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）：该项目将基于压缩感测理论开发一种创新的重建方法，用于单光量发射计算机断层扫描（SPECT）。拟议的研究的目的是使示踪剂吸收的动态图像（每张图像〜1秒），摄像机数量最少，因为相机代表了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系统可以成功商业化并使一般研究界可以访问的可能性。可以从动态研究中估算的动力学参数提供了有关生理机制和疾病状态的重要信息。压缩传感算法已经显示出有望从磁共振（MR）和计算机断层扫描（CT）采集中重建未采样的数据。拟议的项目将开发出一种基于压缩感应理论的新型基于优化的重建算法，用于SPECT成像。与以前提出的MR和CT算法不同，我们提出的算法将模拟SPECT和使用样条小波的Poisson噪声统计量，作为一种新颖的稀疏变换。提出的CS算法将通过模拟，幻影实验和从肺部生理学和研究实验室的三台小型小动物SPECT系统获得的动物数据进行研究。对于空间和时间准确性的定量指标，将量化算法的性能并将其与常规重建方法进行比较。该算法将根据模拟，动态幻影实验和动物数据的快速成像（〜1秒）示踪剂的预期临床任务进行测试。该项目与Zablocki VA医疗中心的肺部生理学和研究实验室合作，该中心的动力学信息将用于开发用于诊断肺损伤和疾病的示踪剂。该项目的成功完成将导致一种新颖的重建方法，该方法将使许多Spect应用受益，例如心脏和肿瘤学研究。 公共卫生相关性：该项目将开发用于快速（约1秒），动态单光子发射计算机断层扫描（SPECT）的创新重建算法。成功完成该项目将导致算法用于构建动态示踪剂摄取的图像，这将有助于估计基础动力学参数。在我们的实验室中，该算法将用于开发成像剂以诊断肺损伤程度。在体内准确量化快速（约1秒）3D示踪剂的能力将对许多小动物和临床研究有益，例如在癌症成像的地区。

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