Self-supervised denoising of Nyquist-sampled volumetric images via deep learning

Matthew B. Applegate; Kivanc Kose; Sandesh Ghimire; Milind Rajadhyaksha; Jennifer Dy

doi:10.1117/1.JMI.10.2.024005

27 March 2023 Self-supervised denoising of Nyquist-sampled volumetric images via deep learning

Matthew B. Applegate, Kivanc Kose, Sandesh Ghimire, Milind Rajadhyaksha, Jennifer Dy

Author Affiliations +

Journal of Medical Imaging, Vol. 10, Issue 2, 024005 (March 2023). https://doi.org/10.1117/1.JMI.10.2.024005

Abstract

Purpose

Deep learning has demonstrated excellent performance enhancing noisy or degraded biomedical images. However, many of these models require access to a noise-free version of the images to provide supervision during training, which limits their utility. Here, we develop an algorithm (noise2Nyquist) that leverages the fact that Nyquist sampling provides guarantees about the maximum difference between adjacent slices in a volumetric image, which allows denoising to be performed without access to clean images. We aim to show that our method is more broadly applicable and more effective than other self-supervised denoising algorithms on real biomedical images, and provides comparable performance to algorithms that need clean images during training.

Approach

We first provide a theoretical analysis of noise2Nyquist and an upper bound for denoising error based on sampling rate. We go on to demonstrate its effectiveness in denoising in a simulated example as well as real fluorescence confocal microscopy, computed tomography, and optical coherence tomography images.

Results

We find that our method has better denoising performance than existing self-supervised methods and is applicable to datasets where clean versions are not available. Our method resulted in peak signal to noise ratio (PSNR) within 1 dB and structural similarity (SSIM) index within 0.02 of supervised methods. On medical images, it outperforms existing self-supervised methods by an average of 3 dB in PSNR and 0.1 in SSIM.

Conclusion

noise2Nyquist can be used to denoise any volumetric dataset sampled at at least the Nyquist rate making it useful for a wide variety of existing datasets.

Citation Download Citation

Matthew B. Applegate, Kivanc Kose, Sandesh Ghimire, Milind Rajadhyaksha, and Jennifer Dy "Self-supervised denoising of Nyquist-sampled volumetric images via deep learning," Journal of Medical Imaging 10(2), 024005 (27 March 2023). https://doi.org/10.1117/1.JMI.10.2.024005

Received: 27 November 2022; Accepted: 6 March 2023; Published: 27 March 2023

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KEYWORDS

Denoising

Education and training

Sampling rates

Deep learning

Error analysis

Point spread functions

Image restoration

Purpose

Approach

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