Area B: Precise DCE-MRI Assessment of Brain Tumors

B区：脑肿瘤的精确DCE-MRI 项目总结 该项目将开发和验证一种改进的动态对比增强(DCE)MRI技术，用于 评估脑瘤对治疗的反应。理论基础：从历史上看，肿瘤大小或增强的增加 肿瘤进展明显，肿瘤大小缩小有明显的治疗反应。与 新的化疗药物的出现，包括免疫治疗，简单的大小改变或增强是 不再足以做出治疗决定。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，以成像脑瘤和检测早期治疗反应。这将增强 我们延长脑瘤患者存活时间的能力传统上被认为是 预后不良组(包括复发的高级别胶质瘤和转移性黑色素瘤)，他们经常被遗漏 由于预期寿命很短，导致了临床试验的减少。创新：我们提出了特别定制的收购和 重建(STAR)DCE-MRI，其中采集和重建是根据估计- 从理论角度创建最准确和可重现的示踪剂动力学(TK)参数图，不同于 优化中间图像质量的传统方法。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，空间性能提高了36倍 分辨率和覆盖率与当前技术相比，脑肿瘤患者的图像质量没有损失； 我们期待的只会变得更好。与目前最先进的DCE-MRI相比，这项技术将提供 三大进步：1-精细(亚1毫米各向同性)空间分辨率，用于定量评估狭窄 肿瘤边缘和小病变，2-全脑覆盖，包括所有病变和周围组织 从而简化了检查，3-稳健地测量患者特定的动脉输入，这是 准确的造影剂动力学建模。方法：我们将优化、技术验证和临床应用 评估STAR DCE-MRI方法，该方法提供改进的量化参数脑图，包括血液- 脑屏障渗漏和血浆体积分数。具体地说，我们将：1-优化和技术验证 STAR DCE-MRI生成准确且可重现的TK参数图，2-生成强大的临床 实施STAR DCE-MRI，并对脑肿瘤患者进行3次临床评价， 尤其是那些使用抗血管生成剂治疗的复发的高级别胶质瘤，以及那些患有脑部的患者。 用免疫治疗剂治疗黑色素瘤转移。更广泛的影响：改进的量化多指标 参数DCE-MRI在所有神经系统疾病的评估中具有潜在的作用 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，可能会对前列腺、肾脏、乳腺和肝脏的DCE-MRI产生影响 肿瘤以及DCE-MRI之外的肿瘤。重要的是，它解决了肿瘤学中一个关键的未得到满足的需求， 一个健壮的、可重现的、高时空分辨率和高空间覆盖率的生物标志物作为潜在的目标 癌症研究中新型治疗剂的临床试验要点。

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.