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17 October 2022 On use of augmentation for the DNN-based CT image denoising
Prabhat KC, Kyle J. Myers, M. Mehdi Farhangi, Rongping Zeng
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Proceedings Volume 12304, 7th International Conference on Image Formation in X-Ray Computed Tomography; 1230435 (2022) https://doi.org/10.1117/12.2646888
Event: Seventh International Conference on Image Formation in X-Ray Computed Tomography (ICIFXCT 2022), 2022, Baltimore, United States
Abstract
Augmentation strategies have been suggested to overcome issues related to limited training data for the Deep Learning (DL)-based CT image denoising problem. Although augmentation is, indeed, a good machine learning practice, the extent of improvements achieved by DL-based CT denoising solvers through augmentation pipelines remains to be quantified. Accordingly, in this work, we make use of two different deep neural networks (the REDCNN, the DnCNN) to quantify gains in CT image quality through augmentation. The augmentation strategies considered include computer vision inspired strategies (like scaling, rotating, flipping, image-blending) and the CT forward model-based noise (radiation dose) insertion. Likewise, image qualities considered in this work include visual perception- and data fidelity-based global metrics (like the PSNR, the SSIM, the RSE) which are common in the computer vision literature, and CT bench tests (like the NPS, the MTF) and a task-based detectability assessment (the LCD) from the CT imaging literature. Our preliminary results indicate that the DL solvers trained on augmented data show gains in the global metrics, in low-frequency noise texture components and in the MTF values as compared to the ones trained on non-augmented data. However, when the augmented DL-solvers were compared against their low-dose counterparts, their performance - with respect to the noise frequency components, resolution, and detection task - was not all improved, and in some cases even worse
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Prabhat KC, Kyle J. Myers, M. Mehdi Farhangi, and Rongping Zeng "On use of augmentation for the DNN-based CT image denoising", Proc. SPIE 12304, 7th International Conference on Image Formation in X-Ray Computed Tomography, 1230435 (17 October 2022); https://doi.org/10.1117/12.2646888
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KEYWORDS
Computed tomography
LCDs
Data modeling
Acquisition tracking and pointing
Image quality
Modulation transfer functions
Visual process modeling
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