Quasi-ideal photon counting x-ray CT with multi-energy inter-pixel coincidence counter (MEICC)

具有多能量像素间符合计数器 (MEICC) 的准理想光子计数 X 射线 CT

• 批准号: 10117252
• 负责人: Katsuyuki Taguchi
• 金额: $ 19.73万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-04-01 至 2024-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10117252
• 关键词: Address; Algorithms; Anodes; Books; Charge; Clinical; Communication; Computer software; Contrast Media; Data; Detection; Diagnostic; Dreams; Electronics; Event; Financial compensation; Funding; Hospitals; Image; Industry; Measures; Modeling; Monte Carlo Method; Needles; Noise; PF4 Gene; Performance; Phase; Photons; Physics; Probability; Process; Radiation Dose Unit; Resolution; Roentgen Rays; Scheme; Signal Transduction; Small Business Innovation Research Grant; Sum; System; Technology; Testing; Time; Work; X-Ray Computed Tomography; analog; base; clinical application; data acquisition; design; detector; digital; expectation; imaging agent; imaging biomarker; imaging modality; improved; next generation; photon-counting detector; programs; prototype; response; soft tissue; tool

Project Summary We propose a technical solution that enables nearly ideal photon counting detectors (PCDs) for x-ray computed tomography (CT), which will bring most of clinical dreams surrounding PCD-CT into reality. We call the solution multi-energy inter-pixel coincidence counter (MEICC) and it is feasible to implement the design and algorithm of MEICC using today’s electronics technology. We plan to provide a proof of concept that will “move the needle” by working on the detail of MEICC, optimizing the design and studying the performance of MEICC using Monte Carlo (MC) simulations. PCD-CT is expected to be the next generation of x-ray CT. It has great potentials such as improve the current CT images but also to enable new clinical applications, such as higher spatial resolution, better soft tissue contrast, stronger contrast agent enhancement, radiation dose reduction, quantitative CT imaging and biomarkers, accurate soft tissue material characterization, K-edge imaging, and simultaneous multi-contrast agent imaging. Studies showed that latest PCDs were sufficiently fast for clinical x-ray CT and several groups developed prototype whole-body PCD-CT systems and installed them for a beta test in 2014–2018. Studies have shown great potential of PCD-CT. But, the performance of the prototype PCD-CT did not meet high expectations. In fact, the performance was sometimes comparable to that of dual-energy CT because of a phenomenon called “charge sharing” between PCD pixels. It increases noise variance by a factor of 4, degrades the spatial resolution, degrades the energy response, and weakens the spectral signals. Overall, it has a significantly negative impact on the performance of PCD-CT. Charge sharing is inherent to the detection physics and the probability of charge sharing is ~70%. Thus, it is impossible to avoid and is a very critical issue that needs to be addressed. MEICC will address both noise and bias added by charge sharing. MEICC uses energy-dependent coincidence counters, keeps the book of charge sharing events during the data acquisition, and corrects them using the exact number of the occurrences after the acquisition is completed. MEICC does not interfere with the primary counting process; thus, PCDs with MEICC will remain as fast as those without MEICC. MEICC can be implemented using today’s electronics technology because its inter-pixel coincidence counters are simple and digital. We hypothesize that MEICC can eliminate the effect of charge sharing, decrease noise to the minimal level, enhance signals, improve the energy response, and over all, enable nearly ideal x-ray PCD-CT. We plan to test the hypothesis by accomplishing the following 3 specific aims: (SA1) Develop MEICC designs and algorithms; (SA2) develop MC simulation programs; (SA3) assess the task-specific performance of MEICC and other completing technologies using Cramér–Rao lower bound as the primary figure of merit.

项目总结