Repeated computed tomography (CT) scans are required for some clinical applications such as image-guided interventions. To optimize radiation dose utility, a normal-dose scan is often first performed to set up reference, followed by a series of low-dose scans for intervention. One common strategy to achieve the low-dose scan is to lower the x-ray tube current and exposure time (mAs) or tube voltage (kVp) setting in the scanning protocol, but the resulted image quality by the conventional filtered back-projection (FBP) method may be severely degraded due to the excessive noise. Penalized weighted least-squares (PWLS) image reconstruction has shown the potential to significantly improve the image quality from low-mAs acquisitions, where the penalty plays an important role. In this work, the authors' explore an adaptive Markov random field (MRF)-based penalty term by utilizing previous normal-dose scan to improve the subsequent low-dose scans image reconstruction.
In this work, the authors employ the widely-used quadratic-form MRF as the penalty model and explore a novel idea of using the previous normal-dose scan to obtain the MRF coefficients for adaptive reconstruction of the low-dose images. In the coefficients determination, the authors further explore another novel idea of using the normal-dose scan to obtain a scale map, which describes an optimal neighborhood for the coefficients determination such that a local uniform region has a small spread of frequency spectrum and, therefore, a small MRF window, andvice versa. The proposed penalty term is incorporated into the PWLS image reconstruction framework, and the low-dose images are reconstructed via the PWLS minimization.
The presented adaptive MRF based PWLS algorithm was validated by physical phantom and patient data. The experimental results demonstrated that the presented algorithm is superior to the PWLS reconstruction using the conventional Gaussian MRF penalty or the edge-preserving Huber penalty and the conventional FBP method, in terms of image noise reduction and edge/detail/contrast preservation.
This study demonstrated the feasibility and efficacy of the proposed scheme in utilizing previous normal-dose CT scan to improve the subsequent low-dose scans.
This work was partly supported by the National Institutes of Health under Grant Nos. CA082402 and CA143111 of the National Cancer Institute. J.W. was supported in part by a grant from the Cancer Prevention and Research Institute of Texas (RP110562-P2) and a grant from the American Cancer Society (RSG-13-326-01-CCE). J.M. was partially supported by the NSF of China under Grant Nos. 81371544, 81000613, and 81101046 and the National Key Technologies R&D Program of China under Grant No. 2011BAI12B03. The authors would also like to thank the anonymous reviewers for their constructive comments and suggestions.
II.A. Statistical model
II.B. PWLS criterion
II.C. Penalty or regularization term
II.C.1. Computation of object scale
II.C.2. Determination of MRF window size and sample window size
II.C.3. Prediction of MRF coefficients
II.D. Selection of smoothing parameter β
II.E. Summary of presented PWLS reconstruction method
III. EXPERIMENTS AND RESULTS
III.A. Anthropomorphic torso phantom study
III.A.1. Data acquisition
III.A.2. Visualization-based evaluation
III.A.3. Quantitative evaluation
III.A.4. Profile-based evaluation
III.B. Patient data study
III.B.1. Data acquisition
III.B.2. Visualization-based evaluation
III.B.3. Quantitative evaluation
III.B.4. Profile-based evaluation
III.B.5. Contrast preservation evaluation
IV. DISCUSSION AND CONCLUSION
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