Real-time feedback MRI for improved reproducibility in imaging biomarkers

该项目的总体目标是将先进的实时反馈技术纳入MRI协议 为了提高MR衍生参数的再现性并提高测量的准确性和效率 宾夕法尼亚大学的各种临床程序。虽然这项建议侧重于几个具体的 在骨小梁的结构微MRI、实时心脏成像和功能性MRI领域的应用 神经影像学，候选人预计，实时反馈编程框架开发 在该项目期间，将对宾夕法尼亚大学的其他NIH资助的研究项目产生重大影响， 其他地方 这位候选人是一位具有计算机科学背景的数学家， 序列编程环境，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，将是所提出的实时反馈编程框架的起点。通过 该候选人与宾夕法尼亚大学的临床和生物医学研究人员合作，希望这项技术能够填补 研究技术的差距，并最终对医疗保健产生重大的积极影响。 医学成像对于新药的开发和验证变得越来越重要， 医疗器械和程序。磁共振成像（MRI）特别适用于这些 目的，因为它是非侵入性的，对广泛的生理过程敏感。但 模态还具有许多固有的挑战，这些挑战常常降低 定量MRI衍生参数（成像生物标志物）。扫描期间受试者运动，不一致 患者定位和其他MRI相关问题可能会阻碍检测微小变化的能力， 生物标志物对治疗或疾病进展的反应。实时反馈技术（包括 前瞻性运动补偿和自动切片处方）可以显著提高准确性 以及这种测量的精度。然而，由于实施这些技术困难， 先进的扫描技术，实时反馈能力不适用于许多临床 最能受益的程序。

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 reproduciblity 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.