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一项关键技术，导致了海量数据库的产生，从而迅速增加了对大数据分析、健壮的池和协调的需求，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，导致缺乏可重现的结果。事实证明，即使注意标准化采集，硬件、软件或协议设计的更改也可能导致量化结果的差异和一致性的丧失。因此，磁共振在多点或长期研究中的定量效用受到了极大的影响。机器学习(ML)已被广泛用于具有多个目标的磁共振成像分析，如结构或异常的定量分析和随时间的进展评估。然而，目前在研究环境之外使用的应用程序数量有限。一个关键原因是模型对来自不同来源或采集域的数据的泛化能力较差。在2010年代初，磁场强度为1.5T的磁共振扫描仪被大量使用。在过去的几年里，这些机器已经被3T磁共振扫描仪取代，不仅用于研究，也用于常规检查。然而，为使用1.5T图像进行研究而开发的工具显示出较差的泛化能力，在3T MR图像中表现不佳。开发新的方法来处理这些不同的磁共振成像数据，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在医学成像中更广泛使用的限制，重点是进行大型和多站点研究的挑战。在我的第一个NSERC Discovery Grant中，我将开发ML模型的数据协调和领域适配策略，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，使用对健康对照参与者和脑部磁共振成像患者的特定分类和分割任务作为概念证明。在不同的成像类型、不同的受试者组(不同的年龄范围、性别和病理)和采集参数上具有一致结果的ML模型更可靠，并允许更广泛的应用。我预计，为脑磁共振成像应用开发的ML工具的泛化能力将有显着改善。我的发现将对研究领域产生重大影响，因为在使用拟议的数据协调和领域适应战略将学习从一个应用程序转换到另一个应用程序时，允许在更大的、不同种类的数据集和最佳做法中使用这种模型。虽然我的短期目标是与磁共振成像合作，但拟议的策略将对其他医学图像和其他计算机视觉应用程序的其他应用程序的翻译具有重要意义。

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.