Abstract
Synthesizing magnetic resonance (MR) and computed tomography (CT) images (from each other) has important implications for clinical neuroimaging. The MR to CT direction is critical for MRI-based radiotherapy planning and dose computation, whereas the CT to MR direction can provide an economic alternative to real MRI for image processing tasks. Additionally, synthesis in both directions can enhance MR/CT multi-modal image registration. Existing approaches have focused on synthesizing CT from MR. In this paper, we propose a multi-atlas based hybrid method to synthesize T1-weighted MR images from CT and CT images from T1-weighted MR images using a common framework. The task is carried out by: (a) computing a label field based on supervoxels for the subject image using joint label fusion; (b) correcting this result using a random forest classifier (RF-C); (c) spatial smoothing using a Markov random field; (d) synthesizing intensities using a set of RF regressors, one trained for each label. The algorithm is evaluated using a set of six registered CT and MR image pairs of the whole head.
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
Achanta, R., et al.: SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Trans. Pattern Anal. Mach. Intell. 34(11), 2274–2282 (2012)
Bai, W., et al.: Multi-atlas segmentation with augmented features for cardiac MR images. Med. Image Anal. 19(1), 98–109 (2015)
Boykov, Y., et al.: Fast approximate energy minimization via graph cuts. IEEE Trans. Pattern Anal. Mach. Intell. 23(11), 1222–1239 (2001)
Burgos, N., et al.: Attenuation correction synthesis for hybrid PET-MR scanners: application to brain studies. IEEE Trans. Med. Imaging 33(12), 2332–2341 (2014)
Burgos, N., et al.: Robust CT synthesis for radiotherapy planning: application to the head and neck region. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9350, pp. 476–484. Springer, Cham (2015). doi:10.1007/978-3-319-24571-3_57
Cao, X., et al.: Dual-core steered non-rigid registration for multi-modal images via bi-directional image synthesis. Med. Image Anal. 41, 18–31 (2017)
Chen, M., et al.: Cross contrast multi-channel image registration using image synthesis for MR brain images. Med. Image Anal. 36, 2–14 (2017)
Huynh, T., et al.: Estimating CT image from MRI data using structured random forest and auto-context model. IEEE Trans. Med. Imaging 35(1), 174–183 (2016)
Iglesias, J.E., Konukoglu, E., Zikic, D., Glocker, B., Van Leemput, K., Fischl, B.: Is synthesizing MRI contrast useful for inter-modality analysis? In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013. LNCS, vol. 8149, pp. 631–638. Springer, Heidelberg (2013). doi:10.1007/978-3-642-40811-3_79
Jog, A., et al.: Random forest regression for magnetic resonance image synthesis. Med. Image Anal. 35, 475–488 (2017)
Komodakis, N.: Image completion using global optimization. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 442–452. IEEE (2006)
Lee, J., et al.: Multi-atlas-based CT synthesis from conventional MRI with patch-based refinement for MRI-based radiotherapy planning. In: SPIE Medical Imaging, pp. 101331I–101331I-6. International Society for Optics and Photonics (2017)
Pham, D.L., et al.: Current methods in medical image segmentation. Annu. Rev. Biomed. Eng. 2(1), 315–337 (2000)
Riederer, S.J., et al.: Automated MR image synthesis: feasibility studies. Radiology 153(1), 203–206 (1984)
Roy, S., et al.: Magnetic resonance image example-based contrast synthesis. IEEE Trans. Med. Imaging 32(12), 2348–2363 (2013)
Roy, S., et al.: PET attenuation correction using synthetic CT from ultrashort echo-time MR imaging. J. Nucl. Med. 55(12), 2071–2077 (2014)
Wang, H., et al.: Multi-atlas segmentation with joint label fusion. IEEE Trans. Pattern Anal. Mach. Intell. 35(3), 611–623 (2013)
Zhao, C., et al.: Whole brain segmentation and labeling from CT using synthetic MR images. In: 8th International Workshop on Machine Learning in Medical Imaging. LNCS. Springer, Heidelberg (2017)
Acknowledgments
This work was supported by NIH/NIBIB grant R01-EB017743.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Zhao, C., Carass, A., Lee, J., Jog, A., Prince, J.L. (2017). A Supervoxel Based Random Forest Synthesis Framework for Bidirectional MR/CT Synthesis. In: Tsaftaris, S., Gooya, A., Frangi, A., Prince, J. (eds) Simulation and Synthesis in Medical Imaging. SASHIMI 2017. Lecture Notes in Computer Science(), vol 10557. Springer, Cham. https://doi.org/10.1007/978-3-319-68127-6_4
Download citation
DOI: https://doi.org/10.1007/978-3-319-68127-6_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-68126-9
Online ISBN: 978-3-319-68127-6
eBook Packages: Computer ScienceComputer Science (R0)