Machine learning approach to non-invasive MRI-based blood oximetry

项目摘要 测量血氧（O2）饱和度，即血液中氧饱和血红蛋白的分数， 关于全身和器官特异性O2输送和消耗的信息，用于指导治疗， 干预X线透视下有创导管采血分析 引导是用于测量心脏中多个解剖位置的氧饱和度的标准方法 腔室和主要血管。使用磁共振（MR）无创测量氧饱和度 成像首次提出近30年前;然而，以前的技术依赖于拟合Luz， Meiboom模型和其他模型变体使用传统的线性和非线性回归模型方法。 虽然该模型捕捉到了基本的生物物理学原理，但它并没有完全描述生物学的特征。 血氧饱和度和MR信号之间的复杂关系。尽管是一种非侵入性的，非- 辐射替代侵入性导管插入术，由于模型的不足，MR血氧饱和度的准确性较低 以及估计方法，阻碍了该技术获得临床认可。我们建议 通过满足我们的总体目标来克服这一关键限制;部署基于以下内容的无模型方法： 机器学习（ML）开发和实施准确的、临床可行的MR血氧测定技术。我们 假设ML算法在参数估计方面比传统方法提供更大的灵活性， 可以被训练以学习和映射真实的体内关系，该关系描述MR血液信号的灵敏度， 氧饱和度。我们打算通过以下具体目标实现我们的目标。在目标1中，我们将开发 用于MR血氧测定的监督ML算法。预训练将使用L-M模拟的训练数据进行 模型，然后通过迁移学习用体内数据进行增强。同时，在目标2中，我们将设计和 实现用于体积数据采集3D MR血氧测定方法。容积图将有助于氧饱和度 整个血管系统的测量，并将支持与4D流量相结合，以评估O2 交付和消费。在目标3中，我们将在 转诊进行基于导管的O2饱和度测量的小患者队列。 我们提出的工作将首次应用机器学习来准确表征体内灵敏度 的横向弛豫时间（T2）加权MR血液信号的氧饱和度，使用的独特组合， 模拟和体内训练数据。基于ML的MR血氧测定将提供所需的测量精度 用于临床使用，因此将能够替代或减少侵入性， 放射治疗是一种安全、无创的替代方法。作为成像工具的基于ML的MR血氧饱和度测定是 预计将显着提高MR检查的诊断价值，并将特别有价值的 先天性心脏病患者的治疗因此，我们的工作符合NIBIB的使命， 随着新技术的发展，对人类健康产生积极影响。

PROJECT SUMMARY Measurement of blood oxygen (O2) saturation, the fraction of oxygen-saturated hemoglobin in blood, provides information on whole-body and organ-specific O2 delivery and consumption and is used to guide therapy and intervention. Blood sampling and analysis by invasive catheterization performed under X-ray fluoroscopic guidance is the standard method used to measure O2 saturation in multiple anatomical locations in the cardiac chambers and major blood vessels. Non-invasive measurement of O2 saturation using magnetic resonance (MR) imaging was first proposed nearly 30 years ago; however, previous techniques have relied on fitting the Luz- Meiboom model and other model variants using traditional linear and non-linear regression model approaches. Although the model captures the basic underlying biophysical principles, it does not fully characterize the complex relationship between blood O2 saturation and the MR signal. Despite being a non-invasive, non- radiating alternative to invasive catheterization, the low accuracy of MR oximetry, due to inadequacy of the model as well as estimation methods, have prevented the technique from gaining clinical acceptance. We propose to overcome this critical limitation by meeting our overall objective; to deploy a model-free approach based on machine learning (ML) to develop and implement an accurate, clinically feasible, MR oximetry technique. We hypothesize that ML algorithms provide greater flexibility in parameter estimation than traditional methods, and can be trained to learn and map the true in vivo relationship that describes the sensitivity of MR blood signal to O2 saturation. We intend to achieve our objective through the following specific aims. In Aim 1, we will develop a supervised ML algorithm for MR oximetry. Pre-training will occur with training data simulated using the L-M model and then augmented with in vivo data via transfer learning. Simultaneously, in Aim 2, we will design and implement a 3D MR oximetry method for volumetric data acquisition. A volumetric map will facilitate O2 saturation measurement throughout the vascular system, and will support the combination with 4D flow to evaluate O2 delivery and consumption. In Aim 3, we will validate the proposed ML-based 3D MR oximetry technique in a small cohort of patients referred for catheter-based O2 saturation measurement. For the first time, our proposed work will apply machine learning to accurately characterize the in vivo sensitivity of the transverse relaxation time (T2) weighted MR blood signal to O2 saturation, using a unique combination of simulated and in vivo training data. ML-based MR oximetry will provide the accuracy of measurement required for clinical use, and therefore will be able to replace or reduce the frequency and duration of an invasive, radiation-based method with a safe, non-invasive alternative. ML-based MR oximetry as an imaging tool is expected to significantly improve the diagnostic value of an MR exam, and will be especially valuable in the management of patients with congenital heart disease. Our work thus aligns with the mission of NIBIB to have a positive impact on human health with the development of novel technology.