SPICER: Self-Supervised Learning for MRI with Automatic Coil Sensitivity Estimation and Reconstruction
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arXiv
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| Format: | Preprint |
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2022
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| _version_ | 1866914825774301184 |
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| author | Hu, Yuyang Gan, Weijie Ying, Chunwei Wang, Tongyao Eldeniz, Cihat Liu, Jiaming Chen, Yasheng An, Hongyu Kamilov, Ulugbek S. |
| author_facet | Hu, Yuyang Gan, Weijie Ying, Chunwei Wang, Tongyao Eldeniz, Cihat Liu, Jiaming Chen, Yasheng An, Hongyu Kamilov, Ulugbek S. |
| contents | Deep model-based architectures (DMBAs) integrating physical measurement models and learned image regularizers are widely used in parallel magnetic resonance imaging (PMRI). Traditional DMBAs for PMRI rely on pre-estimated coil sensitivity maps (CSMs) as a component of the measurement model. However, estimation of accurate CSMs is a challenging problem when measurements are highly undersampled. Additionally, traditional training of DMBAs requires high-quality groundtruth images, limiting their use in applications where groundtruth is difficult to obtain. This paper addresses these issues by presenting SPICE as a new method that integrates self-supervised learning and automatic coil sensitivity estimation. Instead of using pre-estimated CSMs, SPICE simultaneously reconstructs accurate MR images and estimates high-quality CSMs. SPICE also enables learning from undersampled noisy measurements without any groundtruth. We validate SPICE on experimentally collected data, showing that it can achieve state-of-the-art performance in highly accelerated data acquisition settings (up to 10x). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2210_02584 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | SPICER: Self-Supervised Learning for MRI with Automatic Coil Sensitivity Estimation and Reconstruction Hu, Yuyang Gan, Weijie Ying, Chunwei Wang, Tongyao Eldeniz, Cihat Liu, Jiaming Chen, Yasheng An, Hongyu Kamilov, Ulugbek S. Image and Video Processing Deep model-based architectures (DMBAs) integrating physical measurement models and learned image regularizers are widely used in parallel magnetic resonance imaging (PMRI). Traditional DMBAs for PMRI rely on pre-estimated coil sensitivity maps (CSMs) as a component of the measurement model. However, estimation of accurate CSMs is a challenging problem when measurements are highly undersampled. Additionally, traditional training of DMBAs requires high-quality groundtruth images, limiting their use in applications where groundtruth is difficult to obtain. This paper addresses these issues by presenting SPICE as a new method that integrates self-supervised learning and automatic coil sensitivity estimation. Instead of using pre-estimated CSMs, SPICE simultaneously reconstructs accurate MR images and estimates high-quality CSMs. SPICE also enables learning from undersampled noisy measurements without any groundtruth. We validate SPICE on experimentally collected data, showing that it can achieve state-of-the-art performance in highly accelerated data acquisition settings (up to 10x). |
| title | SPICER: Self-Supervised Learning for MRI with Automatic Coil Sensitivity Estimation and Reconstruction |
| topic | Image and Video Processing |
| url | https://arxiv.org/abs/2210.02584 |