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).

项目总结/摘要 化学交换饱和转移（CEST）MRI使用选择性射频（RF）脉冲来饱和 可交换质子在各种分子和大分子，包括蛋白质， 由于与大量水的快速化学交换，这导致水MRI信号降低。CEST 对比度取决于化学交换速率（kex），其是pH敏感的，并且对比度取决于化学交换速率（kex）的体积分数。 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 MRI对pH值和蛋白质/代谢物浓度的测量已被证明是用于成像广泛的 疾病病理学例如，已经使用来自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进展和真进展， 坏死和肿瘤进展，并成像肿瘤的细胞外pH值。然而，这些临床翻译 CEST-MRI方法一直受到图像对比度的定性性质、长时间图像采集的阻碍 时间，以及所需的复杂数据处理。需要用于量化kex和fs的有效方法， 生成高质量的pH和体积分数图，将这些研究推向临床。 在该提案中，CEST磁共振指纹（MRF）方法， 质子交换率和体积分数在一小部分的时间所需的常规脉冲 序列将被开发和优化。这些新技术利用深度学习方法， 从一次测量中同时量化多个组织图。改进的CEST-MRF 该方法将能够获得准确的pH值、水T1和T2以及蛋白质/代谢物浓度图 采集时间小于5分钟。该序列将适用于临床扫描仪，以及一种新型的多功能扫描仪。 将实施切片方法以获得全脑覆盖（目标1）。CEST-MRF收购 时间表将被优化，以最大限度地提高参数图的歧视和准确性，使用深度学习 参数图重建的方法。正常人参数图的重建 将通过常规CEST和重测研究进行验证（目标2）。最后，优化的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图作为脑转移瘤预测性成像生物标志物的潜在作用（目的3）。