Improving Image-Guided Radiation Therapy of Gliomas with High-Resolution MR Spectroscopic Imaging

摘要 胶质瘤占所有原发恶性脑肿瘤的80%。新诊断患者的现行标准治疗方法 胶质瘤包括最大限度的手术切除、放射治疗(RT)和化疗。一项关键技术 放射治疗计划中的挑战是准确地描绘胶质瘤的靶区体积。当前的临床 靶区勾画指南主要依赖于结构磁共振成像(MRI)图像。 大体肿瘤体积(GTV)是基于增强后的T1加权MRI和T2加权MRI来定义的。 然而，单纯的结构磁共振成像缺乏对真实肿瘤边界的特异性。相应地，临床 目标体积(CTV)通常定义为GTV加上较大的边距(例如，20-25 mm)以说明可能的情况 显微镜下的渗透。结构磁共振缺乏特异性是限制研究和治疗的关键因素 新的放疗技术的临床应用，以获得更好的临床结果。磁共振波谱成像(Mrsi)由来已久。 被认为是一种潜在的强大的无标记脑肿瘤分子成像工具。在最近的阶段中 I临床试验，MRSI用于指导多形性胶质母细胞瘤患者放射治疗的剂量递增，显示非常 有希望的初步结果。尽管核磁共振成像的一般临床应用因其局限性而受到阻碍 空间分辨率和长扫描时间，在解决这些技术方面取得了重大进展 使用先进的数据采集和处理方法在过去十年中面临的挑战。我们组有 成功开发了一种功能强大的MRSI技术，称为SPICE(利用 空间谱相关)。SPICE有效地集成了快速扫描、稀疏采样、量子模拟 分子共振结构和机器学习，以实现快速高分辨率核磁共振成像。初步 我们和其他小组的结果表明，SPICE具有令人兴奋的潜力，可以实现前所未有的 结合分辨率、速度和信噪比进行代谢成像。我们还首次展示了， 利用非抑制水绘制脑组织T1、T2和质子密度参数的可行性研究 来自调味品扫描的信号。该项目的主要目标是利用MRSI中的这一重大进步 技术和调查使用高分辨率新陈代谢和结构信息以实现更多 精确的靶区勾画可用于脑胶质瘤的放射治疗计划。我们将：1)进一步发展和优化 SPICE在临床环境下用于MRI/MRSI引导的脑胶质瘤RT，2)执行系统的性能评估 所提出的方法适用于幻影、健康受试者和胶质瘤患者，以及3)调查代谢指标的使用 以及用于描绘生物靶体积的结构生物标记物，以改进图像引导的胶质瘤RT。这个 所提出的研究在开发一种新的分子成像技术方面具有创新性，并在 用定量代谢和结构生物标记物改进脑胶质瘤的放射治疗计划。成功 该项目的完成将对胶质瘤的图像引导放射治疗产生重大影响，开辟新的 应用剂量递增放疗更好地控制脑胶质瘤患者复发的机会。

ABSTRACT Glioma make up 80% of all primary malignant brain tumors. The current standard treatment for newly diagnosed gliomas includes maximal surgical resection, radiation therapy (RT), and chemotherapy. A key technical challenge in RT treatment planning is accurate target volume delineation of gliomas. The current clinical guidelines for target volume delineation rely primarily on structural Magnetic Resonance Imaging (MRI) images. Gross Tumor Volume (GTV) is defined based on contrast-enhanced T1-weighted MRI and T2-weighted MRI. However, structural MRI alone lacks specificity for delineation of true tumor boundaries. Accordingly, Clinical Target Volume (CTV) is often defined as the GTV plus a large margin (e.g., 20-25 mm) to account for possible microscopic infiltration. The lack of specificity of structural MRI is a critical factor limiting the investigation and clinical application of new RT techniques for better clinical outcome. MR spectroscopic imaging (MRSI) has long been recognized as a potentially powerful tool for label-free molecular imaging of brain tumor. In a recent Phase I clinical trial, MRSI is used to guide dose escalation in RT for Glioblastoma multiforme patients, showing very promising preliminary results. Although general clinical applications of MRSI have been impeded by its limited spatial resolution and long scan time, significant progresses have been made in addressing these technical challenges over the past decade using advanced data acquisition and processing methods. Our group have successfully developed a powerful MRSI technology, known as SPICE (SPectroscopic Imaging by exploiting spatiospectral CorrElation). SPICE effectively integrates rapid scanning, sparse sampling, quantum simulation of molecule resonance structures, and machine learning to enable rapid high-resolution MRSI. Preliminary results by our and other groups have shown an exciting potential of SPICE to achieve an unprecedented combination of resolution, speed, and SNR for metabolic imaging. We have also demonstrated, for the first time, the feasibility of mapping T1, T2 and proton-density parameters of brain tissues using the unsuppressed water signals from the SPICE scans. The primary goal of this project is to leverage this significant advance in MRSI technology and investigate the use of high-resolution metabolic and structural information to achieve more accurate target volume delineation for RT treatment planning of gliomas. We will: 1) further develop and optimize SPICE for MRI/MRSI-guided RT of gliomas in clinical settings, 2) perform systematic performance evaluation of the proposed method on phantoms, healthy subjects, and glioma patients, and 3) investigate the use of metabolic and structural biomarkers for delineation of biological target volume to improve image-guided RT of gliomas. The proposed research is innovative in developing a novel molecular imaging technology and a timely effort on improving RT treatment planning of gliomas with quantitative metabolic and structural biomarkers. Successful completion of the project will have a significant impact on image-guided RT for gliomas, opening up new opportunities for better control of recurrence in glioma patients using dose escalated RT.