Multi-parametric optimization of magnetic resonance imaging sequences for magnetic resonance-guided radiotherapy
Background and purpose: 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...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-10-01
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| Series: | Physics and Imaging in Radiation Oncology |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S240563162300088X |
| _version_ | 1797397558046228480 |
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| author | Hafiz Muhammad Fahad Stefan Dorsch Moritz Zaiss Christian P. Karger |
| author_facet | Hafiz Muhammad Fahad Stefan Dorsch Moritz Zaiss Christian P. Karger |
| author_sort | Hafiz Muhammad Fahad |
| collection | DOAJ |
| description | Background and purpose: 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: 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: 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: 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. |
| first_indexed | 2024-03-09T01:11:47Z |
| format | Article |
| id | doaj.art-8ea0c340a9944beb834ace3571e50eac |
| institution | Directory Open Access Journal |
| issn | 2405-6316 |
| language | English |
| last_indexed | 2024-03-09T01:11:47Z |
| publishDate | 2023-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Physics and Imaging in Radiation Oncology |
| spelling | doaj.art-8ea0c340a9944beb834ace3571e50eac2023-12-11T04:16:29ZengElsevierPhysics and Imaging in Radiation Oncology2405-63162023-10-0128100497Multi-parametric optimization of magnetic resonance imaging sequences for magnetic resonance-guided radiotherapyHafiz Muhammad Fahad0Stefan Dorsch1Moritz Zaiss2Christian P. Karger3German Cancer Research Center (DKFZ), Medical Physics in Radiation Oncology, Heidelberg, Germany; University of Heidelberg, Faculty of Medicine, Heidelberg, Germany; National Center for Radiation Research in Oncology (NCRO), Heidelberg Institute for Radiation Oncology (HIRO), Heidelberg, Germany; Corresponding author.German Cancer Research Center (DKFZ), Medical Physics in Radiation Oncology, Heidelberg, Germany; National Center for Radiation Research in Oncology (NCRO), Heidelberg Institute for Radiation Oncology (HIRO), Heidelberg, Germany; Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, GermanyFriedrich-Alexander Universität Erlangen-Nürnberg (FAU), Institute of Neuroradiology, University Hospital Erlangen, Erlangen, Germany; Magnetic Resonance Center, Max- Planck Institute for Biological Cyberrnetics, Tübingen, GermanyGerman Cancer Research Center (DKFZ), Medical Physics in Radiation Oncology, Heidelberg, Germany; National Center for Radiation Research in Oncology (NCRO), Heidelberg Institute for Radiation Oncology (HIRO), Heidelberg, GermanyBackground and purpose: 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: 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: 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: 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.http://www.sciencedirect.com/science/article/pii/S240563162300088XContrast maximizationMR-guided radiotherapyRemote control MRISupervised learningMR sequences optimization |
| spellingShingle | Hafiz Muhammad Fahad Stefan Dorsch Moritz Zaiss Christian P. Karger Multi-parametric optimization of magnetic resonance imaging sequences for magnetic resonance-guided radiotherapy Physics and Imaging in Radiation Oncology Contrast maximization MR-guided radiotherapy Remote control MRI Supervised learning MR sequences optimization |
| title | Multi-parametric optimization of magnetic resonance imaging sequences for magnetic resonance-guided radiotherapy |
| title_full | Multi-parametric optimization of magnetic resonance imaging sequences for magnetic resonance-guided radiotherapy |
| title_fullStr | Multi-parametric optimization of magnetic resonance imaging sequences for magnetic resonance-guided radiotherapy |
| title_full_unstemmed | Multi-parametric optimization of magnetic resonance imaging sequences for magnetic resonance-guided radiotherapy |
| title_short | Multi-parametric optimization of magnetic resonance imaging sequences for magnetic resonance-guided radiotherapy |
| title_sort | multi parametric optimization of magnetic resonance imaging sequences for magnetic resonance guided radiotherapy |
| topic | Contrast maximization MR-guided radiotherapy Remote control MRI Supervised learning MR sequences optimization |
| url | http://www.sciencedirect.com/science/article/pii/S240563162300088X |
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