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

基于 MRI 的无创血氧测定的机器学习方法

• 批准号: 10217710
• 负责人: Juliet J. Varghese
• 金额: $ 58.78万
• 依托单位: OHIO STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-21 至 2024-09-20
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10217710
• 关键词: 3-Dimensional; Adult; Age; Anatomy; Biomedical Technology; Biophysics; Blood; Blood Circulation; Blood Vessels; Blood flow; Blood specimen; Cardiac; Catheterization; Catheters; Cerebrum; Clinical; Clinical Management; Collaborations; Complex; Congenital Cardiovascular Abnormality; Consumption; Data; Detection; Development; Diagnosis; Diagnostic; Disease; Early Diagnosis; Early Intervention; Evaluation; Fetus; Fingers; Frequencies; Funding; Goals; Health; Heart; Heart Abnormalities; Heart failure; Hemoglobin; Human; Image; Imaging Device; Institution; Intervention; Kidney; Learning; Limb structure; Location; Machine Learning; Magnetic Resonance; Magnetic Resonance Imaging; Maps; Measurement; Measures; Methods; Mission; Modeling; Morphology; National Institute of Biomedical Imaging and Bioengineering; Nature; Network-based; Operative Surgical Procedures; Organ; Outcome; Oxygen; Oxygen saturation measurement; Patients; Pediatric Hospitals; Peripheral; Physiologic pulse; Physiology; Procedures; Process; Property; Psychological Transfer; Public Health; Pulmonary Hypertension; Pulmonary vessels; Radiation; Radiation exposure; Relaxation; Risk; Roentgen Rays; Salvelinus; Scanning; Signal Transduction; Structure; Techniques; Technology; Testing; Time; Training; Variant; Vascular System; Venous; Work; base; cerebrovascular; clinical application; clinical practice; cohort; congenital heart disorder; data acquisition; design; diagnostic catheterization; disease diagnosis; feedforward neural network; flexibility; imaging capabilities; improved; in vivo; individual patient; interest; limb ischemia; machine learning algorithm; machine learning method; meetings; neonate; neural network; new technology; nonlinear regression; novel; post-transplant; prevent; supervised learning; tool

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.

项目摘要 血液氧（O2）饱和度的测量，血液中氧饱和血红蛋白的分数提供 有关全身和特定器官的O2输送和消费的信息，用于指导治疗和 干涉。在X射线荧光镜下进行的浸润性导管插入术进行的血液采样和分析 指导是用于测量心脏多个解剖位置中O2饱和度的标准方法 腔室和主要血管。使用磁共振（MR）对O2饱和度进行非侵入性测量 成像是大约30年前提出的。但是，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。 尽管该模型捕获了基本的潜在生物物理原理，但并未完全表征 血液O2饱和度与MR信号之间的复杂关系。尽管是无创，非侵入性的 由于模型不足而导致侵入性导管插入术的替代替代品，MR的准确性低。 以及估计方法，也阻止了该技术获得临床接受。我们建议 通过满足我们的整体目标来克服这一关键限制；根据基于模型的方法部署 机器学习（ML）以开发和实施准确的，临床上可行的血氧仪技术。我们 假设ML算法比传统方法提供了更大的参数估计灵活性，并且 可以训练学习并绘制描述MR信号的敏感性的真实体内关系 O2饱和度。我们打算通过以下特定目标来实现我们的目标。在AIM 1中，我们将发展 用于血氧仪的有监督的ML算法。使用L-M模拟的训练数据将发生预训练 模型，然后通过转移学习来增强体内数据。同时，在AIM 2中，我们将设计和 实施3D MR血氧蛋白法以进行体积数据采集。体积图将有助于O2饱和 整个血管系统的测量，将支持4D流以评估O2的组合 交付和消费。在AIM 3中，我们将验证A中提出的基于ML的3D MR血氧仪技术 少量参考基于导管的O2饱和度测量的患者。 我们提出的工作首次应用机器学习来准确表征体内敏感性 横向松弛时间（T2）加权MR信号至O2饱和，使用独特的组合 模拟和体内培训数据。基于ML的MR Oximetry将提供所需的测量的准确性 供临床使用，因此能够替换或减少侵入性的频率和持续时间 基于辐射的方法，具有安全，无创替代方案。基于ML的MR Oximetry作为成像工具是 预计将显着提高MR考试的诊断价值，并且在 先天性心脏病患者的管理。因此，我们的工作与尼比布的使命保持一致 随着新技术的发展，对人类健康的积极影响。