A feasibility study of extracting tissue textures from a previous normal-dose CT database as prior for Bayesian reconstruction of current ultra-low-dose CT images

Yongfeng Gao; Zhengrong Liang; Yuxiang Xing; Hao Zhang; Jianhua Ma; Hongbing Lu; Lihong Li; Bo Chen; Marc Pomeroy; William H. Moore

doi:10.1117/12.2293854

Translator Disclaimer

9 March 2018 A feasibility study of extracting tissue textures from a previous normal-dose CT database as prior for Bayesian reconstruction of current ultra-low-dose CT images

Yongfeng Gao, Zhengrong Liang, Yuxiang Xing, Hao Zhang, Jianhua Ma, Hongbing Lu, Lihong Li, Bo Chen, Marc Pomeroy, William H. Moore

Author Affiliations +

Proceedings Volume 10573, Medical Imaging 2018: Physics of Medical Imaging; 105733C (2018) https://doi.org/10.1117/12.2293854
Event: SPIE Medical Imaging, 2018, Houston, Texas, United States

Abstract

Tremendous research efforts have been devoted to lower the X-ray radiation exposure to the patient in order to expand the utility of computed tomography (CT), particularly to pediatric imaging and population-based screening. When the exposure dosage goes down, both the X-ray quanta fluctuation and the system electronic background noise become significant factors affecting the image quality. Conventional edge-preserving noise smoothing would sacrifice tissue textures and compromise the clinical tasks. To relieve these challenges, this work models the noise problem by pre-log shifted Poisson statistics and extracts tissue textures from previous normal-dose CT scans as prior knowledge for texturepreserving Bayesian reconstruction of current ultralow-dose CT images. The pre-log shift Poisson model considers accurately both the X-ray quanta fluctuation and the system electronic noise while the prior knowledge of tissue textures removes the limitation of the conventional edge-preserving noise smoothing. The Bayesian reconstruction was tested by experimental studies. One patient chest scan was selected from a database of 133 patients’ scans at 100mAs/120kVp normal-dose level. From the selected patient scan, ultralow-dose data was simulated at 5mAs/120kVp level. The other 132 normal-dose scans were grouped according to how close their lung tissue texture patterns are from that of the selected patient scan. The tissue textures of each group were used to reconstruct the ultralow-dose scan by the Bayesian algorithm. The closest group to the selected patient produced almost identical results to the reconstruction when the tissue textures of the selected patient’s normal-dose scan were used, indicating the feasibility of extracting tissue textures from a previous normal-dose database to reconstruct any current ultralow-dose CT image. Since the Bayesian reconstruction can be time consuming, this work further investigates a strategy to efficiently store the projection matrix rather than computing the line integrals on-flight. This strategy accelerated the computing speed by more than 18 times.

Citation Download Citation

Yongfeng Gao, Zhengrong Liang, Yuxiang Xing, Hao Zhang, Jianhua Ma, Hongbing Lu, Lihong Li, Bo Chen, Marc Pomeroy, and William H. Moore "A feasibility study of extracting tissue textures from a previous normal-dose CT database as prior for Bayesian reconstruction of current ultra-low-dose CT images", Proc. SPIE 10573, Medical Imaging 2018: Physics of Medical Imaging, 105733C (9 March 2018); https://doi.org/10.1117/12.2293854

ACCESS THE FULL ARTICLE