CIF: Small: Model-based sparse X-ray CT signal processing using polychromatic measurements

CIF：小型：使用多色测量进行基于模型的稀疏 X 射线 CT 信号处理

• 批准号: 1421480
• 负责人: Aleksandar Dogandzic
• 金额: $ 15万
• 依托单位: Iowa State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1421480&HistoricalAwards=false
• 关键词: CIF; Small; Model; based; sparse

X-ray CT measurement systems are important in modern nondestructive evaluation (NDE) and medical diagnostics. Improving reconstruction accuracy and speed of data collection in these systems could have a significant impact on these broad areas. Thanks to recent computational and theoretical advances, it is now possible to design iterative reconstruction methods that incorporate accurate nonlinear physical models into sparse signal reconstructions from significantly undersampled measurements. This research aims at achieving accurate X-ray CT reconstruction from polychromatic measurements via parsimonious nonlinear signal models.The investigators develop sparse image reconstruction methods for polychromatic CT measurements under the blind scenario where the material of the inspected object and the incident energy spectrum are unknown. This research builds on mass attenuation parametrization that leads to a simple signal model of a polychromatic X-ray CT measurement system. Since both the mass attenuation coefficient of a material and the incident spectrum of the measurement system are functions of the photon energy, photon energy can be removed from the signal model through a change of variables, leading to integral expressions of the attenuated signal strength having the Laplace kernel form. The investigators represent the incident spectrum as a function of mass attenuation and estimate it by using an appropriate basis-function expansion. A major effort focuses on developing similar simple models and corresponding algorithms for reconstructing objects consisting of multiple materials.

X射线CT测量系统在现代无损评估（NDE）和医学诊断中很重要。提高这些系统中数据收集的重建精度和数据收集速度可能会对这些广泛领域产生重大影响。得益于最近的计算和理论进步，现在可以设计迭代重建方法，将精确的非线性物理模型纳入稀疏的信号重建中，从显着采样的测量中进行了稀疏信号重建。这项研究旨在通过副型非线性信号模型从多色测量中实现准确的X射线CT重建。研究人员在盲型场景下开发了用于多色CT测量的稀疏图像重建方法，其中检查对象的材料和入射能谱未知。这项研究基于质量衰减参数化，从而导致多色X射线CT测量系统的简单信号模型。由于材料的质量衰减系数和测量系统的入射光谱都是光子能的函数，因此可以通过变量改变信号模型从信号模型中去除光子能，从而导致具有拉普拉斯内核形式的衰减信号强度的积分表达式。 研究人员代表事件频谱是大规模衰减的函数，并通过使用适当的基础功能扩展来估计它。一项重大努力侧重于开发类似的简单模型和相应的算法，用于重建由多种材料组成的对象。

项目成果

Blind X-Ray CT Image Reconstruction From Polychromatic Poisson Measurements

• DOI: 10.1109/tci.2016.2523431
• 发表时间: 2016-06-01
• 期刊: IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING
• 影响因子: 5.4
• 作者: Gu, Renliang;Dogandzic, Aleksandar
• 通讯作者: Dogandzic, Aleksandar

Projected Nesterov's Proximal-Gradient Algorithm for Sparse Signal Recovery

• DOI: 10.1109/tsp.2017.2691661
• 发表时间: 2015-02
• 期刊: IEEE Transactions on Signal Processing
• 影响因子: 5.4
• 作者: Renliang Gu;Aleksandar Dogandzic

Polychromatic sparse image reconstruction and mass attenuation spectrum estimation via B-spline basis function expansion

通过 B 样条基函数展开进行多色稀疏图像重建和质量衰减​​谱估计

• DOI: 10.1063/1.4914792
• 发表时间: 2015
• 期刊: AIP conference proceedings
• 作者: Gu, Renliang;Dogandzic, Aleksandar
• 通讯作者: Dogandzic, Aleksandar

Blind polychromatic X-ray CT reconstruction from poisson measurements

根据泊松测量进行盲多色 X 射线 CT 重建

• DOI: 10.1109/icassp.2016.7471805
• 发表时间: 2016
• 期刊: Speech and Signal Processing (ICASSP
• 作者: Gu, Renliang;Dogandzic, Aleksandar
• 通讯作者: Dogandzic, Aleksandar

Blind Beam-Hardening Correction from Poisson Measurements

• DOI: 10.1063/1.4940581
• 发表时间: 2016-01-01
• 期刊: 42ND ANNUAL REVIEW OF PROGRESS IN QUANTITATIVE NONDESTRUCTIVE EVALUATION: INCORPORATING THE 6TH EUROPEAN-AMERICAN WORKSHOP ON RELIABILITY OF NDE
• 作者: Gu, Renliang;Dogandzic, Aleksandar
• 通讯作者: Dogandzic, Aleksandar