Abstract
Single photon emission computed tomography (SPECT) of dopamine transporters with 99mTc-TRODAT-1 has recently been proposed to provide valuable information of assessing the dopaminergic system. In order to measure the binding ratio of the nuclear medicine, registering magnetic resonance imaging (MRI) and SPECT image is a significant process. Therefore, an automated MRI/SPECT image registration algorithm of using an adaptive similarity metric is proposed. This similarity metric combines anatomic features characterized by specific binding (SB), the mean counts per voxel within the specific tissues, of nuclear medicine and distribution of image intensity characterized by the Normalized Mutual Information (NMI). In addition, we have also built a computer-aid clinical diagnosis system which automates all the processes of MRI/SPECT registration for further evaluation of Parkinson’s disease. Clinical MRI/SPECT data from eighteen healthy subjects and thirteen patients are involved to validate the performance of the proposed system. Comparing with the conventional NMI-based registration algorithm, our system reduces the target of registration error (TRE) from >7 mm to approximate 4 mm. From the view point of clinical evaluation, the error of binding ratio, the ratio of specific-to-non-specific 99mTc-TRODAT-1 binding, is 0.20 in the healthy group and 0.13 in the patient group via the proposed system.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Pohjonen, H.K., et al.: Abdominal SPECT/MRI fusion applied to the study of splenic and hepatic uptake of radiolabeled thrombocytes and colloids. Ann. Nucl. Med. 10, 409–417 (1996)
Forster, G.J., et al.: SPET/CT image co-registration in the abdomen with a simple and cost-effective tool. Eur. J. Nucl. Med. Mol. Imaging 30, 32–39 (2003)
Weng, Y.H., et al.: Sensitivity and specificity of 99mTc-TRODAT-1 SPECT imaging in differentiating patients with idiopathic Parkinson’s disease from healthy subjects. J. Nucl. Med. 45, 393–401 (2004)
Grova, C., et al.: A methodology to validate MRI/SPECT registration methods using realistic simulated SPECT data. In: Niessen, W.J., Viergever, M.A. (eds.) MICCAI 2001. LNCS, vol. 2208, pp. 275–282. Springer, Heidelberg (2001)
Yokoi, T., et al.: Accuracy and reproducibility of co-registration techniques based on mutual information and normalized mutual information for MRI and SPECT brain images. Ann. Nucl. Med. 18, 659–667 (2004)
Thurfjell, L., et al.: Improved efficiency for MR-SPET registration based on mutual information. Eur. J. Nucl. Med. 27, 847–856 (2000)
Zhu, Y.M., Cochoff, S.M.: Influence of implementation parameters on registration of MR and SPECT brain images by maximization of mutual information. J. Nucl. Med. 43, 160–166 (2002)
Maes, F., et al.: Multimodality image registration by maximization of mutual information. IEEE Trans. Med. Imaging 16, 187–198 (1997)
Pluim, J.P.W., Maintz, J.B.A., Viergever, M.A.: Mutual-information-based registration of medical images: a survey. IEEE Trans. Med. Imaging 22, 986–1004 (2003)
Studholme, C., Hill, D.L.G., Hawkes, D.J.: An overlap invariant entropy measure of 3D medical image alignment. Pattern Recognition 32, 71–86 (1999)
Periaswamy, S., Farid, H.: Elastic registration in the presence of intensity variations. IEEE Trans. Med. Imaging 22, 865–874 (2003)
Collins, D.L., et al.: Automatic 3D model-based neuroanatomical segmentation. Hum. Brain Mapp. 3, 190–208 (1995)
Pfluger, T., et al.: Quantitative comparison of automatic and interactive methods for MRI-SPECT image registration of the brain based on 3-Dimensional calculation of error. J. Nucl. Med. 41, 1823–1829 (2000)
Press, W.H., et al.: Numerical Recipes in C++, 2nd edn. Cambridge University Press, Cambridge (2002)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
Lee, JD., Huang, CH., Chen, CW., Weng, YH., Lin, KJ., Chen, CT. (2007). A Brain MRI/SPECT Registration System Using an Adaptive Similarity Metric: Application on the Evaluation of Parkinson’s Disease. In: Gagalowicz, A., Philips, W. (eds) Computer Vision/Computer Graphics Collaboration Techniques. MIRAGE 2007. Lecture Notes in Computer Science, vol 4418. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71457-6_22
Download citation
DOI: https://doi.org/10.1007/978-3-540-71457-6_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-71456-9
Online ISBN: 978-3-540-71457-6
eBook Packages: Computer ScienceComputer Science (R0)