Abstract
Positron emission tomography (PET) is a nuclear medical imaging technology that produces 3D images of tissue metabolic activity in human body. PET has been used in various clinical applications, such as diagnosis of tumors and diffuse brain disorders. High quality PET image plays an essential role in diagnosing diseases/disorders and assessing the response to therapy. In practice, in order to obtain the high quality PET images, standard-dose radionuclide (tracer) needs to be used and injected into the living body. As a result, it will inevitably increase the risk of radiation. In this paper, we propose a regression forest (RF) based framework for predicting standard-dose PET images using low-dose PET and corresponding magnetic resonance imaging (MRI) images instead of injecting the standard-dose radionuclide into the body. The proposed approach has been evaluated on a dataset consisting of 7 subjects using leave-one-out cross-validation. Moreover, we compare the prediction performance between sparse representation (SR) based method and our proposed method. Both qualitative and quantitative results illustrate the practicability of our proposed method.
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
Preview
Unable to display preview. Download preview PDF.
References
Rohren, E.M., Turkington, T.G., Coleman, R.E.: Clinical Applications of PET in Oncology. Radiology 231, 305–332 (2004)
Zhang, D., Wang, Y., Zhou, L., Yuan, H., Shen, D.: Multimodal Classification of Alzheimer’s Disease and Mild Cognitive Impairment. NeuroImage 55, 856–867 (2011)
Bai, W., Brady, M.: Motion Correction and Attenuation Correction for Respiratory Gated PET Images. IEEE TMI 30, 351–365 (2011)
Breiman, L.: Random Forests. Machine Learning 45, 5–32 (2001)
Lindner, C., Thiagarajah, S., Wilkinson, J.M., arcOGEN Consortium, Wallis, G.A., Cootes, T.F.: Fully Automatic Segmentation of the Proximal Femur using Random Forest Regression Voting. IEEE TMI 32, 1462–1472 (2013)
Fanelli, G., Dantone, M., Gall, J., Fossati, A., Van Gool, L.: Random Forests for Real Time 3D Face Analysis. IJCV 101, 437–458 (2013)
Gao, Y., Liao, S., Shen, D.: Prostate Segmentation by Sparse Representation Based Classification. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012, Part III. LNCS, vol. 7512, pp. 451–458. Springer, Heidelberg (2012)
Fischer, B., Modersitzki, J.: FLIRT: A Flexible Image Registration Toolbox. In: Gee, J.C., Maintz, J.B.A., Vannier, M.W. (eds.) WBIR 2003. LNCS, vol. 2717, pp. 261–270. Springer, Heidelberg (2003)
Zhang, Y., Brady, M., Smith, S.: Segmentation of Brain MR Images through a Hidden Markov Random Field Model and the Expectation-maximization Algorithm. IEEE TMI 20, 45–57 (2001)
Yang, J., Wright, J., Huang, T.S., Ma, Y.: Image Super-resolution via Sparse Representation. IEEE TIP 19, 2861–2873 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Kang, J. et al. (2014). Prediction of Standard-Dose PET Image by Low-Dose PET and MRI Images. In: Wu, G., Zhang, D., Zhou, L. (eds) Machine Learning in Medical Imaging. MLMI 2014. Lecture Notes in Computer Science, vol 8679. Springer, Cham. https://doi.org/10.1007/978-3-319-10581-9_35
Download citation
DOI: https://doi.org/10.1007/978-3-319-10581-9_35
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-10580-2
Online ISBN: 978-3-319-10581-9
eBook Packages: Computer ScienceComputer Science (R0)