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Magnetic Resonance Parameter Mapping using Self-supervised Deep Learning with Model Reinforcement
This paper proposes a novel self-supervised learning method, RELAX-MORE, for quantitative MRI (qMRI) reconstruction. The proposed method uses an optimization algorithm to unroll a model-based qMRI reconstruction into a deep learning framework, enabling the generation of highly accurate and robust MR...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Cornell University
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10402181/ https://www.ncbi.nlm.nih.gov/pubmed/37547657 |
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author | Bian, Wanyu Jang, Albert Liu, Fang |
author_facet | Bian, Wanyu Jang, Albert Liu, Fang |
author_sort | Bian, Wanyu |
collection | PubMed |
description | This paper proposes a novel self-supervised learning method, RELAX-MORE, for quantitative MRI (qMRI) reconstruction. The proposed method uses an optimization algorithm to unroll a model-based qMRI reconstruction into a deep learning framework, enabling the generation of highly accurate and robust MR parameter maps at imaging acceleration. Unlike conventional deep learning methods requiring a large amount of training data, RELAX-MORE is a subject-specific method that can be trained on single-subject data through self-supervised learning, making it accessible and practically applicable to many qMRI studies. Using the quantitative [Formula: see text] mapping as an example at different brain, knee and phantom experiments, the proposed method demonstrates excellent performance in reconstructing MR parameters, correcting imaging artifacts, removing noises, and recovering image features at imperfect imaging conditions. Compared with other state-of-the-art conventional and deep learning methods, RELAX-MORE significantly improves efficiency, accuracy, robustness, and generalizability for rapid MR parameter mapping. This work demonstrates the feasibility of a new self-supervised learning method for rapid MR parameter mapping, with great potential to enhance the clinical translation of qMRI. |
format | Online Article Text |
id | pubmed-10402181 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Cornell University |
record_format | MEDLINE/PubMed |
spelling | pubmed-104021812023-08-05 Magnetic Resonance Parameter Mapping using Self-supervised Deep Learning with Model Reinforcement Bian, Wanyu Jang, Albert Liu, Fang ArXiv Article This paper proposes a novel self-supervised learning method, RELAX-MORE, for quantitative MRI (qMRI) reconstruction. The proposed method uses an optimization algorithm to unroll a model-based qMRI reconstruction into a deep learning framework, enabling the generation of highly accurate and robust MR parameter maps at imaging acceleration. Unlike conventional deep learning methods requiring a large amount of training data, RELAX-MORE is a subject-specific method that can be trained on single-subject data through self-supervised learning, making it accessible and practically applicable to many qMRI studies. Using the quantitative [Formula: see text] mapping as an example at different brain, knee and phantom experiments, the proposed method demonstrates excellent performance in reconstructing MR parameters, correcting imaging artifacts, removing noises, and recovering image features at imperfect imaging conditions. Compared with other state-of-the-art conventional and deep learning methods, RELAX-MORE significantly improves efficiency, accuracy, robustness, and generalizability for rapid MR parameter mapping. This work demonstrates the feasibility of a new self-supervised learning method for rapid MR parameter mapping, with great potential to enhance the clinical translation of qMRI. Cornell University 2023-07-25 /pmc/articles/PMC10402181/ /pubmed/37547657 Text en https://creativecommons.org/licenses/by/4.0/This work is licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/) , which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use. |
spellingShingle | Article Bian, Wanyu Jang, Albert Liu, Fang Magnetic Resonance Parameter Mapping using Self-supervised Deep Learning with Model Reinforcement |
title | Magnetic Resonance Parameter Mapping using Self-supervised Deep Learning with Model Reinforcement |
title_full | Magnetic Resonance Parameter Mapping using Self-supervised Deep Learning with Model Reinforcement |
title_fullStr | Magnetic Resonance Parameter Mapping using Self-supervised Deep Learning with Model Reinforcement |
title_full_unstemmed | Magnetic Resonance Parameter Mapping using Self-supervised Deep Learning with Model Reinforcement |
title_short | Magnetic Resonance Parameter Mapping using Self-supervised Deep Learning with Model Reinforcement |
title_sort | magnetic resonance parameter mapping using self-supervised deep learning with model reinforcement |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10402181/ https://www.ncbi.nlm.nih.gov/pubmed/37547657 |
work_keys_str_mv | AT bianwanyu magneticresonanceparametermappingusingselfsuperviseddeeplearningwithmodelreinforcement AT jangalbert magneticresonanceparametermappingusingselfsuperviseddeeplearningwithmodelreinforcement AT liufang magneticresonanceparametermappingusingselfsuperviseddeeplearningwithmodelreinforcement |