PMID- 27147376 OWN - NLM STAT- MEDLINE DCOM- 20170206 LR - 20191210 IS - 2473-4209 (Electronic) IS - 0094-2405 (Print) IS - 0094-2405 (Linking) VI - 43 IP - 5 DP - 2016 May TI - Noise suppression for dual-energy CT via penalized weighted least-square optimization with similarity-based regularization. PG - 2676 LID - 10.1118/1.4947485 [doi] AB - PURPOSE: Dual-energy CT (DECT) expands applications of CT imaging in its capability to decompose CT images into material images. However, decomposition via direct matrix inversion leads to large noise amplification and limits quantitative use of DECT. Their group has previously developed a noise suppression algorithm via penalized weighted least-square optimization with edge-preservation regularization (PWLS-EPR). In this paper, the authors improve method performance using the same framework of penalized weighted least-square optimization but with similarity-based regularization (PWLS-SBR), which substantially enhances the quality of decomposed images by retaining a more uniform noise power spectrum (NPS). METHODS: The design of PWLS-SBR is based on the fact that averaging pixels of similar materials gives a low-noise image. For each pixel, the authors calculate the similarity to other pixels in its neighborhood by comparing CT values. Using an empirical Gaussian model, the authors assign high/low similarity value to one neighboring pixel if its CT value is close/far to the CT value of the pixel of interest. These similarity values are organized in matrix form, such that multiplication of the similarity matrix to the image vector reduces image noise. The similarity matrices are calculated on both high- and low-energy CT images and averaged. In PWLS-SBR, the authors include a regularization term to minimize the L-2 norm of the difference between the images without and with noise suppression via similarity matrix multiplication. By using all pixel information of the initial CT images rather than just those lying on or near edges, PWLS-SBR is superior to the previously developed PWLS-EPR, as supported by comparison studies on phantoms and a head-and-neck patient. RESULTS: On the line-pair slice of the Catphan((c))600 phantom, PWLS-SBR outperforms PWLS-EPR and retains spatial resolution of 8 lp/cm, comparable to the original CT images, even at 90% reduction in noise standard deviation (STD). Similar performance on spatial resolution is observed on an anthropomorphic head phantom. In addition, results of PWLS-SBR show substantially improved image quality due to preservation of image NPS. On the Catphan((c))600 phantom, NPS using PWLS-SBR has a correlation of 93% with that via direct matrix inversion, while the correlation drops to -52% for PWLS-EPR. Electron density measurement studies indicate high accuracy of PWLS-SBR. On seven different materials, the measured electron densities calculated from the decomposed material images using PWLS-SBR have a root-mean-square error (RMSE) of 1.20%, while the results of PWLS-EPR have a RMSE of 2.21%. In the study on a head-and-neck patient, PWLS-SBR is shown to reduce noise STD by a factor of 3 on material images with image qualities comparable to CT images, whereas fine structures are lost in the PWLS-EPR result. Additionally, PWLS-SBR better preserves low contrast on the tissue image. CONCLUSIONS: The authors propose improvements to the regularization term of an optimization framework which performs iterative image-domain decomposition for DECT with noise suppression. The regularization term avoids calculation of image gradient and is based on pixel similarity. The proposed method not only achieves a high decomposition accuracy, but also improves over the previous algorithm on NPS as well as spatial resolution. FAU - Harms, Joseph AU - Harms J AD - Nuclear and Radiological Engineering and Medical Physics Programs, The George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332. FAU - Wang, Tonghe AU - Wang T AD - Nuclear and Radiological Engineering and Medical Physics Programs, The George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332. FAU - Petrongolo, Michael AU - Petrongolo M AD - Nuclear and Radiological Engineering and Medical Physics Programs, The George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332. FAU - Niu, Tianye AU - Niu T AD - Sir Run Run Shaw Hospital, Zhejiang University School of Medicine; Institute of Translational Medicine, Zhejiang University, Hangzhou, Zhejiang, 310016, People's Republic of China. FAU - Zhu, Lei AU - Zhu L AD - Nuclear and Radiological Engineering and Medical Physics Programs, The George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332. LA - eng GR - R21 EB019597/EB/NIBIB NIH HHS/United States PT - Evaluation Study PT - Journal Article PL - United States TA - Med Phys JT - Medical physics JID - 0425746 SB - IM MH - Algorithms MH - Head/diagnostic imaging MH - Head and Neck Neoplasms/diagnostic imaging MH - Humans MH - Image Processing, Computer-Assisted/*methods MH - Least-Squares Analysis MH - Models, Anatomic MH - Phantoms, Imaging MH - Tomography, X-Ray Computed/instrumentation/*methods PMC - PMC4859835 EDAT- 2016/05/06 06:00 MHDA- 2017/02/07 06:00 PMCR- 2017/05/01 CRDT- 2016/05/06 06:00 PHST- 2016/05/06 06:00 [entrez] PHST- 2016/05/06 06:00 [pubmed] PHST- 2017/02/07 06:00 [medline] PHST- 2017/05/01 00:00 [pmc-release] AID - 066605MPH [pii] AID - 1.4947485 [pii] AID - 10.1118/1.4947485 [doi] PST - ppublish SO - Med Phys. 2016 May;43(5):2676. doi: 10.1118/1.4947485.