Development of a Clinical CEST MR Fingerprinting Method for Treatment Response Assessment in Brain Metastases

开发用于脑转移治疗反应评估的临床 CEST MR 指纹识别方法

• 批准号: 10443238
• 负责人: Ouri Cohen
• 金额: $ 70.79万
• 依托单位: SLOAN-KETTERING INST CAN RESEARCH
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-04-01 至 2027-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10443238
• 关键词: 3-Dimensional; Address; Amides; Artificial Intelligence; Biological; Brain; Brain Neoplasms; Brain imaging; Cancer Patient; Chemicals; Clinic; Clinical; Custom; Data; Development; Diagnosis; Dictionary; Discrimination; Disease; Disease Marker; Disease Progression; Early Diagnosis; Fingerprint; Goals; Guidelines; Head; Human; Image; Imaging Device; Imaging Techniques; Immunotherapy; Incidence; Magnetic Resonance; Magnetic Resonance Imaging; Malignant Glioma; Malignant Neoplasms; Maps; Measurement; Measures; Metastatic malignant neoplasm to brain; Methods; Motion; Nature; Neurologic Symptoms; Normal tissue morphology; Operative Surgical Procedures; Outcome; Pathology; Patients; Physics; Physiologic pulse; Physiological; Primary Neoplasm; Property; Proteins; Protons; Radiation necrosis; Radiation therapy; Radiosurgery; Relaxation; Reproducibility; Resolution; Role; Scanning; Schedule; Signal Transduction; Slice; Solid; Specificity; Survival Rate; Techniques; Testing; Time; Tissues; Translating; Treatment Protocols; Water; base; cancer imaging; chemotherapy; clinical translation; complex data; computerized data processing; contrast imaging; deep learning; diagnostic tool; experimental study; extracellular; human subject; imaging biomarker; imaging modality; imaging study; improved; in vivo; insight; learning strategy; macromolecule; mortality; neural network; novel; pre-clinical; predicting response; protein metabolite; quantitative imaging; radio frequency; reconstruction; soft tissue; tissue mapping; tool; treatment planning; treatment response; tumor; tumor progression

Project Summary/Abstract Chemical Exchange Saturation Transfer (CEST) MRI uses selective radio-frequency (RF) pulses to saturate the magnetization of exchangeable protons on a variety of molecules and macromolecules, including proteins, which, due to fast chemical exchange with bulk water, results in a decreased water MRI signal. The CEST contrast depends on the chemical exchange rate (kex), which is pH sensitive, and the volume fraction of the exchangeable proton pool (fs) that is sensitive to protein and metabolite concentrations. The sensitivity of CEST MRI to pH and protein/metabolite concentrations has proven to be a powerful tool for imaging a wide range of disease pathologies. For example, the amide proton CEST contrast from endogenous proteins has been used to distinguish pseudo-progression from true progression in malignant gliomas, differentiate between radiation necrosis and tumor progression, and image the tumor's extracellular pH. However, clinical translation of these CEST-MRI methods has been hindered by the qualitative nature of the image contrast, long image acquisition times, and the complex data processing required. Efficient methods for quantification of kex and fs are needed to produce high-quality pH and volume fraction maps required to move many of these studies forward into the clinic. In this proposal a CEST magnetic resonance fingerprinting (MRF) method that enables accurate quantification of both proton exchange rates and volume fractions in a fraction of the time required by conventional pulse sequences will be developed and optimized. These novel techniques exploit deep learning methods to enable the simultaneous quantification of multiple tissue maps from a single measurement. The improved CEST-MRF method will enable the acquisition of accurate pH, water T1 and T2, and protein/metabolite concentration maps in acquisition times of less than 5 minutes. The sequence will be adapted to a clinical scanner, and a novel multi- slice method will be implemented to obtain whole brain coverage (Aim 1). Next the CEST-MRF acquisition schedule will be optimized to maximize the parameter map discrimination and accuracy using a deep learning approach for the parameter map reconstruction. The parameter map reconstructions in normal human subjects will be validated with conventional CEST and test-retest studies (Aim 2). Lastly, the optimized CEST-MRF method will be used to evaluate the change in the quantitative parameter maps before and after radiation therapy to assess the potential role of CEST-MRF maps as predictive imaging biomarkers for brain metastases (Aim 3).

项目概要/摘要 化学交换饱和转移 (CEST) MRI 使用选择性射频 (RF) 脉冲使 各种分子和大分子（包括蛋白质）上可交换质子的磁化， 由于与大量水的快速化学交换，导致水 MRI 信号减弱。中欧夏令时 对比度取决于化学交换率 (kex)（对 pH 值敏感）和体积分数 对蛋白质和代谢物浓度敏感的可交换质子池（fs）。 CEST的敏感性 pH 值和蛋白质/代谢物浓度的 MRI 已被证明是对多种物质进行成像的强大工具。 疾病病理学。例如，已使用与内源性蛋白质的酰胺质子 CEST 对比 为了区分恶性胶质瘤的假性进展和真实进展，请区分放射治疗 坏死和肿瘤进展，并对肿瘤的细胞外 pH 值进行成像。然而，这些的临床转化 CEST-MRI 方法受到图像对比度的定性本质、图像采集时间长的阻碍 时间和所需的复杂数据处理。需要有效的方法来量化 kex 和 fs 生成将许多此类研究推进临床所需的高质量 pH 值和体积分数图。 在本提案中，CEST 磁共振指纹 (MRF) 方法能够实现精确定量 在传统脉冲所需时间的一小部分内同时计算质子交换率和体积分数 将开发和优化序列。这些新技术利用深度学习方法来实现 通过单次测量同时量化多个组织图。改进的CEST-MRF 方法将能够获取准确的 pH、水 T1 和 T2 以及蛋白质/代谢物浓度图 采集时间少于 5 分钟。该序列将适用于临床扫描仪和新型多功能 将实施切片方法以获得全脑覆盖（目标1）。接下来是 CEST-MRF 收购 将使用深度学习来优化时间表，以最大限度地提高参数图的辨别力和准确性 参数图重建的方法。正常人类受试者的参数图重建 将通过传统的 CEST 和重测研究进行验证（目标 2）。最后，优化后的CEST-MRF 方法将用于评估放射治疗前后定量参数图的变化 评估 CEST-MRF 图作为脑转移的预测成像生物标志物的潜在作用（目标 3）。