Personalized synthetic MR imaging with deep learning enhancements
Purpose Personalized synthetic MRI (syn‐MRI) uses MR images of an individual subject acquired at a few design parameters (echo time, repetition time, flip angle) to obtain underlying parametric (ρ,T1,T2)$$ \left(\rho, {\mathrm{T}}_1,{\mathrm{T}}_2\right) $$ maps, from where MR images of that individ...
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| Veröffentlicht in: | Magnetic resonance in medicine Jg. 89; H. 4; S. 1634 - 1643 |
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United States
Wiley Subscription Services, Inc
01.04.2023
John Wiley and Sons Inc |
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| ISSN: | 0740-3194, 1522-2594, 1522-2594 |
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| Abstract | Purpose
Personalized synthetic MRI (syn‐MRI) uses MR images of an individual subject acquired at a few design parameters (echo time, repetition time, flip angle) to obtain underlying parametric (ρ,T1,T2)$$ \left(\rho, {\mathrm{T}}_1,{\mathrm{T}}_2\right) $$ maps, from where MR images of that individual at other design parameter settings are synthesized. However, classical methods that use least‐squares (LS) or maximum likelihood estimators (MLE) are unsatisfactory at higher noise levels because the underlying inverse problem is ill‐posed. This article provides a pipeline to enhance the synthesis of such images in three‐dimensional (3D) using a deep learning (DL) neural network architecture for spatial regularization in a personalized setting where having more than a few training images is impractical.
Methods
Our DL enhancements employ a Deep Image Prior (DIP) with a U‐net type denoising architecture that includes situations with minimal training data, such as personalized syn‐MRI. We provide a general workflow for syn‐MRI from three or more training images. Our workflow, called DIPsyn‐MRI, uses DIP to enhance training images, then obtains parametric images using LS or MLE before synthesizing images at desired design parameter settings. DIPsyn‐MRI is implemented in our publicly available Python package DeepSynMRI available at:
https://github.com/StatPal/DeepSynMRI.
Results
We demonstrate feasibility and improved performance of DIPsyn‐MRI on 3D datasets acquired using the Brainweb interface for spin‐echo and FLASH imaging sequences, at different noise levels. Our DL enhancements improve syn‐MRI in the presence of different intensity nonuniformity levels of the magnetic field, for all but very low noise levels.
Conclusion
This article provides recipes and software to realistically facilitate DL‐enhanced personalized syn‐MRI. |
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for author‐reader discussions PurposePersonalized synthetic MRI (syn‐MRI) uses MR images of an individual subject acquired at a few design parameters (echo time, repetition time, flip angle) to obtain underlying parametric (ρ,T1,T2)$$ \left(\rho, {\mathrm{T}}_1,{\mathrm{T}}_2\right) $$ maps, from where MR images of that individual at other design parameter settings are synthesized. However, classical methods that use least‐squares (LS) or maximum likelihood estimators (MLE) are unsatisfactory at higher noise levels because the underlying inverse problem is ill‐posed. This article provides a pipeline to enhance the synthesis of such images in three‐dimensional (3D) using a deep learning (DL) neural network architecture for spatial regularization in a personalized setting where having more than a few training images is impractical.MethodsOur DL enhancements employ a Deep Image Prior (DIP) with a U‐net type denoising architecture that includes situations with minimal training data, such as personalized syn‐MRI. We provide a general workflow for syn‐MRI from three or more training images. Our workflow, called DIPsyn‐MRI, uses DIP to enhance training images, then obtains parametric images using LS or MLE before synthesizing images at desired design parameter settings. DIPsyn‐MRI is implemented in our publicly available Python package DeepSynMRI available at: https://github.com/StatPal/DeepSynMRI.ResultsWe demonstrate feasibility and improved performance of DIPsyn‐MRI on 3D datasets acquired using the Brainweb interface for spin‐echo and FLASH imaging sequences, at different noise levels. Our DL enhancements improve syn‐MRI in the presence of different intensity nonuniformity levels of the magnetic field, for all but very low noise levels.ConclusionThis article provides recipes and software to realistically facilitate DL‐enhanced personalized syn‐MRI. Personalized synthetic MRI (syn-MRI) uses MR images of an individual subject acquired at a few design parameters (echo time, repetition time, flip angle) to obtain underlying parametric ( ρ , T 1 , T 2 ) $$ \left(\rho, {\mathrm{T}}_1,{\mathrm{T}}_2\right) $$ maps, from where MR images of that individual at other design parameter settings are synthesized. However, classical methods that use least-squares (LS) or maximum likelihood estimators (MLE) are unsatisfactory at higher noise levels because the underlying inverse problem is ill-posed. This article provides a pipeline to enhance the synthesis of such images in three-dimensional (3D) using a deep learning (DL) neural network architecture for spatial regularization in a personalized setting where having more than a few training images is impractical.PURPOSEPersonalized synthetic MRI (syn-MRI) uses MR images of an individual subject acquired at a few design parameters (echo time, repetition time, flip angle) to obtain underlying parametric ( ρ , T 1 , T 2 ) $$ \left(\rho, {\mathrm{T}}_1,{\mathrm{T}}_2\right) $$ maps, from where MR images of that individual at other design parameter settings are synthesized. However, classical methods that use least-squares (LS) or maximum likelihood estimators (MLE) are unsatisfactory at higher noise levels because the underlying inverse problem is ill-posed. This article provides a pipeline to enhance the synthesis of such images in three-dimensional (3D) using a deep learning (DL) neural network architecture for spatial regularization in a personalized setting where having more than a few training images is impractical.Our DL enhancements employ a Deep Image Prior (DIP) with a U-net type denoising architecture that includes situations with minimal training data, such as personalized syn-MRI. We provide a general workflow for syn-MRI from three or more training images. Our workflow, called DIPsyn-MRI, uses DIP to enhance training images, then obtains parametric images using LS or MLE before synthesizing images at desired design parameter settings. DIPsyn-MRI is implemented in our publicly available Python package DeepSynMRI available at: https://github.com/StatPal/DeepSynMRI.METHODSOur DL enhancements employ a Deep Image Prior (DIP) with a U-net type denoising architecture that includes situations with minimal training data, such as personalized syn-MRI. We provide a general workflow for syn-MRI from three or more training images. Our workflow, called DIPsyn-MRI, uses DIP to enhance training images, then obtains parametric images using LS or MLE before synthesizing images at desired design parameter settings. DIPsyn-MRI is implemented in our publicly available Python package DeepSynMRI available at: https://github.com/StatPal/DeepSynMRI.We demonstrate feasibility and improved performance of DIPsyn-MRI on 3D datasets acquired using the Brainweb interface for spin-echo and FLASH imaging sequences, at different noise levels. Our DL enhancements improve syn-MRI in the presence of different intensity nonuniformity levels of the magnetic field, for all but very low noise levels.RESULTSWe demonstrate feasibility and improved performance of DIPsyn-MRI on 3D datasets acquired using the Brainweb interface for spin-echo and FLASH imaging sequences, at different noise levels. Our DL enhancements improve syn-MRI in the presence of different intensity nonuniformity levels of the magnetic field, for all but very low noise levels.This article provides recipes and software to realistically facilitate DL-enhanced personalized syn-MRI.CONCLUSIONThis article provides recipes and software to realistically facilitate DL-enhanced personalized syn-MRI. Personalized synthetic MRI (syn-MRI) uses MR images of an individual subject acquired at a few design parameters (echo time, repetition time, flip angle) to obtain underlying parametric maps, from where MR images of that individual at other design parameter settings are synthesized. However, classical methods that use least-squares (LS) or maximum likelihood estimators (MLE) are unsatisfactory at higher noise levels because the underlying inverse problem is ill-posed. This article provides a pipeline to enhance the synthesis of such images in three-dimensional (3D) using a deep learning (DL) neural network architecture for spatial regularization in a personalized setting where having more than a few training images is impractical. Our DL enhancements employ a Deep Image Prior (DIP) with a U-net type denoising architecture that includes situations with minimal training data, such as personalized syn-MRI. We provide a general workflow for syn-MRI from three or