Fast Compressed Sensing of 3D Radial T<sub>1</sub> Mapping with Different Sparse and Low-Rank Models
Knowledge of the relative performance of the well-known sparse and low-rank compressed sensing models with 3D radial quantitative magnetic resonance imaging acquisitions is limited. We use 3D radial T<sub>1</sub> relaxation time mapping data to compare the total variation, low-rank, and...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-07-01
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| Series: | Journal of Imaging |
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| Online Access: | https://www.mdpi.com/2313-433X/9/8/151 |
| _version_ | 1797584300593381376 |
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| author | Antti Paajanen Matti Hanhela Nina Hänninen Olli Nykänen Ville Kolehmainen Mikko J. Nissi |
| author_facet | Antti Paajanen Matti Hanhela Nina Hänninen Olli Nykänen Ville Kolehmainen Mikko J. Nissi |
| author_sort | Antti Paajanen |
| collection | DOAJ |
| description | Knowledge of the relative performance of the well-known sparse and low-rank compressed sensing models with 3D radial quantitative magnetic resonance imaging acquisitions is limited. We use 3D radial T<sub>1</sub> relaxation time mapping data to compare the total variation, low-rank, and Huber penalty function approaches to regularization to provide insights into the relative performance of these image reconstruction models. Simulation and ex vivo specimen data were used to determine the best compressed sensing model as measured by normalized root mean squared error and structural similarity index. The large-scale compressed sensing models were solved by combining a GPU implementation of a preconditioned primal-dual proximal splitting algorithm to provide high-quality T<sub>1</sub> maps within a feasible computation time. The model combining spatial total variation and locally low-rank regularization yielded the best performance, followed closely by the model combining spatial and contrast dimension total variation. Computation times ranged from 2 to 113 min, with the low-rank approaches taking the most time. The differences between the compressed sensing models are not necessarily large, but the overall performance is heavily dependent on the imaged object. |
| first_indexed | 2024-03-10T23:50:55Z |
| format | Article |
| id | doaj.art-d6c06fe664f04efcb1dcb14fbeb52346 |
| institution | Directory Open Access Journal |
| issn | 2313-433X |
| language | English |
| last_indexed | 2024-03-10T23:50:55Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Imaging |
| spelling | doaj.art-d6c06fe664f04efcb1dcb14fbeb523462023-11-19T01:43:31ZengMDPI AGJournal of Imaging2313-433X2023-07-019815110.3390/jimaging9080151Fast Compressed Sensing of 3D Radial T<sub>1</sub> Mapping with Different Sparse and Low-Rank ModelsAntti Paajanen0Matti Hanhela1Nina Hänninen2Olli Nykänen3Ville Kolehmainen4Mikko J. Nissi5Department of Technical Physics, University of Eastern Finland, 70211 Kuopio, FinlandDepartment of Technical Physics, University of Eastern Finland, 70211 Kuopio, FinlandDepartment of Technical Physics, University of Eastern Finland, 70211 Kuopio, FinlandDepartment of Technical Physics, University of Eastern Finland, 70211 Kuopio, FinlandDepartment of Technical Physics, University of Eastern Finland, 70211 Kuopio, FinlandDepartment of Technical Physics, University of Eastern Finland, 70211 Kuopio, FinlandKnowledge of the relative performance of the well-known sparse and low-rank compressed sensing models with 3D radial quantitative magnetic resonance imaging acquisitions is limited. We use 3D radial T<sub>1</sub> relaxation time mapping data to compare the total variation, low-rank, and Huber penalty function approaches to regularization to provide insights into the relative performance of these image reconstruction models. Simulation and ex vivo specimen data were used to determine the best compressed sensing model as measured by normalized root mean squared error and structural similarity index. The large-scale compressed sensing models were solved by combining a GPU implementation of a preconditioned primal-dual proximal splitting algorithm to provide high-quality T<sub>1</sub> maps within a feasible computation time. The model combining spatial total variation and locally low-rank regularization yielded the best performance, followed closely by the model combining spatial and contrast dimension total variation. Computation times ranged from 2 to 113 min, with the low-rank approaches taking the most time. The differences between the compressed sensing models are not necessarily large, but the overall performance is heavily dependent on the imaged object.https://www.mdpi.com/2313-433X/9/8/151compressed sensingT<sub>1</sub> relaxationquantitative magnetic resonance imagingregularizationimage reconstruction |
| spellingShingle | Antti Paajanen Matti Hanhela Nina Hänninen Olli Nykänen Ville Kolehmainen Mikko J. Nissi Fast Compressed Sensing of 3D Radial T<sub>1</sub> Mapping with Different Sparse and Low-Rank Models Journal of Imaging compressed sensing T<sub>1</sub> relaxation quantitative magnetic resonance imaging regularization image reconstruction |
| title | Fast Compressed Sensing of 3D Radial T<sub>1</sub> Mapping with Different Sparse and Low-Rank Models |
| title_full | Fast Compressed Sensing of 3D Radial T<sub>1</sub> Mapping with Different Sparse and Low-Rank Models |
| title_fullStr | Fast Compressed Sensing of 3D Radial T<sub>1</sub> Mapping with Different Sparse and Low-Rank Models |
| title_full_unstemmed | Fast Compressed Sensing of 3D Radial T<sub>1</sub> Mapping with Different Sparse and Low-Rank Models |
| title_short | Fast Compressed Sensing of 3D Radial T<sub>1</sub> Mapping with Different Sparse and Low-Rank Models |
| title_sort | fast compressed sensing of 3d radial t sub 1 sub mapping with different sparse and low rank models |
| topic | compressed sensing T<sub>1</sub> relaxation quantitative magnetic resonance imaging regularization image reconstruction |
| url | https://www.mdpi.com/2313-433X/9/8/151 |
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