Denoising diffusion weighted imaging data using convolutional neural networks.
Diffusion weighted imaging (DWI) with multiple, high b-values is critical for extracting tissue microstructure measurements; however, high b-value DWI images contain high noise levels that can overwhelm the signal of interest and bias microstructural measurements. Here, we propose a simple denoising...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2022-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0274396 |
| _version_ | 1811208422415138816 |
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| author | Hu Cheng Sophia Vinci-Booher Jian Wang Bradley Caron Qiuting Wen Sharlene Newman Franco Pestilli |
| author_facet | Hu Cheng Sophia Vinci-Booher Jian Wang Bradley Caron Qiuting Wen Sharlene Newman Franco Pestilli |
| author_sort | Hu Cheng |
| collection | DOAJ |
| description | Diffusion weighted imaging (DWI) with multiple, high b-values is critical for extracting tissue microstructure measurements; however, high b-value DWI images contain high noise levels that can overwhelm the signal of interest and bias microstructural measurements. Here, we propose a simple denoising method that can be applied to any dataset, provided a low-noise, single-subject dataset is acquired using the same DWI sequence. The denoising method uses a one-dimensional convolutional neural network (1D-CNN) and deep learning to learn from a low-noise dataset, voxel-by-voxel. The trained model can then be applied to high-noise datasets from other subjects. We validated the 1D-CNN denoising method by first demonstrating that 1D-CNN denoising resulted in DWI images that were more similar to the noise-free ground truth than comparable denoising methods, e.g., MP-PCA, using simulated DWI data. Using the same DWI acquisition but reconstructed with two common reconstruction methods, i.e. SENSE1 and sum-of-square, to generate a pair of low-noise and high-noise datasets, we then demonstrated that 1D-CNN denoising of high-noise DWI data collected from human subjects showed promising results in three domains: DWI images, diffusion metrics, and tractography. In particular, the denoised images were very similar to a low-noise reference image of that subject, more than the similarity between repeated low-noise images (i.e. computational reproducibility). Finally, we demonstrated the use of the 1D-CNN method in two practical examples to reduce noise from parallel imaging and simultaneous multi-slice acquisition. We conclude that the 1D-CNN denoising method is a simple, effective denoising method for DWI images that overcomes some of the limitations of current state-of-the-art denoising methods, such as the need for a large number of training subjects and the need to account for the rectified noise floor. |
| first_indexed | 2024-04-12T04:20:57Z |
| format | Article |
| id | doaj.art-3f0021448461476595670f16a477962b |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-04-12T04:20:57Z |
| publishDate | 2022-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-3f0021448461476595670f16a477962b2022-12-22T03:48:13ZengPublic Library of Science (PLoS)PLoS ONE1932-62032022-01-01179e027439610.1371/journal.pone.0274396Denoising diffusion weighted imaging data using convolutional neural networks.Hu ChengSophia Vinci-BooherJian WangBradley CaronQiuting WenSharlene NewmanFranco PestilliDiffusion weighted imaging (DWI) with multiple, high b-values is critical for extracting tissue microstructure measurements; however, high b-value DWI images contain high noise levels that can overwhelm the signal of interest and bias microstructural measurements. Here, we propose a simple denoising method that can be applied to any dataset, provided a low-noise, single-subject dataset is acquired using the same DWI sequence. The denoising method uses a one-dimensional convolutional neural network (1D-CNN) and deep learning to learn from a low-noise dataset, voxel-by-voxel. The trained model can then be applied to high-noise datasets from other subjects. We validated the 1D-CNN denoising method by first demonstrating that 1D-CNN denoising resulted in DWI images that were more similar to the noise-free ground truth than comparable denoising methods, e.g., MP-PCA, using simulated DWI data. Using the same DWI acquisition but reconstructed with two common reconstruction methods, i.e. SENSE1 and sum-of-square, to generate a pair of low-noise and high-noise datasets, we then demonstrated that 1D-CNN denoising of high-noise DWI data collected from human subjects showed promising results in three domains: DWI images, diffusion metrics, and tractography. In particular, the denoised images were very similar to a low-noise reference image of that subject, more than the similarity between repeated low-noise images (i.e. computational reproducibility). Finally, we demonstrated the use of the 1D-CNN method in two practical examples to reduce noise from parallel imaging and simultaneous multi-slice acquisition. We conclude that the 1D-CNN denoising method is a simple, effective denoising method for DWI images that overcomes some of the limitations of current state-of-the-art denoising methods, such as the need for a large number of training subjects and the need to account for the rectified noise floor.https://doi.org/10.1371/journal.pone.0274396 |
| spellingShingle | Hu Cheng Sophia Vinci-Booher Jian Wang Bradley Caron Qiuting Wen Sharlene Newman Franco Pestilli Denoising diffusion weighted imaging data using convolutional neural networks. PLoS ONE |
| title | Denoising diffusion weighted imaging data using convolutional neural networks. |
| title_full | Denoising diffusion weighted imaging data using convolutional neural networks. |
| title_fullStr | Denoising diffusion weighted imaging data using convolutional neural networks. |
| title_full_unstemmed | Denoising diffusion weighted imaging data using convolutional neural networks. |
| title_short | Denoising diffusion weighted imaging data using convolutional neural networks. |
| title_sort | denoising diffusion weighted imaging data using convolutional neural networks |
| url | https://doi.org/10.1371/journal.pone.0274396 |
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