title: PET transmission tomography using a novel nonlocal MRF prior.
abstract: In positron emission tomography, transmission scans can be performed to estimate attenuation correction factors (ACFs) which are in turn used to correct the emission scans. And such an attenuation correction is crucial for quantitatively accurate PET reconstructions. The prior model used in this work was based on our assumption that the attenuation values vary smoothly, with occasional discontinuities at anatomical borders. And on the other hand, long acquisition or scan times, although alleviating the noise effect of the count-limited scans, are blamed for patient uncomfortableness and movements. So, transmission tomography often suffers from the noise effect because of the short scan time. Thus reconstruction which is capable of overcoming the noise effect is highly needed. In this article, we apply the nonlocal prior Bayesian reconstruction method in PET transmission tomography. Resulting experimentations validate that the reconstructions using the nonlocal prior can reconstruct better transmission images and overcome noise effect even when the scan time is relatively short.
authors.affiliation: The Laboratory of Image Science and Technology, Southeast University, China; The School of Biomedical Engineering, Southern Medical University, China.
authors.forename: Yang
authors.initials: Y
authors.lastname: Chen
authors.forename: Liwei
authors.initials: L
authors.lastname: Hao
authors.forename: Xianghua
authors.initials: X
authors.lastname: Ye
authors.forename: Wufan
authors.initials: W
authors.lastname: Chen
authors.forename: Limin
authors.initials: L
authors.lastname: Luo
authors.forename: Xindao
authors.initials: X
authors.lastname: Yin
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003627:Data Interpretation, Statistical; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008390:Markov Chains; D008954:Models, Biological; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D019047:Phantoms, Imaging; D049268:Positron-Emission Tomography / Q000295:instrumentation / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique