SPARSITY-DRIVEN IDEAL OBSERVERS FOR GUIDING IMAGING HARDWARE OPTIMIZATION

用于指导成像硬件优化的稀疏驱动的理想观测器

• 批准号: 8975499
• 负责人: Mark A Anastasio
• 金额: $ 21.3万
• 依托单位: WASHINGTON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-06-01 至 2017-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8975499
• 关键词: Bayesian Analysis; Case Study; Complex; Computing Methodologies; Coupled; Data; Detection; Dose; Health; Image; Knowledge; Lead; Linear Models; Magnetic Resonance Imaging; Markov chain Monte Carlo methodology; Measurement; Medical; Medical Imaging; Methodology; Methods; Modeling; Performance; Probability; Property; Radiation; Reporting; Research; Sampling; Science; Signal Transduction; Solutions; Statistical Models; System; Technology; Testing; Time; Validation; Work; X-Ray Computed Tomography; base; computerized tools; data acquisition; density; design; ideal observer (Bayesian); image guided; image reconstruction; imaging modality; imaging system; improved; insight; interest; novel; reconstruction; statistics; theories

 DESCRIPTION (provided by applicant): The broad objective of this R21 application is to develop and investigate a novel method for optimizing hardware of modern computed imaging systems with respect to signal detection tasks. Specifically, we will establish an efficient method for computing a sparsity-driven Bayesian ideal observer (IO) test statistic that exploits object information that is relevant to modern sparse reconstruction methods. Significance: Modern reconstruction methods, referred to as sparse reconstruction methods, exploit the fact that objects of interest can often be described by sparse representations and have proven to be highly effective at reconstructing images from under-sampled measurement data. Conventional wisdom dictates that imaging hardware should be optimized by use of an IO that exploits full statistical knowledge of the class of objects to- be-imaged, without consideration of the reconstruction method to-be-employed. However, accurate and tractable models of the complete object statistics are often difficult to determine in practice. Moreover, in computed imaging approaches that employ compressive sensing concepts, imaging hardware and image reconstruction are innately coupled technologies. Accordingly, we propose to investigate a practical approach in which the hardware is optimized by use of the same low-level statistical information about the object that enables sparse reconstruction. This will facilitate reductions in data-acquisition times and/or radiation doses for a wide range of modern medical imaging systems. Challenges: There remain several impediments to computing IO performance to guide hardware optimization in practice. Perhaps most fundamental is the need to know the full probability density function of the object. Unrealistic assumptions, such as Gaussian-distributed object backgrounds, are generally required for analytical computation of IO performance. Markov chain Monte Carlo (MCMC) techniques are available for computing IO performance, but are not routinely employed due to their extreme computational burdens. Solutions: We will formulate sparsity-driven IOs (SD-IOs) to guide hardware optimization that assume knowledge of low-level statistical properties of the object that are related to sparsity. The SD-IO will explit the same statistical information regarding the object that is utilized by highly effective sparse image reconstruction methods. To efficiently compute SD-IO performance, we will estimate the posterior distribution by use of computational tools developed recently for variational Bayesian inference with sparse linear models. Subsequently, the SD-IO test statistic will be computed semi-analytically. Aims: The specific aims of this project are as follows. Aim 1: To develop and validate a method for computing SD-IO signal detection performance Aim 2: To investigate the use of the SD-IO for guiding optimization of data-acquisition parameters

 描述（由申请人提供）：该R21申请的广泛目标是开发和研究一种用于优化现代计算机成像系统的硬件的关于信号检测任务的新颖方法。具体来说，我们将建立一个有效的方法， 用于计算稀疏驱动的贝叶斯理想观测器（IO）测试统计量，其利用与现代稀疏重建方法相关的对象信息。重要性：被称为稀疏重建方法的现代重建方法利用了感兴趣的对象通常可以通过稀疏表示来描述的事实，并且已经证明在从欠采样测量数据重建图像方面非常有效。传统观点认为，成像硬件应该通过使用利用待成像的对象的类别的全部统计知识的IO来优化，而不考虑待采用的重建方法。然而，在实践中，通常难以确定完整对象统计的准确和易处理的模型。此外，在采用压缩感测概念的计算成像方法中，成像硬件和图像重建是固有耦合的技术。因此，我们建议研究一种实用的方法，其中硬件通过使用相同的低级别的统计信息，使稀疏重建的对象进行优化。这将有助于减少 数据采集时间和/或辐射剂量。挑战：在计算IO性能以指导实践中的硬件优化方面仍然存在一些障碍。也许最基本的是需要知道物体的全部概率密度函数。不切实际的假设，如高斯分布的对象背景，通常需要IO性能的分析计算。马尔可夫链蒙特卡罗（MCMC）技术可用于计算IO性能，但由于其极端的计算负担而不被常规采用。解决方案：我们将制定稀疏驱动的IO（SD-IO）来指导硬件优化，假设与稀疏性相关的对象的低级统计特性的知识。SD-IO将解释与高效稀疏图像重建方法所使用的对象相同的统计信息。为了有效地计算SD-IO性能，我们将使用最近开发的用于稀疏线性模型的变分贝叶斯推理的计算工具来估计后验分布。随后，将以半分析方式计算SD-IO检验统计量。目标：该项目的具体目标如下。目标1：开发并验证计算SD-IO信号检测性能的方法目的2：研究使用SD-IO指导数据采集参数的优化