Diffusion X-ray image denoising

Daniel Sanderson, Pablo M. Olmos, Carlos Fernández Del Cerro, Manuel Desco, Mónica Abella
Proceedings of The 7nd International Conference on Medical Imaging with Deep Learning, PMLR 250:1328-1340, 2024.

Abstract

X-ray imaging is a cornerstone in medical diagnosis, constituting a significant portion of the radiation dose encountered by patients. Excessive radiation poses health risks, particularly for pediatric patients, but despite the imperative to reduce radiation doses, conventional image processing methods for X-ray denoising often struggle with heuristic parameter calibration and prolonged execution times. Deep Learning solutions have emerged as promising alternatives, but their effectiveness varies, and challenges persist in preserving image quality.This paper presents an exploration of diffusion models for planar X-ray image denoising, a novel approach that to our knowledge has not been yet investigated in this domain. We perform real time denoising of Poisson noise while preserving image resolution and structural similarity. The results indicate that diffusion models show promise for planar X-ray image denoising, offering a potential improvement in the optimization of diagnostic utility amid dose reduction efforts.

Cite this Paper

BibTeX
@InProceedings{pmlr-v250-sanderson24a, title = {Diffusion X-ray image denoising}, author = {Sanderson, Daniel and Olmos, Pablo M. and Cerro, Carlos Fern\'andez Del and Desco, Manuel and Abella, M\'onica}, booktitle = {Proceedings of The 7nd International Conference on Medical Imaging with Deep Learning}, pages = {1328--1340}, year = {2024}, editor = {Burgos, Ninon and Petitjean, Caroline and Vakalopoulou, Maria and Christodoulidis, Stergios and Coupe, Pierrick and Delingette, Hervé and Lartizien, Carole and Mateus, Diana}, volume = {250}, series = {Proceedings of Machine Learning Research}, month = {03--05 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v250/main/assets/sanderson24a/sanderson24a.pdf}, url = {https://proceedings.mlr.press/v250/sanderson24a.html}, abstract = {X-ray imaging is a cornerstone in medical diagnosis, constituting a significant portion of the radiation dose encountered by patients. Excessive radiation poses health risks, particularly for pediatric patients, but despite the imperative to reduce radiation doses, conventional image processing methods for X-ray denoising often struggle with heuristic parameter calibration and prolonged execution times. Deep Learning solutions have emerged as promising alternatives, but their effectiveness varies, and challenges persist in preserving image quality.This paper presents an exploration of diffusion models for planar X-ray image denoising, a novel approach that to our knowledge has not been yet investigated in this domain. We perform real time denoising of Poisson noise while preserving image resolution and structural similarity. The results indicate that diffusion models show promise for planar X-ray image denoising, offering a potential improvement in the optimization of diagnostic utility amid dose reduction efforts.} }
Endnote
%0 Conference Paper %T Diffusion X-ray image denoising %A Daniel Sanderson %A Pablo M. Olmos %A Carlos Fernández Del Cerro %A Manuel Desco %A Mónica Abella %B Proceedings of The 7nd International Conference on Medical Imaging with Deep Learning %C Proceedings of Machine Learning Research %D 2024 %E Ninon Burgos %E Caroline Petitjean %E Maria Vakalopoulou %E Stergios Christodoulidis %E Pierrick Coupe %E Hervé Delingette %E Carole Lartizien %E Diana Mateus %F pmlr-v250-sanderson24a %I PMLR %P 1328--1340 %U https://proceedings.mlr.press/v250/sanderson24a.html %V 250 %X X-ray imaging is a cornerstone in medical diagnosis, constituting a significant portion of the radiation dose encountered by patients. Excessive radiation poses health risks, particularly for pediatric patients, but despite the imperative to reduce radiation doses, conventional image processing methods for X-ray denoising often struggle with heuristic parameter calibration and prolonged execution times. Deep Learning solutions have emerged as promising alternatives, but their effectiveness varies, and challenges persist in preserving image quality.This paper presents an exploration of diffusion models for planar X-ray image denoising, a novel approach that to our knowledge has not been yet investigated in this domain. We perform real time denoising of Poisson noise while preserving image resolution and structural similarity. The results indicate that diffusion models show promise for planar X-ray image denoising, offering a potential improvement in the optimization of diagnostic utility amid dose reduction efforts.
APA
Sanderson, D., Olmos, P.M., Cerro, C.F.D., Desco, M. & Abella, M.. (2024). Diffusion X-ray image denoising. Proceedings of The 7nd International Conference on Medical Imaging with Deep Learning, in Proceedings of Machine Learning Research 250:1328-1340 Available from https://proceedings.mlr.press/v250/sanderson24a.html.

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