Transfer-learning is a key ingredient to fast deep learning-based 4D liver MRI reconstruction
Abstract Time-resolved volumetric magnetic resonance imaging (4D MRI) could be used to address organ motion in image-guided interventions like tumor ablation. Current 4D reconstruction techniques are unsuitable for most interventional settings because they are limited to specific breathing phases, l...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-07-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-38073-1 |
| _version_ | 1797779011752951808 |
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| author | Gino Gulamhussene Marko Rak Oleksii Bashkanov Fabian Joeres Jazan Omari Maciej Pech Christian Hansen |
| author_facet | Gino Gulamhussene Marko Rak Oleksii Bashkanov Fabian Joeres Jazan Omari Maciej Pech Christian Hansen |
| author_sort | Gino Gulamhussene |
| collection | DOAJ |
| description | Abstract Time-resolved volumetric magnetic resonance imaging (4D MRI) could be used to address organ motion in image-guided interventions like tumor ablation. Current 4D reconstruction techniques are unsuitable for most interventional settings because they are limited to specific breathing phases, lack temporal/spatial resolution, and have long prior acquisitions or reconstruction times. Deep learning-based (DL) 4D MRI approaches promise to overcome these shortcomings but are sensitive to domain shift. This work shows that transfer learning (TL) combined with an ensembling strategy can help alleviate this key challenge. We evaluate four approaches: pre-trained models from the source domain, models directly trained from scratch on target domain data, models fine-tuned from a pre-trained model and an ensemble of fine-tuned models. For that the data base was split into 16 source and 4 target domain subjects. Comparing ensemble of fine-tuned models (N = 10) with directly learned models, we report significant improvements (P < 0.001) of the root mean squared error (RMSE) of up to 12% and the mean displacement (MDISP) of up to 17.5%. The smaller the target domain data amount, the larger the effect. This shows that TL + Ens significantly reduces beforehand acquisition time and improves reconstruction quality, rendering it a key component in making 4D MRI clinically feasible for the first time in the context of 4D organ motion models of the liver and beyond. |
| first_indexed | 2024-03-12T23:24:38Z |
| format | Article |
| id | doaj.art-dafafdd333aa46dd9b0d411af05ef1c1 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-12T23:24:38Z |
| publishDate | 2023-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-dafafdd333aa46dd9b0d411af05ef1c12023-07-16T11:14:08ZengNature PortfolioScientific Reports2045-23222023-07-0113111210.1038/s41598-023-38073-1Transfer-learning is a key ingredient to fast deep learning-based 4D liver MRI reconstructionGino Gulamhussene0Marko Rak1Oleksii Bashkanov2Fabian Joeres3Jazan Omari4Maciej Pech5Christian Hansen6Otto-von-Guericke University Magdeburg, Faculty of Computer ScienceOtto-von-Guericke University Magdeburg, Faculty of Computer ScienceOtto-von-Guericke University Magdeburg, Faculty of Computer ScienceOtto-von-Guericke University Magdeburg, Faculty of Computer ScienceDepartment of Radiology and Nuclear Medicine, University Hospital MagdeburgDepartment of Radiology and Nuclear Medicine, University Hospital MagdeburgOtto-von-Guericke University Magdeburg, Faculty of Computer ScienceAbstract Time-resolved volumetric magnetic resonance imaging (4D MRI) could be used to address organ motion in image-guided interventions like tumor ablation. Current 4D reconstruction techniques are unsuitable for most interventional settings because they are limited to specific breathing phases, lack temporal/spatial resolution, and have long prior acquisitions or reconstruction times. Deep learning-based (DL) 4D MRI approaches promise to overcome these shortcomings but are sensitive to domain shift. This work shows that transfer learning (TL) combined with an ensembling strategy can help alleviate this key challenge. We evaluate four approaches: pre-trained models from the source domain, models directly trained from scratch on target domain data, models fine-tuned from a pre-trained model and an ensemble of fine-tuned models. For that the data base was split into 16 source and 4 target domain subjects. Comparing ensemble of fine-tuned models (N = 10) with directly learned models, we report significant improvements (P < 0.001) of the root mean squared error (RMSE) of up to 12% and the mean displacement (MDISP) of up to 17.5%. The smaller the target domain data amount, the larger the effect. This shows that TL + Ens significantly reduces beforehand acquisition time and improves reconstruction quality, rendering it a key component in making 4D MRI clinically feasible for the first time in the context of 4D organ motion models of the liver and beyond.https://doi.org/10.1038/s41598-023-38073-1 |
| spellingShingle | Gino Gulamhussene Marko Rak Oleksii Bashkanov Fabian Joeres Jazan Omari Maciej Pech Christian Hansen Transfer-learning is a key ingredient to fast deep learning-based 4D liver MRI reconstruction Scientific Reports |
| title | Transfer-learning is a key ingredient to fast deep learning-based 4D liver MRI reconstruction |
| title_full | Transfer-learning is a key ingredient to fast deep learning-based 4D liver MRI reconstruction |
| title_fullStr | Transfer-learning is a key ingredient to fast deep learning-based 4D liver MRI reconstruction |
| title_full_unstemmed | Transfer-learning is a key ingredient to fast deep learning-based 4D liver MRI reconstruction |
| title_short | Transfer-learning is a key ingredient to fast deep learning-based 4D liver MRI reconstruction |
| title_sort | transfer learning is a key ingredient to fast deep learning based 4d liver mri reconstruction |
| url | https://doi.org/10.1038/s41598-023-38073-1 |
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