然而，目前只有有限数量的应用程序在研究环境之外使用。一个关键原因是模型对不同来源或采集领域的数据的通用性差。在2010年代初，磁场强度为1.5 T的MR扫描仪被大量使用。在过去的几年里，这些机器已经被3T MR扫描仪所取代，不仅用于研究，而且用于常规检查。然而，为使用1.5 T图像的研究开发的工具显示出较差的泛化能力，在3 T MR图像中表现不佳。开发新的方法来处理这种多样化的MR成像数据对于实现准确的模型和扩大其用途至关重要。我的长期研究计划目标是解决目前ML在医学成像中广泛使用的局限性，重点关注进行大型和多站点研究的挑战。在我的第一个NSERC发现补助金中，我将开发ML模型的数据协调和领域适应策略，允许从一个数据集到另一个数据集的泛化，避免特定领域的决策，使用健康对照参与者和脑MR成像患者的特定分类和分割任务作为概念证明。ML模型在不同成像类型、不同受试者组（不同年龄范围、性别和病理学）和采集参数之间具有一致的结果，更可靠，并允许更广泛的使用。我预计，为大脑MR成像应用开发的ML工具的泛化能力将有显着提高。我的研究结果将显著影响研究领域，允许在更大的，异构的数据集和最佳实践中使用这些模型，当使用拟议的数据协调和域适应策略将学习从一个应用程序转换到另一个应用程序时。虽然我的短期目标是与MR成像合作，但所提出的策略对于其他医学图像和其他计算机视觉应用的其他应用来说将是实质性的。

Magnetic resonance (MR) imaging has become a key technology for brain imaging, resulting in massive databases, rapidly increasing the need for big data analytics, robust pooling, and harmonization, especially for data acquired across diverse cohorts. A barrier to the success of these techniques is the inherent variation between image acquisition protocols and different equipment, resulting in a lack of reproducible results. It has been shown that even when care is taken to standardize acquisitions, changes in hardware, software, or protocol design can lead to differences in quantitative results and loss of consistency. As a result, the quantitative utility of MR in multi-site or long-term studies is dramatically impacted. Machine learning (ML) has been extensively investigated for MR imaging analysis with multiple goals, such as quantitative analysis of structures or abnormalities and progress evaluation over time. Yet only a limited number of applications are now in use outside the research environment. A key reason for that is the poor generalizability of the models to data from different sources or acquisition domains. In the early 2010s, MR scanners with a magnetic field strength of 1.5 T were largely used. These machines have been replaced with 3 T MR scanners in the last years, not only for research but also for routine exams. However, tools developed for studies using 1.5 T images show poor generalization capability, performing poorly in 3 T MR images. Developing new methods to handle this diverse MR imaging data is crucial for achieving accurate models and broadening their usage. My long-term research program goal is to tackle the current limitations of the broader use of ML for medical imaging, focusing on the challenges of conducting large and multi-site studies. In my first NSERC Discovery Grant, I will develop data harmonization and domain adaptation strategies of ML models that allow generalization from one dataset to another, avoiding domain-specific decision-making, using specific classification and segmentation tasks on healthy control participants and patients on brain MR imaging as proof of concepts. ML models with consistent results across different imaging types, different groups of subjects (varying age ranges, sex, and pathology), and acquisition parameters are more reliable and allow broader usage. I anticipate a significant improvement in the generalization capability of ML tools developed for brain MR imaging applications. My findings will significantly impact the research area by allowing the usage of such models in larger, heterogeneous datasets and best practices when translating learning from one application to another using the proposed data harmonization and domain adaptation strategies. While my short-term goal is to work with MR imaging, the proposed strategies would be substantial for translation to other applications for other medical images and other computer vision applications.