PMID- 33014727 OWN - NLM STAT- PubMed-not-MEDLINE LR - 20220417 IS - 2223-4292 (Print) IS - 2223-4306 (Electronic) IS - 2223-4306 (Linking) VI - 10 IP - 10 DP - 2020 Oct TI - Multi-energy CT reconstruction using tensor nonlocal similarity and spatial sparsity regularization. PG - 1940-1960 LID - 10.21037/qims-20-594 [doi] AB - BACKGROUND: Multi-energy computed tomography (MECT) based on a photon-counting detector is an emerging imaging modality that collects projections at several energy bins with a single scan. However, the limited number of photons collected into the divided, narrow energy bins results in high quantum noise levels in reconstructed images. This study aims to improve MECT image quality by minimizing noise levels while retaining image details. METHODS: A novel MECT reconstruction method was proposed by exploiting the nonlocal tensor similarity among interchannel images and spatial sparsity in single-channel images. Similar patches were initially extracted from the interchannel images in spectral and spatial domains, then stacked into a new three-order tensor. Intrinsic tensor sparsity regularization that combined the Tuker and canonical polyadic (CP) low-rank decomposition techniques were applied to exploit the nonlocal similarity of the formulated tensor. Spatial sparsity in single-channel images was modeled by total variation (TV) regularization that utilizes the compressibility of gradient image. A new MECT reconstruction model was established by simultaneously incorporating the intrinsic tensor sparsity and TV regularizations. The iterative alternating minimization method was utilized to solve the reconstruction model based on a flexible framework. RESULTS: The proposed method was applied to the digital phantom and real mouse data to assess its feasibility and reliability. The reconstruction and decomposition results in the mouse data were encouraging and demonstrated the ability of the proposed method in noise suppression while preserving image details, not observed with other methods. Imaging data from the digital phantom illustrated this method as achieving the best intuitive reconstruction and decomposition results among all compared methods. They reduced the root mean square error (RMSE) by 89.75%, 50.75%, and 36.54% on the reconstructed images compared with analytic, TV-based, and tensor-based methods, respectively. This phenomenon was also observed with decomposition results, where the RMSE was also reduced by 97.96%, 67.74%, 72.05%, respectively. CONCLUSIONS: In this study, we proposed a reconstruction method for photon counting detector-based MECT, using the intrinsic tensor sparsity and TV regularizations. Improvements in noise suppression and detail preservation in the digital phantom and real mouse data were validated by the qualitative and quantitative evaluations on the reconstruction and decomposition results, verifying the potential of the proposed method in MECT reconstruction. CI - 2020 Quantitative Imaging in Medicine and Surgery. All rights reserved. FAU - Zhang, Wenkun AU - Zhang W AD - Key Laboratory of Imaging and Intelligent Processing of Henan Province, PLA Strategic Support Force Information Engineering University, Zhengzhou, China. FAU - Liang, Ningning AU - Liang N AD - Key Laboratory of Imaging and Intelligent Processing of Henan Province, PLA Strategic Support Force Information Engineering University, Zhengzhou, China. FAU - Wang, Zhe AU - Wang Z AD - Beijing Engineering Research Center of Radiographic Techniques and Equipment, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, China. FAU - Cai, Ailong AU - Cai A AD - Key Laboratory of Imaging and Intelligent Processing of Henan Province, PLA Strategic Support Force Information Engineering University, Zhengzhou, China. FAU - Wang, Linyuan AU - Wang L AD - Key Laboratory of Imaging and Intelligent Processing of Henan Province, PLA Strategic Support Force Information Engineering University, Zhengzhou, China. FAU - Tang, Chao AU - Tang C AD - Key Laboratory of Imaging and Intelligent Processing of Henan Province, PLA Strategic Support Force Information Engineering University, Zhengzhou, China. FAU - Zheng, Zhizhong AU - Zheng Z AD - Key Laboratory of Imaging and Intelligent Processing of Henan Province, PLA Strategic Support Force Information Engineering University, Zhengzhou, China. FAU - Li, Lei AU - Li L AD - Key Laboratory of Imaging and Intelligent Processing of Henan Province, PLA Strategic Support Force Information Engineering University, Zhengzhou, China. FAU - Yan, Bin AU - Yan B AD - Key Laboratory of Imaging and Intelligent Processing of Henan Province, PLA Strategic Support Force Information Engineering University, Zhengzhou, China. FAU - Hu, Guoen AU - Hu G AD - Key Laboratory of Imaging and Intelligent Processing of Henan Province, PLA Strategic Support Force Information Engineering University, Zhengzhou, China. LA - eng PT - Journal Article PL - China TA - Quant Imaging Med Surg JT - Quantitative imaging in medicine and surgery JID - 101577942 PMC - PMC7495318 OTO - NOTNLM OT - Multi-energy CT reconstruction OT - spatial sparsity OT - tensor nonlocal similarity COIS- Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at http://dx.doi.org/10.21037/qims-20-594). The authors have no conflicts of interest to declare. EDAT- 2020/10/06 06:00 MHDA- 2020/10/06 06:01 PMCR- 2020/10/01 CRDT- 2020/10/05 06:20 PHST- 2020/10/05 06:20 [entrez] PHST- 2020/10/06 06:00 [pubmed] PHST- 2020/10/06 06:01 [medline] PHST- 2020/10/01 00:00 [pmc-release] AID - qims-10-10-1940 [pii] AID - 10.21037/qims-20-594 [doi] PST - ppublish SO - Quant Imaging Med Surg. 2020 Oct;10(10):1940-1960. doi: 10.21037/qims-20-594.