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Radiation image reconstruction and uncertainty quantification using a Gaussian process prior.

Jaewon Lee¹, Tenzing H Joshi², Mark S Bandstra²

¹Department of Nuclear Engineering, University of California, Berkeley, Berkeley, CA, 94720, USA. jwonlee@berkeley.edu.

Scientific Reports

|October 3, 2024

Summary

This summary is machine-generated.

We introduce a Bayesian image reconstruction framework using Gaussian process priors (GPP) to enhance image quality and uncertainty quantification. This method improves upon ML-EM, offering better source distribution understanding in radiation imaging.

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Neutron Radiography and Computed Tomography of Biological Systems at the Oak Ridge National Laboratory's High Flux Isotope Reactor

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Published on: May 7, 2021

Area of Science:

Medical Imaging
Computational Science
Statistical Modeling

Background:

Maximum Likelihood Expectation Maximization (ML-EM) algorithms have limitations in image reconstruction.
Bayesian methods offer potential for improved image quality and uncertainty quantification.

Purpose of the Study:

To develop a comprehensive Bayesian framework for image reconstruction and uncertainty quantification.
To overcome limitations of existing ML-EM algorithms using Gaussian Process Priors (GPP).

Main Methods:

Utilized a zero-mean Gaussian Process (GP) with a selectable covariance function for prior distribution.
Employed empirical Bayes for automatic hyperparameter selection, enhancing interpretability.
Integrated structural priors into the GP covariance for multi-modality imaging and data fusion.
Applied Bayesian uncertainty quantification techniques like preconditioned Crank-Nicolson and Laplace approximation.

Main Results:

The GPP framework significantly improved image quality compared to ML-EM.
Demonstrated enhanced understanding of source distribution through uncertainty quantification.
Showcased significant image quality improvements by incorporating structural priors.

Conclusions:

The proposed GPP framework provides a versatile and effective approach for Bayesian image reconstruction.
It offers superior performance over ML-EM, particularly in radiation imaging applications.
The method facilitates robust uncertainty quantification and allows for the integration of prior structural information.