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Jointly estimating parametric maps of multiple diffusion models from undersampled q‐space data: A comparison of three deep learning approaches.

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Title: Jointly estimating parametric maps of multiple diffusion models from undersampled q‐space data: A comparison of three deep learning approaches.
Authors: HashemizadehKolowri, SeyyedKazem1 (AUTHOR) s.hashemizadehkolowri@utah.edu, Chen, Rong‐Rong2 (AUTHOR), Adluru, Ganesh1,3 (AUTHOR), DiBella, Edward V. R.1,2,3 (AUTHOR)
Source: Magnetic Resonance in Medicine. Jun2022, Vol. 87 Issue 6, p2957-2971. 15p.
Subjects: Deep learning, Signal convolution, Diffusion tensor imaging, Convolutional neural networks, Maps
Abstract: Purpose: While advanced diffusion techniques have been found valuable in many studies, their clinical availability has been hampered partly due to their long scan times. Moreover, each diffusion technique can only extract a few relevant microstructural features. Using multiple diffusion methods may help to better understand the brain microstructure, which requires multiple expensive model fittings. In this work, we compare deep learning (DL) approaches to jointly estimate parametric maps of multiple diffusion representations/models from highly undersampled q‐space data. Methods: We implement three DL approaches to jointly estimate parametric maps of diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), neurite orientation dispersion and density imaging (NODDI), and multi‐compartment spherical mean technique (SMT). A per‐voxel q‐space deep learning (1D‐qDL), a per‐slice convolutional neural network (2D‐CNN), and a 3D‐patch‐based microstructure estimation with sparse coding using a separable dictionary (MESC‐SD) network are considered. Results: The accuracy of estimated diffusion maps depends on the q‐space undersampling, the selected network architecture, and the region and the parameter of interest. The smallest errors are observed for the MESC‐SD network architecture (less than 10% normalized RMSE in most brain regions). Conclusion: Our experiments show that DL methods are very efficient tools to simultaneously estimate several diffusion maps from undersampled q‐space data. These methods can significantly reduce both the scan (∼6‐fold) and processing times (∼25‐fold) for estimating advanced parametric diffusion maps while achieving a reasonable accuracy. [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.)
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  Data: Purpose: While advanced diffusion techniques have been found valuable in many studies, their clinical availability has been hampered partly due to their long scan times. Moreover, each diffusion technique can only extract a few relevant microstructural features. Using multiple diffusion methods may help to better understand the brain microstructure, which requires multiple expensive model fittings. In this work, we compare deep learning (DL) approaches to jointly estimate parametric maps of multiple diffusion representations/models from highly undersampled q‐space data. Methods: We implement three DL approaches to jointly estimate parametric maps of diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), neurite orientation dispersion and density imaging (NODDI), and multi‐compartment spherical mean technique (SMT). A per‐voxel q‐space deep learning (1D‐qDL), a per‐slice convolutional neural network (2D‐CNN), and a 3D‐patch‐based microstructure estimation with sparse coding using a separable dictionary (MESC‐SD) network are considered. Results: The accuracy of estimated diffusion maps depends on the q‐space undersampling, the selected network architecture, and the region and the parameter of interest. The smallest errors are observed for the MESC‐SD network architecture (less than 10% normalized RMSE in most brain regions). Conclusion: Our experiments show that DL methods are very efficient tools to simultaneously estimate several diffusion maps from undersampled q‐space data. These methods can significantly reduce both the scan (∼6‐fold) and processing times (∼25‐fold) for estimating advanced parametric diffusion maps while achieving a reasonable accuracy. [ABSTRACT FROM AUTHOR]
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  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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      – SubjectFull: Signal convolution
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      – SubjectFull: Diffusion tensor imaging
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              Text: Jun2022
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