PET reconstruction using a cooperative coevolution strategy in LOR space

Vidal, Franck; Louchet, Jean; Rocchisani, Jean-Marie; Lutton, Évelyne

doi:10.1109/nssmic.2009.5401758

Cited by 5 publications

(11 citation statements)

References 11 publications

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“…In past implementations [1,5,[45][46][47][48], the final result given by the last iteration is considered as the reconstructed image. It provides a simple way to extract the answer of the optimisation problem, but it is not certain that it is the best answer that the evolutionary process provided.…”

Section: Resultsmentioning

confidence: 99%

“…The intensity of pixel(r, ) in the sinogram is derived from the number of corresponding LOR events detected by the PET scanner. Note that we will use this format in this paper, although we demonstrated in [45,46] that LOR data can be used within our algorithm.…”

Section: Emission Tomography In Nuclear Medicinementioning

confidence: 99%

“…It is a class of stochastic optimisation tool that relies on Darwin's principles to mimic complex natural behaviours [10]. A proof-of-concept of evolutionary reconstruction was initially developed with a relatively basic Fly Algorithm [45,46]. The implementation supported list-mode reconstruction using LOR data as well as conventional reconstruction using sinograms.…”

Section: Emission Tomography In Nuclear Medicinementioning

confidence: 99%

“…A decade after the initial developments of the Fly Algorithm in robotics; it was adapted to SPECT reconstruction [18], then to PET [1,5,[45][46][47][48]. It has also be used in filtering to generate artistic effects on images [2][3][4].…”

Section: Fly Algorithm and Its Applicationsmentioning

confidence: 99%

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Data exploration in evolutionary reconstruction of PET images

Gray

Al-Maliki

Vidal

2018

Genet Program Evolvable Mach

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This work is based on a cooperative co-evolution algorithm called 'Fly Algorithm', which is an evolutionary algorithm (EA) where individuals are called 'flies'. It is a specific case of the 'Parisian Approach' where the solution of an optimisation problem is a set of individuals (e.g. the whole population) instead of a single individual (the best one) as in typical EAs. The optimisation problem considered here is tomography reconstruction in positron emission tomography (PET). It estimates the concentration of a radioactive substance (called a radiotracer) within the body. Tomography, in this context, is considered as a difficult ill-posed inverse problem. The Fly Algorithm aims at optimising the position of 3-D points that mimic the radiotracer. At the end of the optimisation process, the fly population is extracted as it corresponds to an estimate of the radioactive concentration. During the optimisation loop a lot of data is generated by the algorithm, such as image metrics, duration, and internal states. This data is recorded in a log file that can be post-processed and visualised. We propose using information visualisation and user interaction techniques to explore the algorithm's internal data. Our aim is to better understand what happens during the evolutionary loop. Using an example, we demonstrate that it is possible to interactively discover when an early termination could be triggered. It is implemented in a new stopping criterion. It is tested on two other examples on which it leads to a 60% reduction of the number of iterations without any loss of accuracy.

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Section: Resultsmentioning

confidence: 99%

Section: Emission Tomography In Nuclear Medicinementioning

confidence: 99%

Section: Emission Tomography In Nuclear Medicinementioning

confidence: 99%

Section: Fly Algorithm and Its Applicationsmentioning

confidence: 99%

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Data exploration in evolutionary reconstruction of PET images

Gray

Al-Maliki

Vidal

2018

Genet Program Evolvable Mach

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“…Evolutionary algorithms have been proven efficient to solve the inverse problem of 3D data reconstruction in tomography [1], and particularly of positron emission tomography (PET) reconstruction in nuclear medicine [2][3][4].…”

Section: Introductionmentioning

confidence: 99%

Threshold Selection, Mitosis and Dual Mutation in Cooperative Co-evolution: Application to Medical 3D Tomography

Vidal

Lutton

Louchet

et al. 2010

Parallel Problem Solving From Nature, PPSN XI

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We present and analyse the behaviour of specialised operators designed for cooperative coevolution strategy in the framework of 3D tomographic PET reconstruction. The basis is a simple cooperative co-evolution scheme (the "fly algorithm"), which embeds the searched solution in the whole population, letting each individual be only a part of the solution. An individual, or fly, is a 3D point that emits positrons. Using a cooperative co-evolution scheme to optimize the position of positrons, the population of flies evolves so that the data estimated from flies matches measured data. The final population approximates the radioactivity concentration. In this paper, three operators are proposed, threshold selection, mitosis and dual mutation, and their impact on the algorithm efficiency is experimentally analysed on a controlled test-case. Their extension to other cooperative co-evolution schemes is discussed.

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Fine-Grained Cooperative Coevolution in a Single Population: Between Evolution and Swarm Intelligence

Lutton,

Al-Maliki,

Louchet

et al. 2023

Lecture Notes in Computer Science

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Particle Swarm Optimisation (PSO) and Evolutionary Algorithms (EAs) differ in various ways, in particular with respect to information sharing and diversity management, making their scopes of applications very diverse. Combining the advantages of both approaches is very attractive and has been successfully achieved through hybridisation. Another possible improvement, notably for addressing scalability issues, is cooperation. It has first been developed for co-evolution in EA techniques and it is now used in PSO. However, until now, attempts to make PSO cooperate have been based on multi-population schemes almost exclusively. The focus of this paper is set on single-population schemes, or fine-grained cooperation. By analogy with an evolutionary scheme that has long been proved effective, the fly algorithm (FA), we design and compare a cooperative PSO (coPSO), and a PSO-flavoured fly algorithm. Experiments run on a benchmark, the Lamp problem, show that fine-grained cooperation based on marginal fitness evaluations and steady-state schemes outperforms classical techniques when the dimension of the problem increases. These preliminary results highlight interesting future directions of research on fine-grained cooperation schemes, by combining features of PSO and FA.1. Particles are initialised with random positions and velocities.

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PET reconstruction using a cooperative coevolution strategy in LOR space

Cited by 5 publications

References 11 publications

Data exploration in evolutionary reconstruction of PET images

Data exploration in evolutionary reconstruction of PET images

Threshold Selection, Mitosis and Dual Mutation in Cooperative Co-evolution: Application to Medical 3D Tomography

Fine-Grained Cooperative Coevolution in a Single Population: Between Evolution and Swarm Intelligence

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