Development of automated web-based spectroscopic MRI clinical interface

胶质母细胞瘤(GBM)是最常见的成人原发脑肿瘤，在其病程中具有高度侵袭性。 胶质瘤细胞向周围正常脑组织的渗透使根治性手术切除基底膜变得不可能。 几乎所有的疾病最终都会复发。因此，放射治疗(RT)策略被赋予了非凡的意义， 这些方法已被证明是有效的，但需要强有力的影像证据来指导RT计划。目前 采用的临床成像方式包括T1加权对比增强(CE)MRI，它只能识别 与高级别肿瘤相关的渗漏的新生血管，以及T2加权磁共振成像，这不是肿瘤的特异性 渗透。通过神经外科的进步，现在实现完全或接近完全切除是可能的。 在许多情况下，CE肿瘤成分；因此，用最高RT剂量治疗的区域被限制为 空的切除腔外加一小块边缘。由于T2区域的面积一般较大，并且未知 在疾病的状态下，它被治疗到较小的“微观疾病”剂量。然而，很多时候，这种微观的 疾病剂量不足以控制肿瘤。光谱磁共振成像(Smri)提供了高度敏感的 以及识别这些区域的具体手段，尽管由于 缺乏用于分析、显示和管理sMRI数据的临床决策支持软件。三把钥匙 需要克服的障碍是：1)缺乏自动、快速和可靠的光谱质量控制方法；2) 根据患者的基线代谢量化代谢物水平的必要性；以及3)临床医生- 友好地显示编码为高分辨率、连续的3D图像集的sMRI频谱，以便直接配准 并纳入RT规划过程。目前，sMRI处理需要熟练的用户干预 以及在多个工具之间管理数据，导致复杂的工作流程耗时数小时且不切实际 用于在快节奏的临床RT环境中的常规使用。使这条管道自动化，并提供临床上有用的 为了给放射肿瘤学家提供信息，我们寻求开发一个自动化和便捷的软件框架 用于RT计划的sMRI处理。我们将在高性能领域使用新的进展 计算和深度学习。具体地说，我们将开发用于识别(和消除)的算法过滤器 光谱伪影、用于肿瘤渗透的个性化定位的算法、以及用于 RT计划和审查所需的sMRI数据的体积显示。拟议的工作取得成功将产生 一个自动化的“扫描仪到临床医生”平台，提供定量、便捷和客观的分析方法 将sMRI整合到常规临床应用中。该工具在基于MRS的诊断中也将具有很高的价值 以及对许多其他神经病理学的评估，包括其他原发(和转移性)脑肿瘤， 中风，多发性硬化症(和其他脱髓鞘疾病)，先天代谢障碍，以及 神经退行性疾病。

Glioblastoma (GBM) is the most common adult primary brain tumor and is highly aggressive in its disease course. Infiltration of glioma cells into surrounding normal brain make curative surgical resection of GBM impossible, and almost all will eventually recur. Thus, extraordinary significance is placed on radiation therapy (RT) strategies, which have been shown to be effective, but require strong imaging evidence to guide RT planning. Currently employed clinical imaging modalities include T1-weighted contrast-enhanced (CE) MRI, which only identifies leaky neovasculature associated with high grade tumor, and T2-weighted MRI, which is not specific for tumor infiltration. Through advances in neurosurgery, it is now possible to achieve complete or near-complete resection of the CE tumor component in many cases; thus, the region that is treated with the highest RT dose is limited to the empty resection cavity plus a small margin. Due to the generally larger size of the T2 area and unknown status of disease, it is treated to a lesser “microscopic disease” dose. Many times, however, this microscopic disease dose is inadequate to control the tumor. Spectroscopic MR imaging (sMRI) provides a highly sensitive and specific means of identifying these regions, although sMRI has not yet seen use in RT planning due to a lack of clinical decision support software for the analysis, display, and management of sMRI data. Three key hurdles to be overcome are: 1) lack of an automatic, fast and reliable method for spectral quality control; 2) the necessity of quantification of metabolite levels relative to a patient's baseline metabolism; and 3) a clinician- friendly display of the sMRI spectra encoded as a high-resolution, continuous, 3D image set for direct registration and incorporation into the RT planning process. Currently, sMRI processing requires skilled user intervention and shepherding data between several tools, resulting in a complex workflow that takes hours and is impractical for routine use in a fast-paced clinical RT environment. To automate this pipeline and provide clinically useful information to radiation oncologists, we seek to develop a software framework for the automated and expedient processing of sMRI for use in RT planning. We will use novel advances in the fields of high performance computing and deep learning. Specifically, we will develop algorithmic filters for identifying (and eliminating) spectral artifacts, algorithms for personalized localization of tumor infiltration, and methods and interfaces for the volumetric display of sMRI data needed for RT planning and review. Success in the proposed work will produce an automated “scanner-to-clinician” platform for quantitative, expedient, and objective analysis methods to integrate sMRI into routine clinical applications. This tool will also be highly valuable in the MRS-based diagnosis and evaluation of numerous other neuropathologies, including other primary (and metastatic) brain tumors, stroke, multiple sclerosis (and other demyelinating diseases), inborn errors of metabolism, and neurodegenerative diseases.