Real-time feedback MRI for improved reproducibility in imaging biomarkers

描述（由申请人提供）：本项目的总体目标是将先进的实时反馈技术纳入MRI方案，以提高MR衍生参数的重现性，并提高宾夕法尼亚大学各种临床程序的准确性和效率。虽然该提案侧重于骨小梁的结构微MRI，实时心脏成像和功能性神经成像领域的一些具体应用，但候选人预计该项目期间开发的实时反馈编程框架将对其他NIH资助的研究项目产生重大影响在宾夕法尼亚大学和其他地方。 候选人是一位具有计算机科学背景的数学家，他开发了一个图形化的脉冲序列编程环境，该环境已完全连接到西门子全身扫描仪。 该软件已被学生和研究人员用于开发数十种研究脉冲序列，将成为拟议的实时反馈编程框架的起点。通过与宾夕法尼亚大学的临床和生物医学研究人员的合作，候选人希望这项技术能够填补研究技术的空白，并最终对医疗保健产生重大的积极影响。 医学成像对于新药、医疗器械和程序的开发和验证变得越来越重要。磁共振成像（MRI）特别适合于这些目的，因为它是非侵入性的，对广泛的生理过程敏感。然而，该模式也具有许多固有的挑战，这些挑战通常会降低定量MRI衍生参数（成像生物标志物）的准确性或再现性。扫描过程中受试者的运动、不一致的患者体位以及其他MRI相关问题可能会阻碍检测生物标志物中响应治疗或疾病进展的微小变化的能力。实时反馈技术（包括前瞻性运动补偿和自动切片处方）可以显着提高这种测量的准确性和精度。然而，由于实施这些先进扫描技术的技术难度，实时反馈功能不适用于许多可能受益最大的临床程序。

DESCRIPTION (provided by applicant): The overall goal of this project is to incorporate advanced real-time feedback techniques into MRI protocols in order to increase reproducibility of MR-derived parameters and improve the accuracy and efficiency of a variety of clinical procedures at the University of Pennsylvania. While this proposal focuses on a few specific applications in the areas of structural micro-MRI of trabecular bone, real-time cardiac imaging, and functional neuroimaging, the candidate anticipates that the real-time feedback programming framework developed during the project will have a significant impact on other NIH funded research projects at Penn and elsewhere. The candidate, a mathematician with a background in computer science, has developed a graphical pulse sequence programming environment which has been fully interfaced to the Siemens whole-body scanners. This software, which has been used by students and researchers to develop dozens of research pulse sequences, will be the starting point for the proposed real-time feedback programming framework. Through collaborations with clinical and biomedical researchers at Penn, the candidate expects this technology to fill gaps in research techniques and ultimately have a significant positive impact on health care. Medical imaging is becoming increasingly important for the development and validation of new drugs, medical devices and procedures. Magnetic resonance imaging (MRI) is particularly suitable for these purposes because it is non-invasive and sensitive to a wide range of physiological processes. However, the modality also has a number of inherent challenges that often reduce the accuracy or reproducibility of quantitative MRI-derived parameters (imaging biomarkers). Subject motion during a scan, inconsistent patient positioning, and other MRI-related problems can hinder the ability to detect small changes in biomarkers in response to treatment or disease progression. Real-time feedback techniques (including prospective motion compensation and automatic section prescription) can significantly improve the accuracy and precision of such measurements. However, due to the technical difficulty of implementing these advanced scanning techniques, real-time feedback capabilities are not available for many of the clinical procedures that could most benefit.