CIF:Small:Coded Aperture Spectral X-Ray Tomography

CIF：小：编码孔径光谱 X 射线断层扫描

• 批准号: 1717578
• 负责人: Gonzalo Arce
• 金额: $ 45万
• 依托单位: University of Delaware
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-07-01 至 2022-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1717578&HistoricalAwards=false
• 关键词: CIF; Small; Coded; Aperture; Spectral

Spectral tomography has emerged as a key imaging modality for medical imaging and homeland security. It offers material discrimination providing useful cues for identifying atomic composition, chemical targets, and explosives. It relies on the principle that the X-ray absorption of different chemical materials has characteristics that depend on the energy of the X-rays used. Thus, detectors that can differentiate between X-ray energies make it possible to discriminate different materials having similar X-ray attenuation properties. Unfortunately, energy resolving X-ray detectors are prohibitively costly for many applications. Other detector technologies often trade cost for acquisition speed and, in many cases, practical limitations require limited angle or subsampled projection geometries that lead to severe artifacts in image reconstruction. This research explores coded X-ray illumination projections which can be captured with standard integrating detectors yet provide information for energy-resolved image reconstructions. The coded measurements have lower radiation dose. They can also be multiplexed for robustness in limited angle scenarios.This research explores colored X-ray coded projections to circumvent current detector limitations. Rather than measuring objects by the attenuated X-rays, the X-rays are first coded in amplitude and spectra that modulate the X-ray field in a known pattern, effectively creating lower-dose structured X-ray bundles that interrogate specific voxels of the object. Careful design of the coded apertures, their placement, and the development of fast computational inverse algorithms that exploit the structured illumination can provide advantages in the application of spectral tomography. Various computerized tomography (CT) geometries are being considered including tomosynthesis, fan-beam, and cone-beam. A test-bed for validating the underlying theoretical concepts is a central part of the project.

光谱层析成像已经成为医学成像和国土安全的关键成像模式。它提供了材料辨别，为识别原子成分、化学目标和爆炸物提供了有用的线索。它依赖于不同化学材料的X射线吸收具有取决于所用X射线能量的特性的原理。因此，能够区分X射线能量的探测器使得能够区分具有相似X射线衰减特性的不同材料。不幸的是，能量分辨X射线探测器对于许多应用来说成本过高。其他检测器技术通常以成本换取采集速度，并且在许多情况下，实际限制需要有限的角度或子采样投影几何形状，这导致图像重建中的严重伪影。本研究探讨了编码的X射线照明投影，它可以与标准的集成探测器捕获，但提供信息的能量分辨图像重建。编码测量具有较低的辐射剂量。他们也可以多路复用的鲁棒性在有限的角度scenaries.This研究探讨彩色X射线编码的投影，以规避目前的探测器的限制。不是通过衰减的X射线来测量对象，而是首先在振幅和光谱中对X射线进行编码，以已知模式调制X射线场，有效地创建询问对象的特定体素的较低剂量结构化X射线束。仔细设计的编码孔径，他们的位置，并开发快速计算逆算法，利用结构化照明可以提供的应用程序中的光谱层析成像的优势。正在考虑各种计算机断层扫描（CT）几何形状，包括断层合成、扇束和锥束。验证基本理论概念的试验台是该项目的核心部分。

项目成果

Low-Dose Dual KVP Switching Using A Static Coded Aperture

• DOI: 10.1109/isbi48211.2021.9434080
• 发表时间: 2021-04
• 期刊: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)
• 作者: Angela P. Cuadros;Carlos M. Restrepo;P. Noel

Static coded illumination strategies for low-dose x-ray material decomposition

用于低剂量 X 射线材料分解的静态编码照明策略

• DOI: 10.1364/ao.446104
• 发表时间: 2022
• 期刊: Applied Optics
• 影响因子: 1.9
• 作者: Cuadros, Angela P.;Restrepo, Carlos M.;Noël, Peter;Arce, Gonzalo R.
• 通讯作者: Arce, Gonzalo R.

K-edge Coded Apertures for Compressive Spectral X-ray Tomography

用于压缩能谱 X 射线断层扫描的 K 边缘编码孔径

• DOI: 10.1109/icassp.2019.8682905
• 发表时间: 2019
• 期刊: Speech and Signal Processing (ICASSP
• 作者: Cuadros, Angela P.;Ma, Xu;Arce, Gonzalo R.
• 通讯作者: Arce, Gonzalo R.

Compressive spectral X-ray tomography based on spatial and spectral coded illumination

• DOI: 10.1364/oe.27.010745
• 发表时间: 2019-04-15
• 期刊: OPTICS EXPRESS
• 影响因子: 3.8
• 作者: Cuadros, Angela;Ma, Xu;Arce, Gonzalo R.
• 通讯作者: Arce, Gonzalo R.

Static coded aperture in robotic X-ray tomography systems

机器人 X 射线断层扫描系统中的静态编码孔径

• DOI: 10.1364/oe.449505
• 发表时间: 2022
• 期刊: Optics Express
• 影响因子: 3.8
• 作者: Mao, Tianyi;Ma, Xu;Cuadros, Angela P.;Dai, XiuBin;Wang, Zhiteng;Zhang, Xin;Zhu, Shujin;Zhu, Jianjian;Arce, Gonzalo R.
• 通讯作者: Arce, Gonzalo R.