A virtual anti-scatter grid for multi-energy photon counting detector systems

• Published in: Physica scripta Vol. 99; no. 10; pp. 105287 - 105300
• Main Authors: Purisha, Zenith, Winkler, Alexander, Emzir, Muhammad, Hostettler, Roland, Luukka, Panja, Särkkä, Simo
• Format: Journal Article
• Language: English
• Published: IOP Publishing 01-10-2024
• Subjects: anti-scatter; counting; grids; multi-energy; photon; virtual
• ISSN: 0031-8949; 1402-4896; 1402-4896
• DOI: 10.1088/1402-4896/ad7ce1

Photon-counting (PC) systems are the next technological generation of medical computed tomography (CT) imaging and is being worked on by all major system providers. CT devices that are based on PC detectors enable multi-energy data collection. The information-content of this data can be used to obtain more detailed patient data, which improves the quality of reconstructions, compared to conventional detector systems. However, PC CT systems are subject to radiation scatter as just as any other imaging systems is. Conventionally anti-scatter grids (ASG) are used to reduce the scatter effect. These are however an imperfect solution, especially for PC detectors. In this work, a software-based scatter correction method, thus a virtual ASG is proposed. The method is tailoring a new statistical model in the measurement space and combining it with the statistical inversion method called Markov chain Monte Carlo (MCMC). The method can recover the measurement data from dense projections. We present the method on simulated data of a single photon emission computed tomography (SPECT) problem for which only under-sampled data is available. However, our approach can in principle be generalised to CT, PC-CT, Positron emission tomography (PET), radiotherapy, or even digital radiography problems. The results show that the proposed model performs similarly as physical ASGs and for cases where ASGs are not possible. The model further offers a significant improvement in the quality of the reconstruction image compared to the image reconstruction from original under-sampled data.