Noise2Noise for denoising photon counting CT images: generating training data from existing scans

Sen Wang; Yirong Yang; Zhye Yin; Adam S. Wang

doi:10.1117/12.2653975

7 April 2023 Noise2Noise for denoising photon counting CT images: generating training data from existing scans

Sen Wang, Yirong Yang, Zhye Yin, Adam S. Wang

Proceedings Volume 12463, Medical Imaging 2023: Physics of Medical Imaging; 1246304 (2023) https://doi.org/10.1117/12.2653975
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Abstract

In recent years, deep learning-based methods for image denoising have drawn increasing attention and shown promising performance in denoising for low-dose CT images. Typically, deep learning utilizes paired data of low-dose images as the input and high-quality images as the target. In clinical imaging, it is difficult to obtain such paired data due to dose concerns. As an effective counterpart, the Noise2Noise framework can train a network based on noisy images and achieve equivalent performance to a conventional training strategy using high-quality images, namely Noise2Clean. In this work, we demonstrate the effectiveness of the Noise2Noise framework for denoising photon counting CT images. The noisy pairs can be generated from existing scans by splitting the raw counts into two scans with independent noise. Photon counting detectors count the number of incident photons, which follow a Poisson distribution. Binomial selection is applied to these measurements to generate noisy pairs at desired dose levels for Noise2Noise training. We show that Noise2Noise-based training approaches the performance of Noise2Clean training through a simulation study. Additionally, increased diversity in dose levels during training improved the performance of Noise2Noise in handling ultra low-dose images.

Conference Presentation

Citation Download Citation

Sen Wang, Yirong Yang, Zhye Yin, and Adam S. Wang "Noise2Noise for denoising photon counting CT images: generating training data from existing scans", Proc. SPIE 12463, Medical Imaging 2023: Physics of Medical Imaging, 1246304 (7 April 2023); https://doi.org/10.1117/12.2653975

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