Prior-image-based CT reconstruction using attenuation mismatched prior.
Hao ZhangDante P I CapaldiDong ZengJianhua MaLei XingPublished in: Physics in medicine and biology (2021)
Prior-image-based reconstruction (PIBR) methods are powerful in reducing radiation dose and improving image quality for low-dose CT. Besides anatomical changes, the prior and current images can also have different attenuation due to different scanners or the same scanner but with different x-ray beam quality (e.g., kVp setting, beam filtration) during data acquisitions. PIBR is challenged in such scenarios with attenuation mismatched prior. In this work, we investigate a specific PIBR method, called statistical image reconstruction using normal dose image induced nonlocal means regularization (SIR-ndiNLM), to address PIBR with such attenuation mismatched prior and achieve quantitative low-dose CT imaging. We proposed two corrective schemes for the original SIR-ndiNLM method, 1) a global histogram matching approach and 2) a local attenuation correction approach, to account for the attenuation differences between the prior and current images in PIBR. We validated the efficacy of the proposed schemes using images acquired from dual-energy CT scanners to emulate attenuation mismatches. Meanwhile, we utilized different CT slices to emulate anatomical mismatches/changes between the prior and the current low-dose images. We observed that the original SIR-ndiNLM introduces artifacts to the reconstruction when using attenuation mismatched prior. Furthermore, we found that larger attenuation mismatch between the prior and current images results in more severe artifacts in the SIR-ndiNLM reconstruction. Our proposed two corrective schemes enabled SIR-ndiNLM to effectively handle attenuation mismatch and anatomical changes between two images and successfully eliminate the artifacts. We demonstrated that the proposed techniques permit SIR-ndiNLM to leverage the attenuation mismatched prior and achieve quantitative low-dose CT reconstruction from both low-flux and sparse-view data acquisitions. This work permits robust and reliable PIBR for CT data acquired using different beam settings.