Chemical Exchange Saturation Transfer MR Fingerprinting

化学交换饱和转移 MR 指纹图谱

• 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
• 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

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.

摘要 我们建议开发一种快速，定量化学交换饱和转移（CEST）成像技术， 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 其可以基于溶质不稳定质子和本体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的变体， 组织中内源性移动的蛋白质和肽的质子（-NH）。APT-MRI已成功用于 成像蛋白质含量和pH值，使肿瘤分级和分化的活动性复发肿瘤， 治疗效果然而，大多数当前使用的APT成像协议依赖于定性、定量和定量的获取。 所谓的APT加权（APTw）图像，将检测灵敏度限制为定量参数，如pH 或蛋白质浓度。目前，定量APT成像通常通过评估所谓的 Z-光谱，通过测量作为饱和频率函数的归一化水信号强度生成 在不同的射频（RF）饱和功率下进行偏移，这很耗时。因此， 需要快速、定量的APT成像技术。MRF是一种新的定量成像方法， 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，显著提高了扫描效率。MRF已成功应用 在患者研究中，评价MR弛豫时间T1和T2的范围和变化，提供初始 证明了它的临床效用。深度神经网络的最新进展为有效解决 在MRF重建中的一般反演问题，并产生高质量的组织参数估计， 高速度我们的假设是，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 使用卷积神经网络的比率;以及3）证明该技术在以下方面的初步临床实用性： 脑癌，将通过放射学引导的立体定向活检进行确认。通过定量APT 成像技术，神经胶质瘤中pH和蛋白质含量的先验知识可能有助于神经胶质瘤的分层。 患者的个性化治疗策略，并帮助监测治疗反应。