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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Azizi, S., et al.: Big self-supervised models advance medical image classification. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 3478–3488 (2021)
Badal, A., Badano, A.: Accelerating Monte Carlo simulations of photon transport in a voxelized geometry using a massively parallel graphics processing unit. Med. Phys. 36(11), 4878–4880 (2009)
Chaitanya, K., Erdil, E., Karani, N., Konukoglu, E.: Contrastive learning of global and local features for medical image segmentation with limited annotations. Adv. Neural Inf. Process. Syst. 33, 12546–12558 (2020)
Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607. PMLR (2020)
Dutta, S., Das, B., Kaushik, S.: Assessment of optimal deep learning configuration for vertebrae segmentation from CT images. In: Medical Imaging 2019: Imaging Informatics for Healthcare, Research, and Applications, vol. 10954, pp. 298–305. SPIE (2019)
Fan, J., Xing, L., Dong, P., Wang, J., Hu, W., Yang, Y.: Data-driven dose calculation algorithm based on deep U-Net. Phys. Med. Biol. 65(24), 245035 (2020)
Furhang, E.E., Chui, C.S., Sgouros, G.: A Monte Carlo approach to patient-specific dosimetry. Med. Phys. 23(9), 1523–1529 (1996)
Götz, T.I., Schmidkonz, C., Chen, S., Al-Baddai, S., Kuwert, T., Lang, E.: A deep learning approach to radiation dose estimation. Phys. Med. Biol. 65(3), 035007 (2020)
Guerreiro, F.: Deep learning prediction of proton and photon dose distributions for paediatric abdominal tumours. Radiotherapy Oncol. 156, 36–42 (2021)
Hadsell, R., Chopra, S., LeCun, Y.: Dimensionality reduction by learning an invariant mapping. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2006), vol. 2, pp. 1735–1742. IEEE (2006)
Hardy, A.J., Bostani, M., Kim, G.H.J., Cagnon, C.H., Zankl, M., McNitt-Gray, M.: Evaluating size-specific dose estimate (SSDE) as an estimate of organ doses from routine CT exams derived from monte carlo simulations. Med. Phys. 48, 6160–6173 (2021)
Imran, A.-A.-Z., Wang, S., Pal, D., Dutta, S., Patel, B., Zucker, E., Wang, A.: Personalized CT organ dose estimation from scout images. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12904, pp. 488–498. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87202-1_47
Jadick, G., Abadi, E., Harrawood, B., Sharma, S., Segars, W.P., Samei, E.: A scanner-specific framework for simulating CT images with tube current modulation. Phys. Med. Biol. 66(18), 185010 (2021)
Kinakh, V., Taran, O., Voloshynovskiy, S.: Scatsimclr: self-supervised contrastive learning with pretext task regularization for small-scale datasets. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 1098–1106 (2021)
Lell, M.M., Kachelrieß, M.: Recent and upcoming technological developments in computed tomography: high speed, low dose, deep learning, multienergy. Invest. Radiol. 55(1), 8–19 (2020)
Leng, S., Shiung, M., Duan, X., Yu, L., Zhang, Y., McCollough, C.H.: Size-specific dose estimates for chest, abdominal, and pelvic CT: effect of intrapatient variability in water-equivalent diameter. Radiology 276(1), 184–190 (2015)
Li, X., Segars, W.P., Samei, E.: The impact on CT dose of the variability in tube current modulation technology: a theoretical investigation. Phys. Med. Biol. 59(16), 4525 (2014)
Liu, X., et al.: Self-supervised learning: generative or contrastive. IEEE Trans. Knowl. Data Eng. (2021)
Maier, J., Eulig, E., Dorn, S., Sawall, S., Kachelrieß, M.: Real-time patient-specific CT dose estimation using a deep convolutional neural network. In: 2018 IEEE Nuclear Science Symposium and Medical Imaging Conference Proceedings (NSS/MIC), pp. 1–3. IEEE (2018)
McCollough, C., et al.: Use of water equivalent diameter for calculating patient size and size-specific dose estimates (SSDE) in CT: the report of AAPM task group 220. AAPM Rep. 2014, 6 (2014)
Wang, S., Imran, A., Pal, D., Zucker, E., Wang, A.: Fast monte carlo simulation of non-isotropic x-ray source for CT dose calculation. In: Medical Physics, vol. 48. Wiley, Hoboken (2021)
Willemink, M.J., Persson, M., Pourmorteza, A., Pelc, N.J., Fleischmann, D.: Photon-counting CT: technical principles and clinical prospects. Radiology 289(2), 293–312 (2018)
Withers, P.J., et al.: X-ray computed tomography. Nat. Rev. Methods Primers 1(1), 1–21 (2021)
Yuan, X., et al.: Multimodal contrastive training for visual representation learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6995–7004 (2021)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
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: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. MICCAI 2022. Lecture Notes in Computer Science, vol 13431. Springer, Cham. https://doi.org/10.1007/978-3-031-16431-6_60
Download citation
DOI: https://doi.org/10.1007/978-3-031-16431-6_60
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-16430-9
Online ISBN: 978-3-031-16431-6
eBook Packages: Computer ScienceComputer Science (R0)
