SPICER: Self‐supervised learning for MRI with automatic coil sensitivity estimation and reconstruction
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| Main Authors: | , , , , , , , , |
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| Format: | Artículo Open Access |
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Wiley
2024
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| _version_ | 1867017054571200512 |
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| author | Yuyang Hu Weijie Gan Chunwei Ying Tongyao Wang Cihat Eldeniz Jiaming Liu Yasheng Chen Hongyu An Ulugbek S. Kamilov |
| author_facet | Yuyang Hu Weijie Gan Chunwei Ying Tongyao Wang Cihat Eldeniz Jiaming Liu Yasheng Chen Hongyu An Ulugbek S. Kamilov Yuyang Hu Weijie Gan Chunwei Ying Tongyao Wang Cihat Eldeniz Jiaming Liu Yasheng Chen Hongyu An Ulugbek S. Kamilov |
| collection | Wiley Open Access |
| contents | SPICER: Self‐supervised learning for MRI with automatic coil sensitivity estimation and reconstruction Yuyang Hu Weijie Gan Chunwei Ying Tongyao Wang Cihat Eldeniz Jiaming Liu Yasheng Chen Hongyu An Ulugbek S. Kamilov Magnetic Resonance in Medicine AbstractPurposeTo introduce a novel deep model‐based architecture (DMBA), SPICER, that uses pairs of noisy and undersampled k‐space measurements of the same object to jointly train a model for MRI reconstruction and automatic coil sensitivity estimation.MethodsSPICER consists of two modules to simultaneously reconstructs accurate MR images and estimates high‐quality coil sensitivity maps (CSMs). The first module, CSM estimation module, uses a convolutional neural network (CNN) to estimate CSMs from the raw measurements. The second module, DMBA‐based MRI reconstruction module, forms reconstructed images from the input measurements and the estimated CSMs using both the physical measurement model and learned CNN prior. With the benefit of our self‐supervised learning strategy, SPICER can be efficiently trained without any fully sampled reference data.ResultsWe validate SPICER on both open‐access datasets and experimentally collected data, showing that it can achieve state‐of‐the‐art performance in highly accelerated data acquisition settings (up to ). Our results also highlight the importance of different modules of SPICER—including the DMBA, the CSM estimation, and the SPICER training loss—on the final performance of the method. Moreover, SPICER can estimate better CSMs than pre‐estimation methods especially when the ACS data is limited.ConclusionDespite being trained on noisy undersampled data, SPICER can reconstruct high‐quality images and CSMs in highly undersampled settings, which outperforms other self‐supervised learning methods and matches the performance of the well‐known E2E‐VarNet trained on fully sampled ground‐truth data. 10.1002/mrm.30121 http://onlinelibrary.wiley.com/termsAndConditions#vor |
| doi_str_mv | 10.1002/mrm.30121 |
| format | Artículo Open Access |
| id | wiley_oa_10_1002_mrm_30121 |
| institution | Wiley Open Access |
| license_str_mv | http://onlinelibrary.wiley.com/termsAndConditions#vor |
| publishDate | 2024 |
| publisher | Wiley |
| record_format | wiley_oa |
| spellingShingle | SPICER: Self‐supervised learning for MRI with automatic coil sensitivity estimation and reconstruction Yuyang Hu Weijie Gan Chunwei Ying Tongyao Wang Cihat Eldeniz Jiaming Liu Yasheng Chen Hongyu An Ulugbek S. Kamilov Magnetic Resonance in Medicine SPICER: Self‐supervised learning for MRI with automatic coil sensitivity estimation and reconstruction Yuyang Hu Weijie Gan Chunwei Ying Tongyao Wang Cihat Eldeniz Jiaming Liu Yasheng Chen Hongyu An Ulugbek S. Kamilov Magnetic Resonance in Medicine AbstractPurposeTo introduce a novel deep model‐based architecture (DMBA), SPICER, that uses pairs of noisy and undersampled k‐space measurements of the same object to jointly train a model for MRI reconstruction and automatic coil sensitivity estimation.MethodsSPICER consists of two modules to simultaneously reconstructs accurate MR images and estimates high‐quality coil sensitivity maps (CSMs). The first module, CSM estimation module, uses a convolutional neural network (CNN) to estimate CSMs from the raw measurements. The second module, DMBA‐based MRI reconstruction module, forms reconstructed images from the input measurements and the estimated CSMs using both the physical measurement model and learned CNN prior. With the benefit of our self‐supervised learning strategy, SPICER can be efficiently trained without any fully sampled reference data.ResultsWe validate SPICER on both open‐access datasets and experimentally collected data, showing that it can achieve state‐of‐the‐art performance in highly accelerated data acquisition settings (up to ). Our results also highlight the importance of different modules of SPICER—including the DMBA, the CSM estimation, and the SPICER training loss—on the final performance of the method. Moreover, SPICER can estimate better CSMs than pre‐estimation methods especially when the ACS data is limited.ConclusionDespite being trained on noisy undersampled data, SPICER can reconstruct high‐quality images and CSMs in highly undersampled settings, which outperforms other self‐supervised learning methods and matches the performance of the well‐known E2E‐VarNet trained on fully sampled ground‐truth data. 10.1002/mrm.30121 http://onlinelibrary.wiley.com/termsAndConditions#vor |
| title | SPICER: Self‐supervised learning for MRI with automatic coil sensitivity estimation and reconstruction |
| topic | Magnetic Resonance in Medicine |
| url | https://onlinelibrary.wiley.com/doi/10.1002/mrm.30121 |