Deep Image Priors for Magnetic Resonance Fingerprinting with pretrained Bloch-consistent denoising autoencoders
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866911971077521408 |
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| author | Mayo, Perla Cencini, Matteo Fatania, Ketan Pirkl, Carolin M. Menzel, Marion I. Menze, Bjoern H. Tosetti, Michela Golbabaee, Mohammad |
| author_facet | Mayo, Perla Cencini, Matteo Fatania, Ketan Pirkl, Carolin M. Menzel, Marion I. Menze, Bjoern H. Tosetti, Michela Golbabaee, Mohammad |
| contents | The estimation of multi-parametric quantitative maps from Magnetic Resonance Fingerprinting (MRF) compressed sampled acquisitions, albeit successful, remains a challenge due to the high underspampling rate and artifacts naturally occuring during image reconstruction. Whilst state-of-the-art DL methods can successfully address the task, to fully exploit their capabilities they often require training on a paired dataset, in an area where ground truth is seldom available. In this work, we propose a method that combines a deep image prior (DIP) module that, without ground truth and in conjunction with a Bloch consistency enforcing autoencoder, can tackle the problem, resulting in a method faster and of equivalent or better accuracy than DIP-MRF. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_19866 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Deep Image Priors for Magnetic Resonance Fingerprinting with pretrained Bloch-consistent denoising autoencoders Mayo, Perla Cencini, Matteo Fatania, Ketan Pirkl, Carolin M. Menzel, Marion I. Menze, Bjoern H. Tosetti, Michela Golbabaee, Mohammad Image and Video Processing Machine Learning The estimation of multi-parametric quantitative maps from Magnetic Resonance Fingerprinting (MRF) compressed sampled acquisitions, albeit successful, remains a challenge due to the high underspampling rate and artifacts naturally occuring during image reconstruction. Whilst state-of-the-art DL methods can successfully address the task, to fully exploit their capabilities they often require training on a paired dataset, in an area where ground truth is seldom available. In this work, we propose a method that combines a deep image prior (DIP) module that, without ground truth and in conjunction with a Bloch consistency enforcing autoencoder, can tackle the problem, resulting in a method faster and of equivalent or better accuracy than DIP-MRF. |
| title | Deep Image Priors for Magnetic Resonance Fingerprinting with pretrained Bloch-consistent denoising autoencoders |
| topic | Image and Video Processing Machine Learning |
| url | https://arxiv.org/abs/2407.19866 |