Adapting model‐based deep learning to multiple acquisition conditions: Ada‐MoDL.
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| Title: | Adapting model‐based deep learning to multiple acquisition conditions: Ada‐MoDL. |
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| Authors: | Pramanik, Aniket1 (AUTHOR) aniket-pramanik@uiowa.edu, Bhave, Sampada2 (AUTHOR), Sajib, Saurav2 (AUTHOR), Sharma, Samir D.2 (AUTHOR), Jacob, Mathews1 (AUTHOR) |
| Source: | Magnetic Resonance in Medicine. Nov2023, Vol. 90 Issue 5, p2033-2051. 19p. |
| Subjects: | Deep learning, Convolutional neural networks, Regularization parameter |
| Abstract: | Purpose: The aim of this work is to introduce a single model‐based deep network that can provide high‐quality reconstructions from undersampled parallel MRI data acquired with multiple sequences, acquisition settings, and field strengths. Methods: A single unrolled architecture, which offers good reconstructions for multiple acquisition settings, is introduced. The proposed scheme adapts the model to each setting by scaling the convolutional neural network (CNN) features and the regularization parameter with appropriate weights. The scaling weights and regularization parameter are derived using a multilayer perceptron model from conditional vectors, which represents the specific acquisition setting. The perceptron parameters and the CNN weights are jointly trained using data from multiple acquisition settings, including differences in field strengths, acceleration, and contrasts. The conditional network is validated using datasets acquired with different acquisition settings. Results: The comparison of the adaptive framework, which trains a single model using the data from all the settings, shows that it can offer consistently improved performance for each acquisition condition. The comparison of the proposed scheme with networks that are trained independently for each acquisition setting shows that it requires less training data per acquisition setting to offer good performance. Conclusion: The Ada‐MoDL framework enables the use of a single model‐based unrolled network for multiple acquisition settings. In addition to eliminating the need to train and store multiple networks for different acquisition settings, this approach reduces the training data needed for each acquisition setting. [ABSTRACT FROM AUTHOR] |
| Copyright of Magnetic Resonance in Medicine is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 171106045 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Adapting model‐based deep learning to multiple acquisition conditions: Ada‐MoDL. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Pramanik%2C+Aniket%22">Pramanik, Aniket</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> aniket-pramanik@uiowa.edu</i><br /><searchLink fieldCode="AR" term="%22Bhave%2C+Sampada%22">Bhave, Sampada</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Sajib%2C+Saurav%22">Sajib, Saurav</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Sharma%2C+Samir+D%2E%22">Sharma, Samir D.</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Jacob%2C+Mathews%22">Jacob, Mathews</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Magnetic+Resonance+in+Medicine%22">Magnetic Resonance in Medicine</searchLink>. Nov2023, Vol. 90 Issue 5, p2033-2051. 19p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Convolutional+neural+networks%22">Convolutional neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Regularization+parameter%22">Regularization parameter</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Purpose: The aim of this work is to introduce a single model‐based deep network that can provide high‐quality reconstructions from undersampled parallel MRI data acquired with multiple sequences, acquisition settings, and field strengths. Methods: A single unrolled architecture, which offers good reconstructions for multiple acquisition settings, is introduced. The proposed scheme adapts the model to each setting by scaling the convolutional neural network (CNN) features and the regularization parameter with appropriate weights. The scaling weights and regularization parameter are derived using a multilayer perceptron model from conditional vectors, which represents the specific acquisition setting. The perceptron parameters and the CNN weights are jointly trained using data from multiple acquisition settings, including differences in field strengths, acceleration, and contrasts. The conditional network is validated using datasets acquired with different acquisition settings. Results: The comparison of the adaptive framework, which trains a single model using the data from all the settings, shows that it can offer consistently improved performance for each acquisition condition. The comparison of the proposed scheme with networks that are trained independently for each acquisition setting shows that it requires less training data per acquisition setting to offer good performance. Conclusion: The Ada‐MoDL framework enables the use of a single model‐based unrolled network for multiple acquisition settings. In addition to eliminating the need to train and store multiple networks for different acquisition settings, this approach reduces the training data needed for each acquisition setting. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Magnetic Resonance in Medicine is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1002/mrm.29750 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 19 StartPage: 2033 Subjects: – SubjectFull: Deep learning Type: general – SubjectFull: Convolutional neural networks Type: general – SubjectFull: Regularization parameter Type: general Titles: – TitleFull: Adapting model‐based deep learning to multiple acquisition conditions: Ada‐MoDL. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Pramanik, Aniket – PersonEntity: Name: NameFull: Bhave, Sampada – PersonEntity: Name: NameFull: Sajib, Saurav – PersonEntity: Name: NameFull: Sharma, Samir D. – PersonEntity: Name: NameFull: Jacob, Mathews IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 11 Text: Nov2023 Type: published Y: 2023 Identifiers: – Type: issn-print Value: 07403194 Numbering: – Type: volume Value: 90 – Type: issue Value: 5 Titles: – TitleFull: Magnetic Resonance in Medicine Type: main |
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