Screening is an effective way to detect lung cancer early and can improve the survival rate significantly. The low-dose computed tomography (LdCT) is demanding for lung screening to ensure the exam radiation as low as reasonably possible. The statistical image reconstruction has shown great advantages in LdCT imaging, where many types of priors can be used as constrain for optimal images. The tissue-specific Markov random field (MRF) type texture prior (MRFt) was proposed in our previous work to address the clinical related texture information. For the chest scans, four tissue texture were extracted from regions of lung, bone, fat and muscle respectively. In this work, we focus on the region of interest, i.e. lung for the lung cancer screening. The quantitative texture analysis of normal and abnormal lung tissue was performed to address the following issues of the proposed MRFt model: (1) a more comprehensive understanding of the lung tissue texture (2) what MRF prior we should use for the abnormal lung tissue. Experiments results showed that normal lung tissue has texture similarity among different subjects. The robust similarity among humans laid the feasibility of building the lung tissue database for the LdCT imaging which has no previous FdCT scans. Different abnormal lung tissue varies significantly. There is no way to get the prior knowledge of lung nodules until the CT exam was performed.
Purpose: Bayesian theory provides a sound framework for ultralow-dose computed tomography (ULdCT) image reconstruction with two terms for modeling the data statistical property and incorporating a priori knowledge for the image that is to be reconstructed. We investigate the feasibility of using a machine learning (ML) strategy, particularly the convolutional neural network (CNN), to construct a tissue-specific texture prior from previous full-dose computed tomography.
Approach: Our study constructs four tissue-specific texture priors, corresponding with lung, bone, fat, and muscle, and integrates the prior with the prelog shift Poisson (SP) data property for Bayesian reconstruction of ULdCT images. The Bayesian reconstruction was implemented by an algorithm called SP-CNN-T and compared with our previous Markov random field (MRF)-based tissue-specific texture prior algorithm called SP-MRF-T.
Results: In addition to conventional quantitative measures, mean squared error and peak signal-to-noise ratio, structure similarity index, feature similarity, and texture Haralick features were used to measure the performance difference between SP-CNN-T and SP-MRF-T algorithms in terms of the structure and tissue texture preservation, demonstrating the feasibility and the potential of the investigated ML approach.
Conclusions: Both training performance and image reconstruction results showed the feasibility of constructing CNN texture prior model and the potential of improving the structure preservation of the nodule comparing to our previous regional tissue-specific MRF texture prior model.
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.
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