Deep Learning Denoising of Low-Dose Computed Tomography Using Convolutional Autoencoder: A Phantom Study

Simone Damiani, Simone Damiani, Manuela Imbriani, Manuela Imbriani, Francesca Lizzi, Mariagrazia Quattrocchi, Alessandra Retico, Sara Saponaro, Camilla Scapicchio, Alessandro Tofani, Arman Zafaranchi, Arman Zafaranchi, Arman Zafaranchi, Maria Irene Tenerani, Maria Irene Tenerani, Maria Evelina Fantacci, Maria Evelina Fantacci

2025

Abstract

Low Dose Computed Tomography (LDCT) has proven to be an optimal clinical exploration method for early diagnosis of lung cancer in asymptomatic but high-risk population; however, this methodology suffers from a considerable increase in image noise with respect to Standard Dose Computed Tomography (CT) scans. Several approaches, both conventional and Deep Learning (DL) based, have been developed to mitigate this problem while preserving the visibility of the radiological signs of pathology. This study aims to exploit the possibility of using DL-based methods for the denoising of LDCTs, using a Convolutional Autoencoder and a paired low-dose and high-dose scans (LD/HD) dataset of phantom images. We used twelve acquisitions of the Catphan-500® phantom, each containing 130 slices, acquired with two CT scanners, two dose levels and reconstructed using the Filtered BackProjection algorithm. The proposed architecture, trained with a com-bined loss function, shows promising results for both noise magnitude reduction and Contrast-to-Noise Ratio enhancement when compared with HD reference images. These preliminary results, while encouraging, leave a wide margin for improvement and need to be replicated first on phantoms with more complex structures, secondly on images reconstructed with Iterative Reconstruction algorithms and then translated to LDCTs of real patients.

Paper Citation

in Harvard Style

Damiani S., Imbriani M., Lizzi F., Quattrocchi M., Retico A., Saponaro S., Scapicchio C., Tofani A., Zafaranchi A., Tenerani M. and Fantacci M. (2025). Deep Learning Denoising of Low-Dose Computed Tomography Using Convolutional Autoencoder: A Phantom Study. In Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOIMAGING; ISBN 978-989-758-731-3, SciTePress, pages 376-385. DOI: 10.5220/0013306300003911

in Bibtex Style

@conference{bioimaging25,
author={Simone Damiani and Manuela Imbriani and Francesca Lizzi and Mariagrazia Quattrocchi and Alessandra Retico and Sara Saponaro and Camilla Scapicchio and Alessandro Tofani and Arman Zafaranchi and Maria Tenerani and Maria Fantacci},
title={Deep Learning Denoising of Low-Dose Computed Tomography Using Convolutional Autoencoder: A Phantom Study},
booktitle={Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOIMAGING},
year={2025},
pages={376-385},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013306300003911},
isbn={978-989-758-731-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOIMAGING
TI - Deep Learning Denoising of Low-Dose Computed Tomography Using Convolutional Autoencoder: A Phantom Study
SN - 978-989-758-731-3
AU - Damiani S.
AU - Imbriani M.
AU - Lizzi F.
AU - Quattrocchi M.
AU - Retico A.
AU - Saponaro S.
AU - Scapicchio C.
AU - Tofani A.
AU - Zafaranchi A.
AU - Tenerani M.
AU - Fantacci M.
PY - 2025
SP - 376
EP - 385
DO - 10.5220/0013306300003911
PB - SciTePress