Towards the automation of MR spectroscopic imaging in patients with glioblashoma

胶质母细胞瘤是最常见的成人原发脑肿瘤，在其病程中具有很高的侵袭性。 尽管在神经外科切除、放射靶向和化疗方面取得了进展，但预后仍然存在。 中位生存期只有15个月。目前放射治疗策略的有效性是 严重受限于用于制定治疗计划的成像方式的缺陷。电流辐射 治疗计划主要基于对比剂增强的T1加权MRI，它可以识别高级别肿瘤 立即与泄漏的新生血管联系在一起。尽管它是一个很好的诊断工具，可以用来识别 从低级别肿瘤到高级别肿瘤，它不能显示肿瘤核心之外的隐匿性浸润。尽管 许多人认为基底膜是一种不治之症，我们相信我们已经找到了一种优化肿瘤的方法 以此为目标将增加放射治疗的有效性。海流的一个重要组成部分 基底膜治疗的问题是缺乏对非强化区域的治疗，这些区域被显著地渗透到 肿瘤性胶质瘤细胞，无新生血管。这种未经治疗的人口无疑会导致早期 复发。拟议的研究是向翻译高级数量词迈出的重要一步 成像方式是对传统成像的补充，能够可靠地显示脑胶质瘤- 渗透区域，用于精确、个性化的治疗靶向。质子波谱磁共振 成像(SMRI)是一种替代方法，能够识别组织内的内源性代谢，而不需要 需要外源性对比，并已被证明可以识别与 在T1加权MRI所确定的区域之外的肿瘤。磁共振成像在患者中的临床整合 由于sMRI数据分析的计算挑战，管理一直受到限制。两个关键障碍 需要克服的是滤波器去除图像伪影的不足以及对图像伪影进行量化的必要性 相对于患者基线代谢的代谢水平。因此，sMRI处理需要熟练的用户 干预以及许多小时的计算和用户时间。使这条管道自动化，并在临床上提供 对于肿瘤学家有用的信息，我们寻求开发一种自动化和便捷的软件框架 用于放射治疗计划的sMRI处理。我们将利用高科技领域的新进展 性能计算和深度学习，这种计算方法已经打破了 许多医疗和非医疗问题。具体地说，我们将开发过滤器来去除伪像、算法 用于肿瘤浸润性的个性化诊断，并探索深度学习作为合成sMRI数据的方法 以全自动的方式进行解剖和临床测量。拟议工作的成功将产生 “扫描者到临床医生”平台，提供定量、便捷和客观的分析方法，以整合sMRI 纳入临床放射治疗计划范型。归根结底，我们认为这种额外的方式 医生的工具带将为患有这种令人衰弱的疾病的患者带来更好的结果。

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.