Characterization of tissue-specific pre-log Bayesian CT reconstruction by 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.
['Gao|Yongfeng|Y|', 'Liang|Zhengrong|Z|', 'Xing|Yuxiang|Y|', 'Zhang|Hao|H|', 'Pomeroy|Marc|M|', 'Lu|Siming|S|', 'Ma|Jianhua|J|', 'Lu|Hongbing|H|', 'Moore|William|W|']
D000465:Algorithms D001499:Bayes Theorem D007091:Image Processing, Computer-Assisted D019047:Phantoms, Imaging D011829:Radiation Dosage D011857:Radiographic Image Interpretation, Computer-Assisted D014057:Tomography, X-Ray Computed
CT texture-dose response curve electronic background noise pre-log Bayesian image reconstruction shifted Poisson fidelity model tissue-specific prior model