Innovative reconstruction algorithms for undersampled SPECT

欠采样 SPECT 的创新重建算法

• 批准号: 7981380
• 负责人: Taly Gilat Schmidt
• 金额: $ 36.63万
• 依托单位: MARQUETTE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-07-01 至 2013-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7981380
• 关键词: Address; Algorithms; Animals; Area; Cardiac; Clinical; Clinical Research; Collaborations; Collimator; Communities; Control Animal; Data; Development; Diagnosis; Disease; Goals; Gold; Image; Kinetics; Laboratories; Laboratory Research; Lung; Magnetic Resonance; Measures; Medical center; Metric; Modeling; Noise; Performance; Physiological; Physiology; Rattus; Research; Resolution; Rotation; Sampling; Scientist; Signal Transduction; Simulate; System; Testing; Time; Tracer; Work; X-Ray Computed Tomography; animal data; base; cancer imaging; cost; expectation; improved; in vivo; innovation; interest; lung injury; novel; oncology; public health relevance; reconstruction; research study; simulation; single photon emission computed tomography; statistics; theories; uptake

DESCRIPTION (provided by applicant): This project will develop an innovative reconstruction approach for single-photon emission computed tomography (SPECT) based on compressed sensing theory. The goal of the proposed research is to enable dynamic images of tracer uptake (~ 1 second per image) with a minimal number of cameras, as cameras represent the major cost of a SPECT system. Reducing the number of cameras increases the likelihood that dynamic small-animal SPECT systems can be successfully commercialized and made accessible to the general research community. The kinetic parameters that can be estimated from dynamic studies provide important information about physiological mechanisms and disease states. Compressed sensing algorithms have shown promise for reconstructing undersampled data from Magnetic Resonance (MR) and Computed Tomography (CT) acquisitions. The proposed project will develop a novel optimization-based reconstruction algorithm for SPECT imaging, drawing on compressed sensing theory. Unlike previously proposed algorithms for MR and CT, our proposed algorithm will model the Poisson noise statistics of SPECT and use spline wavelets as a novel sparsifying transform. The proposed CS algorithm will be studied through simulations, phantom experiments, and animal data obtained from the three-camera small-animal SPECT system at the Pulmonary Physiology and Research Laboratory. The performance of the algorithm will be quantified and compared to conventional reconstruction approaches with respect to quantitative metrics of spatial and temporal accuracy. The algorithm will be tested with respect to the expected clinical task of imaging rapid (~ 1 second) tracer uptake through simulations, dynamic phantom experiments, and animal data. This project is in collaboration with the Pulmonary Physiology and Research Laboratory at the Zablocki VA Medical Center, where the kinetic information from the reconstructed time-activity curves will be used to develop tracers for diagnosing lung injury and disease. The successful completion of this project will result in a novel reconstruction approach that will benefit numerous SPECT applications, for example cardiac and oncology studies. PUBLIC HEALTH RELEVANCE: This project will develop innovative reconstruction algorithms for rapid (~ 1 sec), dynamic single-photon emission computed tomography (SPECT) acquisitions. Successful completion of the project will result in algorithms for constructing images of the dynamic tracer uptake, which will facilitate estimation of the underlying kinetic parameters. In our laboratory, the algorithms will be used to develop imaging agents for diagnosing the extent of lung injury. The ability to accurately quantify the fast (~1 sec) 3D tracer uptake in vivo will be beneficial for numerous small-animal and clinical studies, for example in the areas cancer imaging.

描述（由申请人提供）：本项目将开发一种基于压缩感知理论的单光子发射计算机断层扫描（SPECT）的创新重建方法。所提出的研究的目标是用最少数量的摄像机实现示踪剂摄取的动态图像（每个图像约1秒），因为摄像机代表SPECT系统的主要成本。减少摄像机的数量增加了动态小动物SPECT系统成功商业化的可能性，并使一般研究界可以使用。动力学参数，可以估计从动态研究提供了重要的信息，生理机制和疾病状态。压缩感知算法已经显示出用于从磁共振（MR）和计算机断层扫描（CT）采集重建欠采样数据的前景。该拟议项目将借鉴压缩传感理论，开发一种新型的基于优化的SPECT成像重建算法。与以前提出的算法MR和CT，我们提出的算法将模拟泊松噪声统计SPECT和使用样条小波作为一种新的稀疏化变换。将通过模拟、体模实验和从肺生理学和研究实验室的三相机小动物SPECT系统获得的动物数据来研究所提出的CS算法。该算法的性能将被量化，并与传统的重建方法的空间和时间精度的定量指标进行比较。将通过模拟、动态体模实验和动物数据，针对成像快速（约1秒）示踪剂摄取的预期临床任务对算法进行测试。该项目与Zablocki VA医疗中心的肺生理学和研究实验室合作，重建的时间-活动曲线的动力学信息将用于开发诊断肺损伤和疾病的示踪剂。该项目的成功完成将产生一种新的重建方法，这将有利于许多SPECT应用，例如心脏和肿瘤研究。 公共卫生相关性：该项目将开发用于快速（~ 1秒）、动态单光子发射计算机断层扫描（SPECT）采集的创新重建算法。该项目的成功完成将产生构建动态示踪剂摄取图像的算法，这将有助于估计基本的动力学参数。在我们的实验室中，该算法将用于开发用于诊断肺损伤程度的成像剂。准确量化体内快速（约1秒）3D示踪剂摄取的能力将有益于许多小动物和临床研究，例如在癌症成像领域。

项目成果

The performance of MLEM for dynamic imaging from simulated few-view, multi-pinhole SPECT.

• DOI: 10.1109/tns.2012.2214235
• 发表时间: 2013-02
• 期刊: IEEE transactions on nuclear science
• 影响因子: 1.8
• 作者: Ma D;Wolf P;Clough AV;Schmidt TG
• 通讯作者: Schmidt TG