Area B: Precise DCE-MRI Assessment of Brain Tumors

B区：脑肿瘤的精确DCE-MRI 项目摘要 该项目将开发和验证一种改进的动态对比增强（DCE）MRI技术， 评估脑瘤对治疗的反应依据：从历史上看，肿瘤大小增加或增强 肿瘤大小的减小表示肿瘤进展，而肿瘤大小的减小表示治疗反应。与 新的化疗药物的出现，包括免疫疗法，简单的大小改变或增强， 已经不足以做出治疗决定了。我们相信，改进的DCE-MRI方法可以提供 新的和强大的生物标志物成像脑肿瘤和检测早期反应的治疗。这将增强 我们能够延长更高比例的脑肿瘤患者的生存期， 可怕的预后组（包括复发性高级别胶质瘤和转移性黑色素瘤），他们经常被排除在外 临床试验的数量非常少创新：我们提出特别定制的收购和 重建（星星）DCE-MRI，其中采集和重建是根据估计量身定制的- 从理论的角度来创建最准确和可再现的示踪剂动力学（TK）参数图， 优化中间图像质量的常规方法。我们将全面整合TK模式 进行DCE-MRI采集和重建。我们的初步数据显示，在空间上有36倍的改善， 与当前技术相比，分辨率和覆盖范围更大，在脑肿瘤患者中不会损失图像质量; 我们只希望变得更好。与当前最先进的DCE-MRI相比，该技术将提供 三大进步：1-精细（亚1毫米各向同性）的空间分辨率，以定量评估狭窄 肿瘤边缘和小病灶，2-全脑覆盖，包括所有病灶和所有周围组织 从而简化了检查，3-对患者特定动脉输入的鲁棒测量， 准确的造影剂动力学建模。方法：我们将进行优化、技术验证和临床试验。 评价星星DCE-MRI方法，该方法提供了改进的定量参数脑图，包括血液- 脑屏障渗漏和血浆体积分数。具体而言，我们将：1-优化和技术验证 星星DCE-MRI生成准确且可重现的TK参数图，2-生成稳健的临床 在脑肿瘤患者中实施星星DCE-MRI和3-临床评价的星星DCE-MRI， 特别是那些用抗血管生成剂治疗的复发性高级别胶质瘤患者，以及那些脑胶质瘤患者， 用免疫治疗剂治疗的黑素瘤转移。更广泛的影响：改善量化的多 参数DCE-MRI在评估所有神经系统疾病中具有潜在的作用， 微血管成分这项技术工作，特别是利用特定的非线性时间模型， 在图像重建过程中，可能对前列腺、肾脏、乳腺和肝脏的DCE-MRI产生影响 肿瘤以及DCE-MRI之外。重要的是，它解决了肿瘤学中一个关键的未满足的需求， 作为潜在目标的稳健、可再现、高时空分辨率和高空间覆盖率的生物标志物 癌症研究中新型治疗药物的临床试验点。

AREA B: PRECISE DCE-MRI OF BRAIN TUMORS PROJECT SUMMARY This project will develop and validate an improved dynamic contrast-enhanced (DCE) MRI technique for assessing brain tumor response to therapy. Rationale: Historically, increases in tumor size or enhancement have signified tumor progression, and decreases in tumor size have signified treatment response. With the advent of novel chemotherapy agents, including immunotherapy, simple changes in size or enhancement are no longer sufficient to make treatment decisions. We believe that improved DCE-MRI methods can provide new and powerful biomarkers to image brain tumors and detect early response to therapy. This will enhance our ability to prolong survival in a higher proportion of brain tumor patients traditionally regarded as the most dire prognostic group (including recurrent high-grade glioma and metastatic melanoma), who are often left out of clinical trials due to very short life expectancy. Innovation: We propose Specially Tailored Acquisition and Reconstruction (STAR) DCE-MRI, in which acquisition and reconstruction are tailored from an estimation- theoretic point of view to create the most accurate and reproducible tracer kinetic (TK) parameter maps, unlike conventional approaches that optimize the quality of intermediate images. We will fully integrate TK models with DCE-MRI acquisition and reconstruction. Our preliminary data shows 36-fold improvement in spatial resolution and coverage compared to current techniques, with no loss of image quality in brain tumor patients; and we expect to only get better. Compared to current state-of-the-art DCE-MRI, this technique will provide three major advances: 1- exquisite (sub-1 mm isotropic) spatial resolution to quantitatively assess narrow tumor margins and small lesions, 2- whole-brain coverage including all lesions and all surrounding tissue thereby simplifying the exam, 3- robust measurement of patient specific arterial inputs which are required for accurate contrast agent kinetic modeling. Approach: We will optimize, technically validate, and clinically evaluate STAR DCE-MRI method that provides improved quantitative parametric brain maps including blood- brain barrier leakage and fractional plasma volume. Specifically, we will: 1- optimize and technically validate STAR DCE-MRI to produce accurate and reproducible TK parameter maps, 2- produce a robust clinical implementation of STAR DCE-MRI, and 3- clinically evaluate STAR DCE-MRI in patients with brain tumors, specifically those with recurrent high-grade glioma treated with an anti-angiogenic agent, and those with brain melanoma metastases treated with an immunotherapy agent. Broader Impact: Improved quantitative multi- parametric DCE-MRI has a potential role in the assessment of all neurologic diseases that have a microvascular component. This technical work, particularly leveraging specific nonlinear temporal models during image reconstruction, is likely to have implications for DCE-MRI of prostate, renal, breast, and liver tumors as well as outside of DCE-MRI. Importantly it addresses a critical unmet need in oncology in providing a robust, reproducible, high spatio-temporal resolution and high spatial coverage biomarker as a potential end point for clinical trials of novel therapeutic agents in cancer research.