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.

抽象的 我们建议通过通过 将CEST与MR指纹（MRF）和深度学习技术集成到统一框架中， 转化为常规临床实践的最终目标。 CEST成像是重要的分子MRI方法 可以根据溶质不稳定质子和块状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，基于酰胺 内源性移动蛋白和组织中肽的质子（-NH）。 APT-MRI已成功使用 图像蛋白含量和pH，使肿瘤分级以及活性复发肿瘤的分化 治疗效果。但是，当前大多数使用的APT成像协议取决于定性的获取 所谓的适当加权（APTW）图像，限制了检测对定量参数的敏感性，例如pH 或蛋白质浓度。目前，通常通过评估所谓的，经常尝试定量APT成像 Z谱，通过测量归一化水信号强度随饱和频率的函数而产生 在不同的射频（RF）饱和功率下的偏移，这是耗时的。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 同时使用伪辅助获取参数量化多个组织特性，从而 与常规技术相比，显着提高了扫描效率。 MRF已成功应用 在评估MR松弛时间T1和T2的范围和变化的患者研究中，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 MRF重建中的一般反转问题，并在组织参数上产生高质量的估计。 高速。我们的假设是，通过结合APT，MRF和深度学习技术，我们可以高度高度 加速图像采集并准确估计组织的定量值。我们的假设会 通过三个特定目的进行测试：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并交换 使用卷积神经网络的比率； 3）证明该技术的最初临床实用性 脑癌，将通过射线照相引导的立体定向活检证实。通过定量公寓 成像技术，对神经胶质瘤中pH和蛋白质含量的先验知识可能有助于分层 患者成为个性化的治疗策略，并有助于监测治疗反应。