TR&D 1: Reimagining the Future of Scanning: Intelligent image acquisition, reconstruction, and analysis

TRD1项目总结 我们高级成像创新与研究中心(CAI2R)的广泛使命是为 合作翻译研究团队共同开发高影响力的生物医学成像 技术，最终目标是改变日常临床实践。技术研究和 开发(研发)项目1旨在用以下内容取代传统的复杂和低效的成像协议 简单、全面的采集，还可以产生对特定疾病敏感的定量参数 流程。在这个P41中心的第一个资助期，我们的项目团队带头建立了RAPID， 连续、全面的成像方法，现在可用于越来越多的商业 全球的磁共振成像(MRI)扫描仪。这一基础将使我们能够在拟议的 下一阶段的研究计划，以丰富我们的数据流，提前提取可操作的 这些数据流中的信息，并将生成的信息反馈到我们的 采集软件和硬件。由于我们第一个资助期的发展，我们现在处于 质疑关于扫描仪设计的长期假设，这些假设源于经典成像 人类放射科医生解释多系列定性图像的管道。我们将重新想象这一过程 MR扫描，利用我们在脉冲序列设计、并行成像、压缩 传感、基于模型的图像重建和机器学习。我们还将把我们的方法扩展到 复杂的多方面数据流，不仅来自磁共振成像，也来自正电子发射断层扫描 (PET)和其他成像方式，以及来自不同阵列的互补传感器。

TRD1 Project Summary The broad mission of our Center for Advanced Imaging Innovation and Research (CAI2R) is to bring together collaborative translational research teams for the development of high-impact biomedical imaging technologies, with the ultimate goal of changing day-to-day clinical practice. Technology Research and Development (TR&D) Project 1 aims to replace traditional complex and inefficient imaging protocols with simple, comprehensive acquisitions that also yield quantitative parameters sensitive to specific disease processes. In the first funding period of this P41 Center, our project team led the way in establishing rapid, continuous, comprehensive imaging methods, which are now available on a growing number of commercial magnetic resonance imaging (MRI) scanners worldwide. This foundation will allow us, in the proposed research plan for the next period, to enrich our data streams, to advance the extraction of actionable information from those data streams, and to feed the resulting information back into the design of our acquisition software and hardware. Thanks to developments during our first funding period, we are now in a position to question long-established assumptions about scanner design, originating from the classical imaging pipeline of human radiologists interpreting multiple series of qualitative images. We will reimagine the process of MR scanning, leveraging our core expertise in pulse-sequence design, parallel imaging, compressed sensing, model-based image reconstruction and machine learning. We will also extend our methods to complex multifaceted data streams, arising not only from MRI but also from Positron Emission Tomography (PET) and other imaging modalities, as well as from diverse arrays of complementary sensors.