Super-resolution of diffusion-weighted images using space-customized learning model
BACKGROUND: Diffusion-weighted imaging (DWI) is a noninvasive method used for investigating the microstructural properties of the brain. However, a tradeoff exists between resolution and scanning time in clinical practice. Super-resolution has been employed to enhance spatial resolution in natural i...
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Published in | Technology and health care Vol. 32; no. 1_suppl; pp. 423 - 435 |
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Main Authors | , |
Format | Journal Article |
Language | English |
Published |
London, England
SAGE Publications
01.01.2024
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Subjects | |
Online Access | Get full text |
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Summary: | BACKGROUND:
Diffusion-weighted imaging (DWI) is a noninvasive method used for investigating the microstructural properties of the brain. However, a tradeoff exists between resolution and scanning time in clinical practice. Super-resolution has been employed to enhance spatial resolution in natural images, but its application on high-dimensional and non-Euclidean DWI remains challenging.
OBJECTIVE:
This study aimed to develop an end-to-end deep learning network for enhancing the spatial resolution of DWI through post-processing.
METHODS:
We proposed a space-customized deep learning approach that leveraged convolutional neural networks (CNNs) for the grid structural domain (x-space) and graph CNNs (GCNNs) for the diffusion gradient domain (q-space). Moreover, we represented the output of CNN as a graph using correlations defined by a Gaussian kernel in q-space to bridge the gap between CNN and GCNN feature formats.
RESULTS:
Our model was evaluated on the Human Connectome Project, demonstrating the effective improvement of DWI quality using our proposed method. Extended experiments also highlighted its advantages in downstream tasks.
CONCLUSION:
The hybrid convolutional neural network exhibited distinct advantages in enhancing the spatial resolution of DWI scans for the feature learning of heterogeneous spatial data. |
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ISSN: | 0928-7329 1878-7401 |
DOI: | 10.3233/THC-248037 |