Development of automated web-based spectroscopic MRI clinical interface

基于网络的自动化光谱 MRI 临床界面的开发

• 批准号: 9332618
• 负责人: Lee Cooper
• 金额: $ 23.33万
• 依托单位: EMORY UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-05-01 至 2019-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9332618
• 关键词: Address; Adult; Aging; Agreement; Algorithmic Software; Algorithms; Alzheimer' s Disease; Anatomy; Area; Atlases; Blood - brain barrier anatomy; Brain; Brain Neoplasms; Cells; Clinical; Clinical Trials; Complex; Computer software; Consensus; Contrast Media; Data; Data Analyses; Databases; Demyelinating Diseases; Detection; Development; Diagnosis; Diffuse; Diffusion; Dimensions; Disease; Dose; Enrollment; Environment; Excision; Glioblastoma; Glioma; Goals; High Performance Computing; High-LET Radiation; Hour; Hybrids; Image; Imagery; Inborn Errors of Metabolism; Infiltration; Informatics; Intervention; Intuition; Lead; Learning; Machine Learning; Magnetic Resonance Imaging; Magnetic Resonance Spectroscopy; Mainstreaming; Malignant Neoplasms; Malignant neoplasm of brain; Manuals; Maps; Measures; Medical Imaging; Metabolic; Metabolism; Methods; Microscopic; Modality; Modeling; Monitor; Morphologic artifacts; Multi-Institutional Clinical Trial; Multiple Sclerosis; Nature; Neoplasms; Neurodegenerative Disorders; Neurosurgeon; Oncologist; Online Systems; Operative Surgical Procedures; Patients; Play; Primary Brain Neoplasms; Process; Quality Control; Radiation; Radiation Dosage; Radiation Oncologist; Radiation therapy; Reader; Recurrence; Reporting; Research; Resolution; Role; Software Framework; Software Tools; Stroke; Surgically-Created Resection Cavity; Techniques; Testing; Three-Dimensional Image; Time; Training; Traumatic Brain Injury; Water; Work; anatomic imaging; base; chemotherapy; clinical application; clinical imaging; clinical practice; clinically relevant; contrast enhanced; design; diagnosis evaluation; expectation; high risk; image processing; imaging modality; imaging platform; improved; innovation; magnetic resonance spectroscopic imaging; neovasculature; neuropathology; neurosurgery; novel; outcome forecast; precision medicine; prognostic; software development; spectroscopic data; success; tool; tumor; user-friendly

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.

胶质母细胞瘤（GBM）是最常见的成人原发性脑肿瘤，其病程具有高度侵袭性。 胶质瘤细胞向周围正常脑的浸润使GBM的治愈性手术切除变得不可能， 几乎所有这些最终都会复发。因此，放射治疗（RT）策略具有非凡的意义， 这已被证明是有效的，但需要强有力的成像证据来指导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，其仅识别 与高级别肿瘤相关的渗漏新生血管，以及T2加权MRI，其对肿瘤不具有特异性 浸润通过神经外科的进步，现在可以实现完全或接近完全切除 在许多情况下，CE肿瘤成分的比例很低;因此，用最高RT剂量治疗的区域仅限于 空的切除腔加上一小块边缘。由于T2区域通常较大， 在疾病状态下，它被治疗到较小的“微观疾病”剂量。然而，很多时候， 疾病剂量不足以控制肿瘤。光谱磁共振成像（sMRI）提供了一个高度敏感的 以及识别这些区域的具体方法，尽管由于缺乏有效的方法，sMRI尚未用于RT计划。 缺乏用于分析、显示和管理sMRI数据的临床决策支持软件。三个关键 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） 相对于患者的基线代谢定量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、3D图像集的sMRI光谱，用于直接配准 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，需要花费数小时且不切实际 适合在快节奏的临床RT环境中常规使用。为了自动化这个管道，并提供临床上有用的 信息辐射肿瘤学家，我们寻求开发一个软件框架的自动化和权宜之计 用于RT计划的sMRI处理。我们将利用高性能领域的新进展， 计算和深度学习。具体来说，我们将开发算法过滤器，用于识别（和消除） 光谱伪影、用于肿瘤浸润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。成功的工作将产生 一个自动化的“扫描仪到临床医生”平台，用于定量、方便和客观的分析方法， 将sMRI整合到常规临床应用中。该工具在基于MRS的诊断中也将非常有价值 以及许多其他神经病理学的评估，包括其他原发性（和转移性）脑肿瘤， 中风，多发性硬化症（和其他脱髓鞘疾病），先天性代谢缺陷， 神经退行性疾病