Towards the automation of MR spectroscopic imaging in patients with glioblashoma

胶质母细胞瘤患者磁共振波谱成像的自动化

基本信息

批准号：
9191930
负责人：
Saumya Gurbani
金额：
$ 4.36万
依托单位：
EMORY UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2016
资助国家：
美国
起止时间：
2016-06-24 至 2021-06-23
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9191930
关键词：
Accounting Address Adoption Adult Algorithmic Software Algorithms Automation Benchmarking Brain Brain Neoplasms Cells Clinical Clinical Data Clinical Management Clinical Trials Complement Computational Technique Computer software Consensus Contrast Media Data Data Analyses Data Set Databases Detection Diagnosis Diagnostic Diffusion Disease Effectiveness Enrollment Excision Gaussian model Genomics Glioblastoma Glioma Goals Gold High Performance Computing Histology Hour Image Infiltration Intervention Knowledge Lead Learning Least-Squares Analysis Machine Learning Magnetic Resonance Magnetic Resonance Imaging Magnetic Resonance Spectroscopy Malignant Neoplasms Malignant neoplasm of brain Maps Measures Medical Medical Imaging Metabolic Metabolism Methods Modality Modeling Monitor Morphologic artifacts Nature Neoplasms Oncologist Online Systems Outcome Pathologic Patients Performance Physicians Play Population Primary Brain Neoplasms Process Radiation Radiation Dosage Radiation therapy Reader Recurrence Role Scheme Seeds Signal Transduction Software Framework T2 weighted imaging Techniques Time Tissues Training Translating Universities Variant Weight Work base chemotherapy clinical application contrast enhanced dosage expectation histological image image processing imaging modality improved learning network magnetic resonance spectroscopic imaging molecular imaging neoplastic neoplastic cell neovascularization neovasculature novel outcome forecast personalized diagnostics personalized medicine precision medicine predictive modeling quantitative imaging response software development spectroscopic imaging success syntax tool treatment planning tumor tumor growth white matter

Accounting Address Adoption Adult Algorithmic Software Algorithms Automation Benchmarking Brain Brain Neoplasms Cells Clinical Clinical Data Clinical Management Clinical Trials Complement Computational Technique Computer software Consensus Contrast Media Data Data Analyses Data Set Databases Detection Diagnosis Diagnostic Diffusion Disease Effectiveness Enrollment Excision Gaussian model Genomics Glioblastoma Glioma Goals Gold High Performance Computing Histology Hour Image Infiltration Intervention Knowledge Lead Learning Least-Squares Analysis Machine Learning Magnetic Resonance Magnetic Resonance Imaging Magnetic Resonance Spectroscopy Malignant Neoplasms Malignant neoplasm of brain Maps Measures Medical Medical Imaging Metabolic Metabolism Methods Modality Modeling Monitor Morphologic artifacts Nature Neoplasms Oncologist Online Systems Outcome Pathologic Patients Performance Physicians Play Population Primary Brain Neoplasms Process Radiation Radiation Dosage Radiation therapy Reader Recurrence Role Scheme Seeds Signal Transduction Software Framework T2 weighted imaging Techniques Time Tissues Training Translating Universities Variant Weight Work base chemotherapy clinical application contrast enhanced dosage expectation histological image image processing imaging modality improved learning network magnetic resonance spectroscopic imaging molecular imaging neoplastic neoplastic cell neovascularization neovasculature novel outcome forecast personalized diagnostics personalized medicine precision medicine predictive modeling quantitative imaging response software development spectroscopic imaging success syntax tool treatment planning tumor tumor growth white matter

