Bayesian reconstruction of ultralow-dose CT images with texture prior from existing diagnostic full-dose CT database

Markov random field (MRF) has been widely used to incorporate a priori knowledge as a penalty for regional smoothing in ultralow-dose computed tomography (ULdCT) image reconstruction, while the regional smoothing does not explicitly consider the tissue-specific textures. Our previous work showed the tissue-specific textures can be enhanced by extracting the tissue-specific MRF from the to-be-reconstructed subject's previous full-dose CT (FdCT) scans. However, the same subject's FdCT scans might not be available in some applications. To address this limitation, we have also investigated the feasibility of extracting the tissue-specific textures from an existing FdCT database instead of the to-be-reconstructed subject. This study aims to implement a machine learning strategy to realize the feasibility. Specifically, we trained a Random Forest (RF) model to learn the intrinsic relationship between the tissue textures and subjects' physiological features. By learning this intrinsic correlation, this model can be used to identify one MRF candidate from the database as the prior knowledge for any subject's current ULdCT image reconstruction. Besides the conventional physiological factors (like body mass index: BMI, gender, age), we further introduced another two features LungMark and BodyAngle to address the scanning position and angle. The experimental results showed that the BMI and LungMark are two features of the most importance for the classification. Our trained model can predict 0.99 precision at the recall rate of 2%, which means that for each subject, there will be 3390*0.02 = 67.8 valid MRF candidates in the database, where 3,390 is the total number of candidates in the database. Moreover, it showed that introducing the ULdCT texture prior into the RF model can increase the recall rate by 3% while the precision remaining 0.99.