Abstract
Mitigating the effects of image appearance due to variations in computed tomography (CT) acquisition and reconstruction parameters is a challenging inverse problem. We present CTFlow, a normalizing flows-based method for harmonizing CT scans acquired and reconstructed using different doses and kernels to a target scan. Unlike existing state-of-the-art image harmonization approaches that only generate a single output, flow-based methods learn the explicit conditional density and output the entire spectrum of plausible reconstruction, reflecting the underlying uncertainty of the problem. We demonstrate how normalizing flows reduces variability in image quality and the performance of a machine learning algorithm for lung nodule detection. We evaluate the performance of CTFlow by 1) comparing it with other techniques on a denoising task using the AAPM-Mayo Clinical Low-Dose CT Grand Challenge dataset, and 2) demonstrating consistency in nodule detection performance across 186 real-world low-dose CT chest scans acquired at our institution. CTFlow performs better in the denoising task for both peak signal-to-noise ratio and perceptual quality metrics. Moreover, CTFlow produces more consistent predictions across all dose and kernel conditions than generative adversarial network (GAN)-based image harmonization on a lung nodule detection task. The code is available at https://github.com/hsu-lab/ctflow.
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
Sala, E., et al.: Unravelling tumour heterogeneity using next-generation imaging: radiomics, radiogenomics, and habitat imaging. Clin. Radiol. 72(1), 3 (2017). https://doi.org/10.1016/j.crad.2016.09.013
Fave, X., et al.: Delta-radiomics features for the prediction of patient outcomes in non-small cell lung cancer. Sci. Rep. 7(1), 588 (2017). https://doi.org/10.1038/s41598-017-00665-z
Aerts, H.J.: The potential of radiomic-based phenotyping in precision medicine: a review. JAMA Oncol. 2(12), 1636–1642 (2016). https://doi.org/10.1001/jamaoncol.2016.2631
Aerts, H.J., et al.: Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat. Commun. 5(1), 4006 (2014). https://doi.org/10.1038/ncomms5006
Traverso, A., Wee, L., Dekker, A., Gillies, R.: repeatability and reproducibility of radiomic features: a systematic review. Int. J. Radiat. Oncol. Biol. Phys. 102(4), 1143–1158 (2018). https://doi.org/10.1016/j.ijrobp.2018.05.053
Mackin, D., et al.: Measuring computed tomography scanner variability of radiomics features. Invest. Radiol. 50(11), 757–765 (2015). https://doi.org/10.1097/RLI.0000000000000180
Lu, L., Ehmke, R.C., Schwartz, L.H., Zhao, B.: Assessing agreement between radiomic features computed for multiple CT imaging settings. PLoS ONE 11(12), e0166550 (2016). https://doi.org/10.1371/journal.pone.0166550
Zhao, B., et al.: Reproducibility of radiomics for deciphering tumor phenotype with imaging. Sci. Rep. 6, 23428 (2016). https://doi.org/10.1038/srep23428
Kalpathy-Cramer, J., et al.: Radiomics of lung nodules: a multi-institutional study of robustness and agreement of quantitative imaging features. Tomography. 2(4), 430–437 (2016). https://doi.org/10.18383/j.tom.2016.00235
Lo, P., Young, S., Kim, H.J., Brown, M.S., McNitt-Gray, M.F.: Variability in CT lung-nodule quantification: effects of dose reduction and reconstruction methods on density and texture based features. Med. Phys. 43(8), 4854 (2016). https://doi.org/10.1118/1.4954845
Nan, Y., et al.: Data harmonisation for information fusion in digital healthcare: a state-of-the-art systematic review, meta-analysis and future research directions. Inf. Fusion 82, 99–122 (2022). https://doi.org/10.1016/j.inffus.2022.01.001
Dinh, L., Sohl-Dickstein, J., Bengio, S.: Density estimation using Real NVP (2016)
Lugmayr, A., Danelljan, M., Van Gool, L., Timofte, R.: SRFlow: learning the super-resolution space with normalizing flow. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12350, pp. 715–732. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58558-7_42
Denker, A., Schmidt, M., Leuschner, J., Maass, P., Behrmann, J.: Conditional normalizing flows for low-dose computed tomography image reconstruction (2020)
Zabic, S., Wang, Q., Morton, T., Brown, K.M.: A low dose simulation tool for CT systems with energy integrating detectors. Med. Phys. 40(3), 031102 (2013). https://doi.org/10.1118/1.4789628
Dinh, L., Krueger, D., Bengio, Y.: Nice: non-linear independent components estimation. arXiv preprint arXiv:14108516 (2014)
Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: International Conference on Machine Learning, pp. 448–56. PMLR (2015)
Kingma, D.P., Dhariwal, P.: Glow: generative flow with invertible 1x1 convolutions. In: Advances in Neural Information Processing Systems, vol. 31 (2018)
Wang, X., et al.: EsrGAN: enhanced super-resolution generative adversarial networks. In: Leal-Taixé, L., Roth, S. (eds.) ECCV 2018. LNCS, vol. 11133, pp. 63–79. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-11021-5_5
Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018)
Wolterink, J.M., Leiner, T., Viergever, M.A., Isgum, I.: Generative adversarial networks for noise reduction in low-dose CT. IEEE Trans. Med. Imaging 36(12), 2536–2545 (2017). https://doi.org/10.1109/TMI.2017.2708987
Yang, Q., et al.: Low-dose CT image denoising using a generative adversarial network with Wasserstein distance and perceptual loss. IEEE Trans. Med. Imaging. 37(6), 1348–1357 (2018). https://doi.org/10.1109/TMI.2018.2827462
Wei, L., Lin, Y., Hsu, W.: Using a generative adversarial network for CT normalization and its impact on radiomic features. In: IEEE International Symposium on Biomedical Imaging. Iowa City, IA (2020)
Lim, B., Son, S., Kim, H., Nah, S., Lee, K.M.: Enhanced deep residual networks for single image super-resolution (2017)
Dabov, K., Foi, A., Katkovnik, V., Egiazarian, K.: Image denoising by sparse 3-D transform-domain collaborative filtering. IEEE Trans Image Process. 16(8), 2080–2095 (2007). https://doi.org/10.1109/tip.2007.901238
Lawrence, I., Lin, K.: A concordance correlation coefficient to evaluate reproducibility. Biometrics, 255–268 (1989)
McBride, G.: A proposal for strength-of-agreement criteria for Lin’s concordance correlation coefficient. NIWA client report: HAM2005-062, p. 62 (2005)
Chan, A., Alaa, A., Qian, Z., Van Der Schaar, M.: Unlabelled data improves Bayesian uncertainty calibration under covariate shift. In: International Conference on Machine Learning, pp. 1392–402. PMLR (2020)
Acknowledgments
This work was supported by the National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health under awards R56 EB031993 and R01 EB031993. The authors thank John M. Hoffman, Nastaran Emaminejad, and Michael McNitt-Gray for providing access to the UCLA low-dose CT dataset. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
1 Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Wei, L., Yadav, A., Hsu, W. (2023). CTFlow: Mitigating Effects of Computed Tomography Acquisition and Reconstruction with Normalizing Flows. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14226. Springer, Cham. https://doi.org/10.1007/978-3-031-43990-2_39
Download citation
DOI: https://doi.org/10.1007/978-3-031-43990-2_39
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-43989-6
Online ISBN: 978-3-031-43990-2
eBook Packages: Computer ScienceComputer Science (R0)