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项目摘要

Glioblastoma is the most common adult primary brain tumor and is highly aggressive in its disease course. Despite advances in neurosurgical resection, radiation targeting, and chemotherapy, the prognosis remains grim with a median survival of just 15 months. The effectiveness of current radiation therapy strategies is severely limited by shortcomings in the imaging modalities used to develop treatment plans. Current radiation therapy planning is mainly based on contrast-enhanced T1-weighted MRI, which identifies high grade tumors that are immediately associated with leaky neovasculature. Although it is an excellent diagnostic tool to identify high grade from low grade tumors, it is unable to signal occult infiltration beyond the core of the tumor. Though many believe GBM to be an incurable disease, we believe we have identified a method for optimizing tumor targeting that will increase the effectiveness of radiation therapy. A significant component of the current problem in GBM therapy is the lack of treatment for non-enhancing regions that are significantly infiltrated by neoplastic glioma cells without neovascularization. This untreated population undoubtedly leads to early recurrence. The proposed study addresses an important step toward translating an advanced quantitative imaging modality that complements the conventional imaging that is capable of reliably revealing glioma- infiltrated regions for precise, personalized treatment targeting. Proton spectroscopic magnetic resonance imaging (sMRI) is an alternative modality able to identify endogenous metabolism within tissue without the need for exogenous contrast, and has been shown to identify the metabolic abnormalities associated with tumor beyond the regions identified by T1-weighted MRI. The clinical integration of sMRI in patient management has been limited due to the computational challenges of analysis of sMRI data. Two key hurdles to be overcome are the insufficiency of filters to remove image artifacts and the necessity of quantification of metabolic levels relative to a patient's baseline metabolism. As a result, sMRI processing requires skilled user intervention and many hours of computational and user time. To automate this pipeline and provide clinically useful information to oncologists, we seek to develop a software framework for the automated and expedient processing of sMRI for use in radiation therapy planning. We will use novel advances in the fields of high performance computing and deep learning, an approach to computation that has shattered benchmarks in many medical and non-medical problems. Specifically, we will develop filters for removing artifacts, algorithms for personalized diagnosis of tumor infiltration, and explore deep learning as a method to synthesize sMRI data with anatomical and clinical metrics in a fully automated fashion. Success in the proposed work will produce a “scanner-to-clinician” platform for quantitative, expedient, and objective analysis methods to integrate sMRI into the clinical radiation therapy planning paradigm. Ultimately, we believe this additional modality in the physician's tool belt will lead to better outcomes in patients suffering from this debilitating disease.

胶质母细胞瘤是最常见的成年原发性脑肿瘤，其疾病病程高度侵略性。尽管神经外科切除，辐射靶向和化学疗法的进步，预后仍然存在严峻的中位生存仅15个月。当前放射治疗策略的有效性是受到制定治疗计划的成像方式的缺点受到严重限制。电流辐射治疗计划主要基于对比增强的T1加权MRI，该MRI识别高级肿瘤立即与泄漏的新生血管系统有关。虽然这是一个出色的诊断工具低级肿瘤的高级别无法向肿瘤核心以外的神秘浸润发出信号。尽管许多人认为GBM是一种无法治愈的疾病，我们认为我们已经确定了一种优化肿瘤的方法靶向将提高放射治疗的有效性。电流的重要组成部分 GBM疗法中的问题是缺乏针对非增强区域的治疗肿瘤神经胶质瘤细胞，没有新血管形成。这个未经处理的人口无疑会导致早期复发。拟议的研究探讨了翻译高级定量的重要一步成像方式完成了传统成像，该成像能够可靠地揭示神经胶质瘤 - 精确的个性化治疗靶向渗透区域。质子光谱磁共振成像（SMRI）是一种替代方式，能够鉴定组织内的内源代谢而没有需要外源性对比，并已证明可以识别与通过T1加权MRI确定的区域之外的肿瘤。 SMRI在患者中的临床整合由于SMRI数据分析的计算挑战，管理人员受到限制。两个关键障碍要克服代谢水平相对于患者的基线代谢。结果，SMRI处理需要熟练的用户干预以及数小时的计算和用户时间。自动化这条管道并在临床上提供向肿瘤学家提供了有用的信息，我们试图为自动化和平稳的将SMRI处理用于放射治疗计划。我们将在高领域中使用新颖的进步绩效计算和深度学习，一种计算方法，其基准破坏了许多医学和非医疗问题。特别是，我们将开发用于删除文物，算法的过滤器用于个性化肿瘤浸润的诊断，并探索深度学习作为合成SMRI数据的方法具有完全自动化的方式的解剖学和临床指标。拟议工作的成功将产生用于整合SMRI的定量，权宜和客观分析方法的“扫描仪到阵容”平台进入临床放射治疗计划范例。最终，我们相信物理学的工具带将导致患有这种使人衰弱的疾病的患者更好地结局。