Deep Learning Reconstruction for Improved TOF PET Using Histo-Image Partitioning

使用组织图像分区进行深度学习重建以改进 TOF PET

• 批准号: 10441527
• 负责人: SAMUEL MATEJ
• 金额: $ 59.85万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-01 至 2025-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10441527
• 关键词: 3-Dimensional; Address; Algorithms; Application procedure; Archives; Area; Breast; Case Study; Clinical; Clinical Data; Clinical Research; Data; Detection; Development; Diagnostic; Diagnostic Imaging; Discipline of Nuclear Medicine; Disease; Dose; Evaluation; Explosion; Geometry; Goals; Half-Life; Image; Imaging Techniques; Intervention; Interventional Imaging; Investigation; Isotopes; Label; Lung; Methods; Modeling; Modernization; Monitor; Morphologic artifacts; Motion; Organ; Patients; Performance; Physics; Positron; Positron-Emission Tomography; Procedures; Property; Protocols documentation; Psychological Transfer; Public Health; Radiation Dose Unit; Research; Resolution; Running; Scanning; System; Techniques; Testing; Time; Tracer; Training; Training Technics; Validation; Work; accurate diagnosis; base; breast imaging; cellular imaging; chimeric antigen receptor T cells; clinical efficacy; clinically relevant; convolutional neural network; data modeling; deep learning; denoising; detector; disease diagnosis; efficacy evaluation; heart motion; improved; innovation; instrumentation; learning network; loss of function; molecular imaging; neural network; neural network architecture; novel; novel strategies; personalized medicine; proton therapy; radiotracer; reconstruction; response; solid state; statistics; success; tomography; tool; treatment response

Project Summary Clinical and research applications of the PET imaging are rapidly expanding from ever improving diagnostic and treatment assessment applications to guidance of personalized treatments, ultra-low dose imaging, and even interventional imaging procedures. Supporting these developments, reconstruction tools that are able to reliably handle both typical and (ultra-)low count situations, imperfect data, and data from specialized imaging geometries, with fast (near real-time) reconstruction performance are of crucial importance. The overall goal of this project is to develop and investigate robust and efficacious Deep Learning (DL) reconstruction approaches addressing these needs. A unique and innovative feature of the proposed approaches (compared to alternative DL applications) is the utilization of list-mode data histogrammed into a very efficient histo-image format. TOF data partitioned into the histo-image format are characterized by strong local properties, thus perfectly fitting convolutional neural network formalism and making DL training and reconstruction directly from realistic clinical data (in size and character) highly feasible and practical. The clinical utility of PET systems has significantly improved over the years thanks to advances in instrumentation, data corrections, and reconstruction approaches. Nevertheless, full utilization of their potential through robust and fast quantitative reconstruction remains a challenge especially for the cases of very low count data, such as in low-count temporal (motion and dynamic) frames, delayed studies, longitudinal low-dose studies, and studies using new isotopes with long half-life and low positron fraction rates (e.g. in 89Zr-labeled CAR-T cell imaging), as well as in specialized PET systems with partial angular coverage, for which exact, artifact-free, reconstruction does not exist. These are the situations for which the developed DL approaches promise great potential due to the demonstrated success of the DL networks to be trained for imperfect and very low count data without reliance on accurate data models. Furthermore, pre-trained networks can provide ultra- fast, near real-time, performance in practical use. Specific Aim 1 will develop tools for DL PET reconstruction using histo-image partitioning along with procedures for training of the proposed DL approaches, including novel approaches advancing the state-of-the- art of DL reconstruction directly from acquired PET data. Specific Aim 2 is directed towards study and evaluation of the performance of the investigated DL approaches for whole-body and long axial FOV scanner data for the wide range of counts from applications such as typical FDG, low dose, delayed, low activity isotope scans, and ultra-short frames in motion correction and dynamic studies. Specific Aim 3 will develop and apply motion correction protocols involving the proposed DL reconstruction tools and test and study their efficacy for clinically realistic situations involving non-rigid lung and heart motions. And finally, Specific Aim 4 is dedicated to an application and study of the developed DL approaches to specialized PET systems with partial angular coverage. 1

项目摘要 PET成像的临床和研究应用正从不断改进的诊断和诊断方法迅速扩展。 和治疗评估应用程序，以指导个性化治疗，超低剂量成像， 甚至是介入成像程序。支持这些发展，重建工具，能够 可靠地处理典型和（超）低计数情况、不完美数据以及来自专业成像的数据 具有快速（近实时）重建性能的几何形状是至关重要的。的总目标 该项目旨在开发和研究强大而有效的深度学习（DL）重建方法 满足这些需求。拟议办法的一个独特和创新的特点（与替代办法相比） DL应用程序）是利用列表模式数据将其直方图化为非常高效的直方图格式。TOF 划分成直方图格式的数据具有很强的局部特性，因此完全拟合 卷积神经网络的形式化，使DL训练和重建直接从现实的临床 数据（在大小和字符）高度可行和实用。 PET系统的临床效用多年来由于以下方面的进步而显著提高： 仪器、数据校正和重建方法。然而，充分利用其潜力 通过稳健和快速的定量重建仍然是一个挑战，特别是对于非常低计数的情况下， 数据，如低计数时间（运动和动态）帧、延迟研究、纵向低剂量 研究，以及使用具有长半衰期和低正电子分数率的新同位素的研究（例如，在89 Zr标记的 CAR-T细胞成像），以及在具有部分角度覆盖的专用PET系统中， 无伪影，重建不存在。这些都是开发的DL方法的情况 由于DL网络的成功证明，它具有巨大的潜力，可以训练不完美和非常 低计数数据，无需依赖准确的数据模型。此外，预先训练的网络可以提供超 快速、接近实时，在实际应用中具有很好的性能。 Specific Aim 1将开发使用组织图像分割沿着进行DL PET重建的工具， 拟议的DL方法的培训程序，包括推进国家的新方法， 直接从采集的PET数据进行DL重建的技术。具体目标2是针对研究和评价 研究的DL方法用于全身和长轴FOV扫描仪数据的性能， 广泛的计数应用，如典型的FDG，低剂量，延迟，低活性同位素扫描， 运动校正和动态研究中的超短帧。具体目标3将开发和应用运动 校正协议涉及拟议的DL重建工具，并测试和研究其临床疗效 涉及非刚性肺和心脏运动的现实情况。最后，具体目标4致力于 开发的DL方法在部分角度覆盖的专用PET系统中的应用和研究。 1