Chemical Exchange Saturation Transfer MR Fingerprinting

化学交换饱和转移 MR 指纹图谱

基本信息

批准号：
10491789
负责人：
Hye Young Heo
金额：
$ 34.73万
依托单位：
JOHNS HOPKINS UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-21 至 2025-06-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10491789
关键词：
3-Dimensional Acceleration Amides Biopsy Biopsy Specimen Brain Neoplasms Chemicals Clinic Clinical Consumption Development Diagnosis Fingerprint Frequencies Genetic Glioma Goals Image Imaging Techniques Imaging technology Knowledge Magnetic Resonance Imaging Malignant neoplasm of brain Measurement Measures Methods Molecular Monitor Operative Surgical Procedures Patients Peptides Property Proteins Protocols documentation Protons Recurrent tumor Relaxation Reproducibility Research Proposals Scanning Schedule Scheme Signal Transduction Speed Standardization Techniques Technology Testing Time Tissue Sample Tissues Training Translations Variant Water Work base clinical diagnosis clinical practice convolutional neural network deep learning deep learning algorithm deep learning model deep neural network design detection sensitivity healthy volunteer human subject imaging modality improved large-scale database novel patient stratification personalized therapeutic quantitative imaging radio frequency radiological imaging reconstruction solute treatment effect treatment response tumor tumor heterogeneity

项目摘要

ABSTRACT We propose to develop a fast, quantitative chemical exchange saturation transfer (CEST) imaging technique, by integrating CEST with MR fingerprinting (MRF) and deep-learning techniques in a unified framework, with the ultimate goal of translation into routine clinical practice. CEST imaging is an important molecular MRI method that can generate contrast based on the proton exchange between solute labile protons and bulk water protons in tissue. Amide proton transfer (APT) imaging, a variant of CEST-based molecular MRI, is based on the amide protons (-NH) of endogenous mobile proteins and peptides in tissue. APT-MRI has been used successfully to image protein content and pH, enabling tumor grading and the differentiation of active recurrent tumor from treatment effects. However, most currently used APT imaging protocols depend on the acquisition of qualitative, so-called APT-weighted (APTw) images, limiting the detection sensitivity to quantitative parameters, such as pH or protein concentration. Currently, quantitative APT imaging is often attempted by assessing a so-called Z-spectrum, generated by measuring the normalized water signal intensity as a function of saturation frequency offset under varied radiofrequency (RF) saturation powers, which is time-consuming. Thus, the development of fast, quantitative APT imaging techniques is needed. MRF is a novel quantitative imaging method that simultaneously quantifies multiple tissue properties using pseudorandom acquisition parameters, and thus, significantly improves scan efficiency compared to conventional techniques. MRF has been successfully applied in patient studies to evaluate the range of and changes in MR relaxation times, T1 and T2, providing initial evidence of its clinical utility. Recent advances in deep neural networks open a new possibility to efficiently solve general inversion problems in MRF reconstruction, and to produce high-quality estimates of tissue parameters at high speed. Our hypothesis is that, by combining APT, MRF, and deep-learning techniques, we can highly accelerate image acquisition and accurately estimate the quantitative values of the tissue. Our hypotheses will be tested through three specific aims: 1) to develop a fast 3D APT-MRF sequence and design an optimal RF saturation schedule using deep-learning; 2) to quantify absolute amide proton concentrations and exchange rates using convolutional neural networks; and 3) to demonstrate the initial clinical utility of the technology in brain cancer, which will be confirmed by radiographically-guided stereotactic biopsy. Through quantitative APT imaging technology, a priori knowledge of the pH and protein content in gliomas may help in the stratification of patients into personalized therapeutic strategies and help monitor treatment response.

抽象的我们建议开发一种快速、定量的化学交换饱和转移（CEST）成像技术，通过将 CEST 与 MR 指纹识别 (MRF) 和深度学习技术集成在一个统一的框架中，最终目标是转化为常规临床实践。 CEST成像是一种重要的分子MRI方法可以根据溶质不稳定质子和大量水质子之间的质子交换产生对比度在组织中。酰胺质子转移 (APT) 成像是基于 CEST 的分子 MRI 的一种变体，以酰胺为基础组织中内源性移动蛋白和肽的质子 (-NH)。 APT-MRI已成功用于对蛋白质含量和 pH 值进行成像，从而实现肿瘤分级以及区分活动性复发肿瘤和治疗效果。然而，目前使用的大多数 APT 成像协议依赖于定性、所谓的 APT 加权 (APTw) 图像，限制了对 pH 等定量参数的检测灵敏度或蛋白质浓度。目前，定量 APT 成像通常通过评估所谓的 Z 谱，通过测量作为饱和频率函数的归一化水信号强度而生成在不同的射频（RF）饱和功率下进行偏移，这是非常耗时的。因此，发展需要快速、定量的 APT 成像技术。 MRF是一种新颖的定量成像方法使用伪随机采集参数同时量化多个组织特性，因此，与传统技术相比，显着提高了扫描效率。 MRF已成功申请在患者研究中评估 MR 弛豫时间 T1 和 T2 的范围和变化，提供初始其临床实用性的证据。深度神经网络的最新进展为有效解决问题提供了新的可能性 MRF 重建中的一般反演问题，并产生组织参数的高质量估计高速。我们的假设是，通过结合 APT、MRF 和深度学习技术，我们可以高度加速图像采集并准确估计组织的定量值。我们的假设将通过三个具体目标进行测试：1）开发快速 3D APT-MRF 序列并设计最佳 RF 使用深度学习的饱和时间表； 2) 量化绝对酰胺质子浓度和交换使用卷积神经网络进行速率； 3）展示该技术的初步临床应用脑癌，将通过放射学引导的立体定向活检来确诊。通过定量APT 成像技术，神经胶质瘤中 pH 值和蛋白质含量的先验知识可能有助于分层患者接受个性化治疗策略并帮助监测治疗反应。