Multi-parametric optimization of magnetic resonance imaging sequences for magnetic resonance-guided radiotherapy.
Contrast maximization
MR sequences optimization
MR-guided radiotherapy
Remote control MRI
Supervised learning
Journal
Physics and imaging in radiation oncology
ISSN: 2405-6316
Titre abrégé: Phys Imaging Radiat Oncol
Pays: Netherlands
ID NLM: 101704276
Informations de publication
Date de publication:
Oct 2023
Oct 2023
Historique:
received:
12
05
2023
revised:
15
09
2023
accepted:
29
09
2023
medline:
23
10
2023
pubmed:
23
10
2023
entrez:
23
10
2023
Statut:
epublish
Résumé
Magnetic Resonance Imaging (MRI) is widely used in oncology for tumor staging, treatment response assessment, and radiation therapy (RT) planning. This study proposes a framework for automatic optimization of MRI sequences based on pulse sequence parameter sets (SPS) that are directly applied on the scanner, for application in RT planning. A phantom with seven in-house fabricated contrasts was used for measurements. The proposed framework employed a derivative-free optimization algorithm to repeatedly update and execute a parametrized sequence on the MR scanner to acquire new data. In each iteration, the mean-square error was calculated based on the clinical application. Two clinically relevant optimization goals were pursued: achieving the same signal and therefore contrast as in a target image, and maximizing the signal difference (contrast) between specified tissue types. The framework was evaluated using two optimization methods: a covariance matrix adaptation evolution strategy (CMA-ES) and a genetic algorithm (GA). The obtained results demonstrated the potential of the proposed framework for automatic optimization of MRI sequences. Both CMA-ES and GA methods showed promising results in achieving the two optimization goals, however, CMA-ES converged much faster as compared to GA. The proposed framework enables for automatic optimization of MRI sequences based on SPS that are directly applied on the scanner and it may be used to enhance the quality of MRI images for dedicated applications in MR-guided RT.
Sections du résumé
Background and purpose
UNASSIGNED
Magnetic Resonance Imaging (MRI) is widely used in oncology for tumor staging, treatment response assessment, and radiation therapy (RT) planning. This study proposes a framework for automatic optimization of MRI sequences based on pulse sequence parameter sets (SPS) that are directly applied on the scanner, for application in RT planning.
Materials and methods
UNASSIGNED
A phantom with seven in-house fabricated contrasts was used for measurements. The proposed framework employed a derivative-free optimization algorithm to repeatedly update and execute a parametrized sequence on the MR scanner to acquire new data. In each iteration, the mean-square error was calculated based on the clinical application. Two clinically relevant optimization goals were pursued: achieving the same signal and therefore contrast as in a target image, and maximizing the signal difference (contrast) between specified tissue types. The framework was evaluated using two optimization methods: a covariance matrix adaptation evolution strategy (CMA-ES) and a genetic algorithm (GA).
Results
UNASSIGNED
The obtained results demonstrated the potential of the proposed framework for automatic optimization of MRI sequences. Both CMA-ES and GA methods showed promising results in achieving the two optimization goals, however, CMA-ES converged much faster as compared to GA.
Conclusions
UNASSIGNED
The proposed framework enables for automatic optimization of MRI sequences based on SPS that are directly applied on the scanner and it may be used to enhance the quality of MRI images for dedicated applications in MR-guided RT.
Identifiants
pubmed: 37869476
doi: 10.1016/j.phro.2023.100497
pii: S2405-6316(23)00088-X
pmc: PMC10585385
doi:
Types de publication
Journal Article
Langues
eng
Pagination
100497Informations de copyright
© 2023 The Authors.
Déclaration de conflit d'intérêts
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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