Cross-Modality Image Quality Prediction for Time-Resolved CT from Breathing Signals

Schwarz A, Dickmann J, Hofmann C, Szkitsak J, Bert C, Maier A, Arias Vergara T (2025)

Publication Type: Conference contribution

Publication year: 2025

Journal

Publisher: Springer Science and Business Media Deutschland GmbH

Book Volume: 15401 LNCS

Pages Range: 147-156

Conference Proceedings Title: Lecture Notes in Computer Science

Event location: Marrakesh

ISBN: 9783031845246

DOI: 10.1007/978-3-031-84525-3_13

Abstract

Four-dimensional computed tomography (4DCT) is a time-resolved, multi-modal imaging method that captures respiratory signals synchronised with the CT scan in order to track the movement of the lung. It is routinely used in radiation therapy treatment planning for lung or liver cancer patients. However, image artifacts during 4DCT scans caused by irregular patient breathing negatively impact treatment outcomes. This work proposes a method to automatically detect patients at high risk for severe image artifacts even before the scan is conducted based on an pre-scan analysis of their breathing. This can help to take proactive measures to improve image quality, such as changing the scan mode or providing in-depth patient coaching. A deep neural network is trained to predict the image quality score of 28 lung CT phantom scans, each rated by ten clinical experts. Different pretrained networks are investigated for feature generation and combined with two linear output heads to predict the average expert image quality score of unseen scans. We were able to predict the quality of a 4DCT with a mean absolute error of 8% using only the one-dimensional breathing signal as input. This accuracy is comparable to the rating consistency of our clinical experts, which were rating the images directly.

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How to cite

APA:

Schwarz, A., Dickmann, J., Hofmann, C., Szkitsak, J., Bert, C., Maier, A., & Arias Vergara, T. (2025). Cross-Modality Image Quality Prediction for Time-Resolved CT from Breathing Signals. In Anna Schroder, Xiang Li, Tanveer Syeda-Mahmood, Neil P. Oxtoby, Alexandra Young, Alessa Hering, Tejas S. Mathai, Pritam Mukherjee, Sven Kuckertz, Tiantian He, Isaac Llorente-Saguer, Andreas Maier, Satyananda Kashyap, Hayit Greenspan, Anant Madabhushi (Eds.), Lecture Notes in Computer Science (pp. 147-156). Marrakesh, MA: Springer Science and Business Media Deutschland GmbH.

MLA:

Schwarz, Annette, et al. "Cross-Modality Image Quality Prediction for Time-Resolved CT from Breathing Signals." Proceedings of the Workshop on Longitudinal Disease Tracking and Modeling with Medical Images and Data, LDTM 2024, 5th International Workshop on Multiscale Multimodal Medical Imaging, MMMI 2024, 1st Workshop on Machine Learning for Multimodal/-sensor Healthcare Data, ML4MHD2024 and Workshop on Multimodal Learning and Fusion Across Scales for Clinical Decision Support, ML-CDS 2024 held in conjunction with the 27th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2024, Marrakesh Ed. Anna Schroder, Xiang Li, Tanveer Syeda-Mahmood, Neil P. Oxtoby, Alexandra Young, Alessa Hering, Tejas S. Mathai, Pritam Mukherjee, Sven Kuckertz, Tiantian He, Isaac Llorente-Saguer, Andreas Maier, Satyananda Kashyap, Hayit Greenspan, Anant Madabhushi, Springer Science and Business Media Deutschland GmbH, 2025. 147-156.

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