Multi-Objective Evolutionary Algorithm for PET Image Reconstruction: Concept

In many diagnostic imaging settings, including positron emission tomography (PET), images are typically used for multiple tasks such as detecting disease and quantifying disease. Unlike conventional image reconstruction that optimizes a single objective, this work proposes a multi-objective optimiza...

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Vydané v:IEEE transactions on medical imaging Ročník 40; číslo 8; s. 2142 - 2151
Hlavní autori: Abouhawwash, Mohamed, Alessio, Adam M.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States IEEE 01.08.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Algorithms
emission tomography
Evolutionary algorithms
Function space
Genetic algorithms
genetic optimization
Image processing
Image reconstruction
Iterative methods
Maximum likelihood estimation
Medical imaging
Multi-objective optimization
Multiple objective analysis
Objective function
Optimization
pareto optimal
PET image reconstruction
Positron emission
Positron emission tomography
Regularization
Sociology
Statistical analysis
Statistics
Tomography
ISSN:0278-0062, 1558-254X, 1558-254X
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Shrnutí:In many diagnostic imaging settings, including positron emission tomography (PET), images are typically used for multiple tasks such as detecting disease and quantifying disease. Unlike conventional image reconstruction that optimizes a single objective, this work proposes a multi-objective optimization algorithm for PET image reconstruction to identify a set of images that are optimal for more than one task. This work is reliant on a genetic algorithm to evolve a set of solutions that satisfies two distinct objectives. In this paper, we defined the objectives as the commonly used Poisson log-likelihood function, typically reflective of quantitative accuracy, and a variant of the generalized scan-statistic model, to reflect detection performance. The genetic algorithm uses new mutation and crossover operations at each iteration. After each iteration, the child population is selected with non-dominated sorting to identify the set of solutions along the dominant front or fronts. After multiple iterations, these fronts approach a single non-dominated optimal front, defined as the set of PET images for which none the objective function values can be improved without reducing the opposing objective function. This method was applied to simulated 2D PET data of the heart and liver with hot features. We compared this approach to conventional, single-objective approaches for trading off performance: maximum likelihood estimation with increasing explicit regularization and maximum a posteriori estimation with varying penalty strength. Results demonstrate that the proposed method generates solutions with comparable to improved objective function values compared to the conventional approaches for trading off performance amongst different tasks. In addition, this approach identifies a diverse set of solutions in the multi-objective function space which can be challenging to estimate with single-objective formulations.
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ISSN:0278-0062
1558-254X
1558-254X
DOI:10.1109/TMI.2021.3073243