Yongfeng Gao1, Zhengrong Liang2, Yuxiang Xing3, Hao Zhang4, Marc Pomeroy5, Siming Lu5, Jianhua Ma6, Hongbing Lu7, William Moore8. 1. Department of Radiology, Stony Brook University, Stony Brook, NY, 11794, USA. 2. Departments of Radiology, Biomedical Engineering, Computer Science, and Electrical Engineering, Stony Brook University, Stony Brook, NY, 11794, USA. 3. Department of Engineering Physics, Tsinghua University, Beijing, 100871, China. 4. Departments of Radiology and Biomedical Engineering, Stony Brook University, Stony Brook, NY, 11794, USA. 5. Departments of Radiology and Biomedical Engineering, State University of New York at Stony Brook, Stony Brook, NY, 11794, USA. 6. School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China. 7. Department of Biomedical Engineering, Fourth Military Medical University, Xi'an, 710032, China. 8. Department of Radiology, New York University, New York, NY, 10016, USA.
Abstract
PURPOSE: Tissue textures have been recognized as biomarkers for various clinical tasks. In computed tomography (CT) image reconstruction, it is important but challenging to preserve the texture when lowering x-ray exposure from full- toward low-/ultra-low dose level. Therefore, this paper aims to explore the texture-dose relationship within one tissue-specific pre-log Bayesian CT reconstruction algorithm. METHODS: To enhance the texture in ultra-low dose CT (ULdCT) reconstruction, this paper presents a Bayesian type algorithm. A shifted Poisson model is adapted to describe the statistical properties of pre-log data, and a tissue-specific Markov random field prior (MRFt) is used to incorporate tissue texture from previous full-dose CT, thus called SP-MRFt algorithm. Utilizing the SP-MRFt algorithm, we investigated tissue texture degradation as a function of x-ray dose levels from full dose (100 mAs/120 kVp) to ultralow dose (1 mAs/120 kVp) by using quantitative texture-based evaluation metrics. RESULTS: Experimental results show the SP-MRFt algorithm outperforms conventional filtered back projection (FBP) and post-log domain penalized weighted least square MRFt (PWLS-MRFt) in terms of noise suppression and texture preservation. Comparable results are also obtained with shifted Poisson model with 7 × 7 Huber MRF weights (SP-Huber7). The investigation on texture-dose relationship shows that the quantified texture measures drop monotonically as dose level decreases, and interestingly a turning point is observed on the texture-dose response curve. CONCLUSIONS: This important observation implies that there exists a minimum dose level, at which a given CT scanner (hardware configuration and image reconstruction software) can achieve without compromising clinical tasks. Moreover, the experiment results show that the variance of electronic noise has higher impact than the mean to the texture-dose relationship.
PURPOSE: Tissue textures have been recognized as biomarkers for various clinical tasks. In computed tomography (CT) image reconstruction, it is important but challenging to preserve the texture when lowering x-ray exposure from full- toward low-/ultra-low dose level. Therefore, this paper aims to explore the texture-dose relationship within one tissue-specific pre-log Bayesian CT reconstruction algorithm. METHODS: To enhance the texture in ultra-low dose CT (ULdCT) reconstruction, this paper presents a Bayesian type algorithm. A shifted Poisson model is adapted to describe the statistical properties of pre-log data, and a tissue-specific Markov random field prior (MRFt) is used to incorporate tissue texture from previous full-dose CT, thus called SP-MRFt algorithm. Utilizing the SP-MRFt algorithm, we investigated tissue texture degradation as a function of x-ray dose levels from full dose (100 mAs/120 kVp) to ultralow dose (1 mAs/120 kVp) by using quantitative texture-based evaluation metrics. RESULTS: Experimental results show the SP-MRFt algorithm outperforms conventional filtered back projection (FBP) and post-log domain penalized weighted least square MRFt (PWLS-MRFt) in terms of noise suppression and texture preservation. Comparable results are also obtained with shifted Poisson model with 7 × 7 Huber MRF weights (SP-Huber7). The investigation on texture-dose relationship shows that the quantified texture measures drop monotonically as dose level decreases, and interestingly a turning point is observed on the texture-dose response curve. CONCLUSIONS: This important observation implies that there exists a minimum dose level, at which a given CT scanner (hardware configuration and image reconstruction software) can achieve without compromising clinical tasks. Moreover, the experiment results show that the variance of electronic noise has higher impact than the mean to the texture-dose relationship.
Authors: Adam M Alessio; Paul E Kinahan; Ken Sauer; Mannudeep K Kalra; Bruno De Man Journal: IEEE Trans Med Imaging Date: 2016-11-09 Impact factor: 10.048
Authors: Yifan Hu; Zhengrong Liang; Bowen Song; Hao Han; Perry J Pickhardt; Wei Zhu; Chaijie Duan; Hao Zhang; Matthew A Barish; Chris E Lascarides Journal: IEEE Trans Med Imaging Date: 2016-01-18 Impact factor: 10.048
Authors: Hugo J W L Aerts; Emmanuel Rios Velazquez; Ralph T H Leijenaar; Chintan Parmar; Patrick Grossmann; Sara Carvalho; Sara Cavalho; Johan Bussink; René Monshouwer; Benjamin Haibe-Kains; Derek Rietveld; Frank Hoebers; Michelle M Rietbergen; C René Leemans; Andre Dekker; John Quackenbush; Robert J Gillies; Philippe Lambin Journal: Nat Commun Date: 2014-06-03 Impact factor: 14.919