A Brain MRI/SPECT Registration System Using an Adaptive Similarity Metric: Application on the Evaluation of Parkinson’s Disease
Single photon emission computed tomography (SPECT) of dopamine transporters with 99m Tc-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 99m Tc-TRODAT-1 binding, is 0.20 in the healthy group and 0.13 in the patient group via the proposed system.
KeywordsRegistration MRI SPECT Medical Imaging Similarity Metric
Unable to display preview. Download preview PDF.
- 3.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)Google Scholar
- 4.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)Google Scholar
- 7.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)Google Scholar
- 13.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)Google Scholar
- 14.Press, W.H., et al.: Numerical Recipes in C++, 2nd edn. Cambridge University Press, Cambridge (2002)Google Scholar