Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Robustness of Single and Dual-Energy Deep Learning-Based Scatter Correction Models on Simulated and Real Chest X-rays

Clara Freijo
* ORCID logo ,
Joaquin L. Herraiz
* ,
Fernando Arias-Valcayo
ORCID logo ,
Paula Ibáñez
,
Gabriela Moreno
,
Amaia Villa-Abaunza
,
Jose M. Udias
Version 1 : Received: 15 November 2023 / Approved: 16 November 2023 / Online: 16 November 2023 (07:43:59 CET)

A peer-reviewed article of this Preprint also exists.

Freijo, C.; Herraiz, J.L.; Arias-Valcayo, F.; Ibáñez, P.; Moreno, G.; Villa-Abaunza, A.; Udías, J.M. Robustness of Single- and Dual-Energy Deep-Learning-Based Scatter Correction Models on Simulated and Real Chest X-rays. Algorithms 2023, 16, 565. Freijo, C.; Herraiz, J.L.; Arias-Valcayo, F.; Ibáñez, P.; Moreno, G.; Villa-Abaunza, A.; Udías, J.M. Robustness of Single- and Dual-Energy Deep-Learning-Based Scatter Correction Models on Simulated and Real Chest X-rays. Algorithms 2023, 16, 565.

Abstract

Chest X-ray (CXR) is the first tool globally employed to detect cardiopulmonary pathologies. These acquisitions are highly affected by scattered photons due to the large field-of-view required. Scatter in CXRs introduces background in the images, which reduces their contrast. We developed three deep learning-based models to estimate and correct the scatter contribution to CXRs. We used a Monte Carlo (MC) ray-tracing model to simulate CXRs from human models obtained from CT scans using different configurations (depending on the availability of dual-energy acquisitions). The simulated CXRs contained the separated contribution of direct and scattered X-rays in the detector. These simulated datasets were then used as the reference for the supervised training of several NN. Three NN models (single and dual energy) were trained with the MultiResUNet architecture. The performance of the NN models was evaluated on CXRs obtained with the MC code from chest CTs of patients affected by COVID-19. The results showed that the NN models were able to estimate and correct the scatter contribution to CXRs with an error <5%, being robust to variations in the simulation setup and improving the contrast in soft tissue. The single-energy model was tested with real CXRs, providing robust estimations of the scatter-corrected CXRs.

Keywords

x-ray image; scatter correction; deep learning; dual energy; monte carlo simulations

Subject

Computer Science and Mathematics, Artificial Intelligence and Machine Learning

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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