more training images. Our workflow, called DIPsyn-MRI, uses DIP to enhance training images, then obtains parametric images using LS or MLE before synthesizing images at desired design parameter settings. DIPsyn-MRI is implemented in our publicly available Python package DeepSynMRI available at: https://github.com/StatPal/DeepSynMRI. We demonstrate feasibility and improved performance of DIPsyn-MRI on 3D datasets acquired using the Brainweb interface for spin-echo and FLASH imaging sequences, at different noise levels. Our DL enhancements improve syn-MRI in the presence of different intensity nonuniformity levels of the magnetic field, for all but very low noise levels. This article provides recipes and software to realistically facilitate DL-enhanced personalized syn-MRI. Purpose Personalized synthetic MRI (syn‐MRI) uses MR images of an individual subject acquired at a few design parameters (echo time, repetition time, flip angle) to obtain underlying parametric (ρ,T1,T2)$$ \left(\rho, {\mathrm{T}}_1,{\mathrm{T}}_2\right) $$ maps, from where MR images of that individual at other design parameter settings are synthesized. However, classical methods that use least‐squares (LS) or maximum likelihood estimators (MLE) are unsatisfactory at higher noise levels because the underlying inverse problem is ill‐posed. This article provides a pipeline to enhance the synthesis of such images in three‐dimensional (3D) using a deep learning (DL) neural network architecture for spatial regularization in a personalized setting where having more than a few training images is impractical. Methods Our DL enhancements employ a Deep Image Prior (DIP) with a U‐net type denoising architecture that includes situations with minimal training data, such as personalized syn‐MRI. We provide a general workflow for syn‐MRI from three or more training images. Our workflow, called DIPsyn‐MRI, uses DIP to enhance training images, then obtains parametric images using LS or MLE before synthesizing images at desired design parameter settings. DIPsyn‐MRI is implemented in our publicly available Python package DeepSynMRI available at: https://github.com/StatPal/DeepSynMRI. Results We demonstrate feasibility and improved performance of DIPsyn‐MRI on 3D datasets acquired using the Brainweb interface for spin‐echo and FLASH imaging sequences, at different noise levels. Our DL enhancements improve syn‐MRI in the presence of different intensity nonuniformity levels of the magnetic field, for all but very low noise levels. Conclusion This article provides recipes and software to realistically facilitate DL‐enhanced personalized syn‐MRI. |
| Author | Maitra, Ranjan Dutta, Somak Pal, Subrata |
| AuthorAffiliation | 1 Department of Statistics Iowa State University Ames Iowa USA |
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/36420834$$D View this record in MEDLINE/PubMed |
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| CitedBy_id | crossref_primary_10_3390_bioengineering10091078 crossref_primary_10_1080_10618600_2023_2284208 crossref_primary_10_1016_j_apradiso_2025_112049 crossref_primary_10_1088_1361_6560_ad94c7 crossref_primary_10_1002_mp_16884 |
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| Keywords | denoising deep-learning Bloch transform synthetic MRI deep-image-prior |
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Personalized synthetic MRI (syn‐MRI) uses MR images of an individual subject acquired at a few design parameters (echo time, repetition time, flip... Click here for author‐reader discussions Personalized synthetic MRI (syn-MRI) uses MR images of an individual subject acquired at a few design parameters (echo time, repetition time, flip angle) to... PurposePersonalized synthetic MRI (syn‐MRI) uses MR images of an individual subject acquired at a few design parameters (echo time, repetition time, flip... |
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| SubjectTerms | Bloch transform Computer architecture Customization Deep Learning deep‐image‐prior denoising Design parameters Image acquisition Image enhancement Image Processing, Computer-Assisted - methods Inverse problems Low noise Magnetic resonance imaging Magnetic Resonance Imaging - methods Maximum likelihood estimators Neural networks Neural Networks, Computer Noise levels Noise reduction Nonuniformity Regularization Signal-To-Noise Ratio Software Synthesis synthetic MRI Technical Note Technical Note—Computer Processing and Modeling Training Workflow |
| Title | Personalized synthetic MR imaging with deep learning enhancements |
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