Multimodal Contrastive Learning for Prospective Personalized Estimation of CT Organ Dose

Abdullah Al Zubaer Imran; Sen Wang; Debashish Pal; Sandeep Dutta; Evan Zucker; Adam Wang

doi:10.1007/978-3-031-16431-6_60

Multimodal Contrastive Learning for Prospective Personalized Estimation of CT Organ Dose

Abdullah Al Zubaer Imran, Sen Wang, Debashish Pal, Sandeep Dutta, Evan Zucker, Adam Wang

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

The increasing frequency of computed tomography (CT) examinations has sparked development of dose reduction techniques to reduce the radiation dose to patients. Optimal dose while maintaining image quality can be achieved through accurate and realistic dose estimates. Unfortunately, existing dosimetric measures are either prohibitively slow or heavily reliant on absorbed dose within a cylindrical phantom, thereby ignoring the impact of patient anatomy and organ radiosensitivity on effective dose. We propose a novel deep learning-based patient-specific CT organ dose estimation method namely, multimodal contrastive learning with Scout images (Scout-MCL). Our proposed Scout-MCL gives accurate and realistic dose estimates in real-time and prospectively, by learning from multi-modal information leveraging image (lateral and frontal scouts) and profile (patient body size). Additionally, the incorporation of an accurately modeled tube current modulation (TCM) enables Scout-MCL to learn realistic dose variations. We evaluate our proposed method on a scout-CT paired scan dataset and show its effectiveness on predicting diverse TCM doses.

Original language	English
Title of host publication	Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 - 25th International Conference, Proceedings
Editors	Linwei Wang, Qi Dou, P. Thomas Fletcher, Stefanie Speidel, Shuo Li
Pages	634-643
Number of pages	10
DOIs	https://doi.org/10.1007/978-3-031-16431-6_60
State	Published - 2022
Event	25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022 - Singapore, Singapore Duration: Sep 18 2022 → Sep 22 2022

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	13431 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022
Country/Territory	Singapore
City	Singapore
Period	9/18/22 → 9/22/22

Bibliographical note

Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Keywords

Computed tomography
Contrastive learning
Dosimetry
Scout
Tube current modulation

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-031-16431-6_60

Cite this

Imran, A. A. Z., Wang, S., Pal, D., Dutta, S., Zucker, E., & Wang, A. (2022). Multimodal Contrastive Learning for Prospective Personalized Estimation of CT Organ Dose. In L. Wang, Q. Dou, P. T. Fletcher, S. Speidel, & S. Li (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 - 25th International Conference, Proceedings (pp. 634-643). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13431 LNCS). https://doi.org/10.1007/978-3-031-16431-6_60

Imran, Abdullah Al Zubaer ; Wang, Sen ; Pal, Debashish et al. / Multimodal Contrastive Learning for Prospective Personalized Estimation of CT Organ Dose. Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 - 25th International Conference, Proceedings. editor / Linwei Wang ; Qi Dou ; P. Thomas Fletcher ; Stefanie Speidel ; Shuo Li. 2022. pp. 634-643 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "The increasing frequency of computed tomography (CT) examinations has sparked development of dose reduction techniques to reduce the radiation dose to patients. Optimal dose while maintaining image quality can be achieved through accurate and realistic dose estimates. Unfortunately, existing dosimetric measures are either prohibitively slow or heavily reliant on absorbed dose within a cylindrical phantom, thereby ignoring the impact of patient anatomy and organ radiosensitivity on effective dose. We propose a novel deep learning-based patient-specific CT organ dose estimation method namely, multimodal contrastive learning with Scout images (Scout-MCL). Our proposed Scout-MCL gives accurate and realistic dose estimates in real-time and prospectively, by learning from multi-modal information leveraging image (lateral and frontal scouts) and profile (patient body size). Additionally, the incorporation of an accurately modeled tube current modulation (TCM) enables Scout-MCL to learn realistic dose variations. We evaluate our proposed method on a scout-CT paired scan dataset and show its effectiveness on predicting diverse TCM doses.",

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author = "Imran, {Abdullah Al Zubaer} and Sen Wang and Debashish Pal and Sandeep Dutta and Evan Zucker and Adam Wang",

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Imran, AAZ, Wang, S, Pal, D, Dutta, S, Zucker, E & Wang, A 2022, Multimodal Contrastive Learning for Prospective Personalized Estimation of CT Organ Dose. in L Wang, Q Dou, PT Fletcher, S Speidel & S Li (eds), Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 - 25th International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13431 LNCS, pp. 634-643, 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022, Singapore, Singapore, 9/18/22. https://doi.org/10.1007/978-3-031-16431-6_60

Multimodal Contrastive Learning for Prospective Personalized Estimation of CT Organ Dose. / Imran, Abdullah Al Zubaer; Wang, Sen; Pal, Debashish et al.
Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 - 25th International Conference, Proceedings. ed. / Linwei Wang; Qi Dou; P. Thomas Fletcher; Stefanie Speidel; Shuo Li. 2022. p. 634-643 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13431 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Imran AAZ, Wang S, Pal D, Dutta S, Zucker E, Wang A. Multimodal Contrastive Learning for Prospective Personalized Estimation of CT Organ Dose. In Wang L, Dou Q, Fletcher PT, Speidel S, Li S, editors, Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 - 25th International Conference, Proceedings. 2022. p. 634-643. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-16431-6_60