Next Article in Journal / Special Issue
Stochastic Gradient Annealed Importance Sampling for Efficient Online Marginal Likelihood Estimation
Previous Article in Journal
Hypergraph Contextuality
Previous Article in Special Issue
Estimating Flight Characteristics of Anomalous Unidentified Aerial Vehicles
Open AccessArticle

Bayesian Maximum-A-Posteriori Approach with Global and Local Regularization to Image Reconstruction Problem in Medical Emission Tomography

Institute of Theoretical and Applied Mechanics, Novosibirsk 630090, Russia
Entropy 2019, 21(11), 1108; https://doi.org/10.3390/e21111108
Received: 9 October 2019 / Revised: 10 November 2019 / Accepted: 10 November 2019 / Published: 12 November 2019
The Bayesian approach Maximum a Posteriori (MAP) provides a common basis for developing statistical methods for solving ill-posed image reconstruction problems. MAP solutions are dependent on a priori model. Approaches developed in literature are based on prior models that describe the properties of the expected image rather than the properties of the studied object. In this paper, such models have been analyzed and it is shown that they lead to global regularization of the solution. Prior models that are based on the properties of the object under study are developed and conditions for local and global regularization are obtained. A new reconstruction algorithm has been developed based on the method of local statistical regularization. Algorithms with global and local regularization were compared in numerical simulations. The simulations were performed close to the real oncologic single photon emission computer tomography (SPECT) study. It is shown that the approach with local regularization produces more accurate images of ‘hot spots’, which is especially important to tumor diagnostics in nuclear oncology. View Full-Text
Keywords: image reconstruction; Bayesian Maximum a Posteriori approach; entropy prior probability; global statistical regularization; local statistical regularization; PET; SPECT image reconstruction; Bayesian Maximum a Posteriori approach; entropy prior probability; global statistical regularization; local statistical regularization; PET; SPECT
Show Figures

Figure 1

MDPI and ACS Style

Denisova, N. Bayesian Maximum-A-Posteriori Approach with Global and Local Regularization to Image Reconstruction Problem in Medical Emission Tomography. Entropy 2019, 21, 1108.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop