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: 10276952
• 负责人: SAMUEL MATEJ
• 金额: $ 62.01万
• 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/10276952
• 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; 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

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在迅速扩展，从以往的诊断水平不断提高 和治疗评估应用于指导个性化治疗、超低剂量成像以及 甚至是介入性成像手术。支持这些发展的重建工具能够 可靠地处理典型和(超)低计数情况、不完美的数据以及来自专门成像的数据 具有快速(接近实时)重建性能的几何图形至关重要。的总目标是 该项目是开发和研究稳健和有效的深度学习(DL)重建方法 满足这些需求。拟议方法的独特和创新特征(与替代方法相比 DL应用程序)是将列表模式数据直方图转换成非常有效的直方图格式。TOF 分割成直方图格式的数据具有强局部属性的特征，因此完全匹配 卷积神经网络形式论与直接从临床实际出发进行动态链式训练和重建 数据(大小和性质)具有很高的可行性和实用性。 由于以下方面的进步，PET系统的临床实用性在过去几年中有了显著提高 仪器、数据校正和重建方法。尽管如此，充分利用他们的潜力 通过稳健和快速的量化重建仍然是一项挑战，特别是对于数量非常少的情况 数据，如低计数时间(运动和动态)帧、延迟研究、纵向低剂量 研究和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，在标记的89Zr中 CAR-T细胞成像)，以及在具有部分角度覆盖的专用PET系统中， 没有神器，不存在重建。在这些情况下，开发的数字图书馆方法 由于被证明成功的数字图书馆网络的巨大潜力，将针对不完美和非常 无需依赖准确的数据模型即可获得数量较少的数据。此外，预先训练的网络可以提供超强的 速度快，接近实时，在实际使用中表现良好。 特殊目标1将开发使用组织图像分割的DL PET重建工具，以及 拟议的数字学习方法的培训程序，包括推进最新情况的新方法 直接从采集的PET数据重建DL的艺术。具体目标2是针对研究和评估的 所研究的用于全身和长轴视场扫描仪数据的DL方法的性能 广泛的计数应用，如典型的FDG、低剂量、延迟、低活度同位素扫描，以及 超短帧在运动校正和动力学研究中的应用《特定目标3》将开发和应用运动 涉及所提出的DL重建工具的矫正方案并测试和研究其临床疗效 涉及非僵硬的肺和心脏运动的现实情况。最后，具体目标4致力于 发展的动态链接法在具有部分角度覆盖的专用PET系统中的应用和研究。 1