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1 April 2024 Denoising x-ray images with deep learning: impact of spatially correlated noise
Alice Ku, Sen Wang, Adam Wang
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Proceedings Volume 12925, Medical Imaging 2024: Physics of Medical Imaging; 129250V (2024) https://doi.org/10.1117/12.3006556
Event: SPIE Medical Imaging, 2024, San Diego, California, United States
Abstract
Deep learning algorithms have been applied to train denoising models for radiographs. Since obtaining full-dose reference images and low-dose training images through repeated scanning is not preferable or accessible, researchers simulate low-dose images through noise insertion. Traditionally, only white/uncorrelated noise is inserted into training images. However, the noise of flat-panel energy-integrated detectors is spatially correlated. In this work, we investigated the impact of spatial correlation on deep learning-based denoising. We applied spatial noise kernels of different strengths to the noise during noise insertion. Then, we compared the performance of denoising models that are trained with either uncorrelated-noise images or correlated-noise images. When the noise characteristic (uncorrelated or correlated) of test images matches that of the training images, the model can output images that are closest to the high-quality reference image. This implies that spatial correlation should be taken into consideration during image simulation and denoising model training.
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(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Alice Ku, Sen Wang, and Adam Wang "Denoising x-ray images with deep learning: impact of spatially correlated noise", Proc. SPIE 12925, Medical Imaging 2024: Physics of Medical Imaging, 129250V (1 April 2024); https://doi.org/10.1117/12.3006556
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KEYWORDS
Quantum noise
Denoising
X-ray imaging
X-rays
Deep learning
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