3D Spectral Imaging based on Compton scattering: data modelling and reconstruction strategies

基于康普顿散射的 3D 光谱成像：数据建模和重建策略

• 批准号: 419953439
• 负责人: Dr. Gael Rigaud
• 金额: --
• 依托单位: Lehrstuhl für Optimierung und inverse Probleme
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2019
• 资助国家: 德国
• 起止时间: 2018-12-31 至 2020-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/419953439?language=en
• 关键词: 3D; Spectral; Imaging; based; Compton

In a standard CT-scan, a x-ray source irradiates a target and a set of detectors measures the attenuation of the crossing beams for various source positions. The measured data is then processed to get an image featuring the target in a non-invasive manner. In this classical scheme, the energy of the detected radiation is unexploited as a data variable. The recent development of spectral cameras opens the way for designing energy-based imaging systems. One concept consists in considering a monochromatic source and modelling the spectrum of the measured photon flux by the Compton effect. In this context, our previous project developed suitable forward models and reconstruction methods when the scattered radiation is assumed only of order one. However, the scattered radiation of order larger than one represents a substantial part of the complete spectrum. Considering higher scattering orders changes the nature of the data. This is why we speak in that respect of 3D Spectral Imaging based on Compton Scattering (CSpI). This will provide significant advances in imaging such as reducing the radiation dose received by the patient (in CT only 20% of the primary radiation is exploited), reducing the data acquisition time and delivering new insights of the object regarding standard techniques.Thus, we strive for developing a mathematical framework for imaging the 3D volume of an object of interest from spectral CSpI-data. For this purpose, the project is divided in two main approaches: The first one shall study the smoothness properties of the derived model for the multiple scattering to enable extracting the features of the sought-for quantity via filtered backprojection type algorithms. These are fast to compute and do not require prior information. In addition, our second approach includes data-driven strategies which allow more flexibility at the cost of prior information or computation times. In this context, we propose to first approximate the nonlinear data model by a linear operator and to consider the unknown part as an uncertain quantity. Methods from optimization theory could then bring a reconstruction scheme. At last, machine learning techniques could help to differentiate the multiple scattering from the first order within the spectrum. This would offer the possibility to further exploit the methods developed in previous project.In conclusion, the proposed project will provide the theoretical basis to exploit fully the multiple scattering for imaging purposes via future CSpI modalities.

在标准 CT 扫描中，X 射线源照射目标，一组探测器测量不同源位置的交叉光束的衰减。然后对测量的数据进行处理，以非侵入方式获取目标的图像。在这个经典方案中，检测到的辐射的能量未被用作数据变量。光谱相机的最新发展为设计基于能量的成像系统开辟了道路。一个概念在于考虑单色源并通过康普顿效应对测量的光子通量的光谱进行建模。在这种背景下，我们之前的项目在假设散射辐射仅为一阶时开发了合适​​的正演模型和重建方法。然而，大于一阶的散射辐射代表了整个光谱的很大一部分。考虑更高的散射阶数会改变数据的性质。这就是为什么我们在这方面谈论基于康普顿散射 (CSpI) 的 3D 光谱成像。这将为成像带来重大进步，例如减少患者接受的辐射剂量（在 CT 中仅利用了 20% 的初级辐射）、减少数据采集时间并提供有关标准技术的对象的新见解。因此，我们努力开发一个数学框架，用于根据光谱 CspI 数据对感兴趣对象的 3D 体积进行成像。为此，该项目分为两种主要方法：第一种方法应研究多重散射的导出模型的平滑特性，以便能够通过滤波反投影类型算法提取所需数量的特征。这些计算速度很快并且不需要先验信息。此外，我们的第二种方法包括数据驱动的策略，它以先验信息或计算时间为代价提供了更大的灵活性。在这种情况下，我们建议首先通过线性算子来近似非线性数据模型，并将未知部分视为不确定量。优化理论的方法可以带来重建方案。最后，机器学习技术可以帮助区分光谱内的多次散射和一阶散射。这将为进一步利用先前项目中开发的方法提供可能性。 总之，所提出的项目将为通过未来的 CSpI 模式充分利用多重散射用于成像目的提供理论基础。