Automatic Three Dimensional (3D) Registration for Enhanced Cancer Management

描述（由申请人提供）：本项目拨款申请的总体目标是通过支持准确，早期检测和测量乳腺癌对化疗的反应（项目1,3），以及通过开发，应用和测试强大而复杂的注册和相关信号处理工具来支持手术前运动和颞叶语言皮层脑功能评估（项目2），从而改变当前的临床范例。项目1研究了通过体积、扩散和动态增强MRI对乳腺癌患者新辅助化疗或激素治疗早期评估和测量反应的有效性。假设间隔乳房检查的非线性配准增加了功能扩散映射（fDM）的敏感性和特异性以及动态对比度增强（DCE）的准确性。开发低噪音、无偏倚的工具来评估病变对治疗的反应是目前的一个重要课题。项目2扩展了先前完成的基于注册的fMRI运动研究，研究了将我们独特的运动校正方法与不同的fMRI采集协议（如集群采集）相结合的好处，以改善与患者的沟通和反应监测。项目3解决了与一般成像相关的动态MRI的基本模糊问题：对于任何给定的技术，我们要么可以获得高空间或时间分辨率的成像数据，但不能两者兼得。在MRI动态成像过程中，支持控制和优化这些权衡的通用技术非常重要。

DESCRIPTION (provided by applicant): The overarching goal of this program project grant application is to change current clinical paradigms through the support of accurate, early detection and measurement of breast cancer response to chemotherapy (Projects 1, 3), and presurgical motor and temporal language cortex evaluation of brain function (Project 2) through the development, application and testing of robust and sophisticated registration and related signal processing tools. Project 1 investigates the efficacy of early-assessment and measurement of response to neoadjuvant chemo or hormonal therapy for patients with breast cancer obtained through the use of volumetric, diffusion and dynamic contrast enhancement MRI. The hypothesis is that nonlinear registration of interval breast exams increases the sensitivity and specificity of functional diffusion mapping (fDM) as well as the accuracy of dynamic contrast enhancement (DCE). Developing low noise, unbiased tools for assessing lesion response to therapy is currently an important topic. Project 2 extends previously completed work on registration-based fMRI motion by examining the benefits of combining our unique motion correction method with different fMRI acquisition protocols, e.g. clustered acquisition, to improve communication with the patient and response monitoring. Project 3 addresses the fundamental ambiguity problem in dynamic MRI associated with imaging in general: for any given technique either we can obtain high spatial or temporal resolution imaging data, but not both. Generalized techniques that support controlling and optimizing these tradeoffs during dynamic imaging in MRI are very important.