Improved accuracy of brain MRI/SPECT registration using a two-cluster SPECT normalization algorithm and a combinative similarity measure: Application to the evaluation of Parkinson's disease

Objective: Single-photon emission computed tomography (SPECT) of dopamine transporters with technetium-99-labeled tropane derivative ^99mTc- TRODAT-1 has recently been suggested to offer valuable information in assessing the functionality of dopaminergic systems. To facilitate the non-invasive examination of the dopamine concentration in vivo, registering magnetic resonance imaging (MRI) and SPECT image is important. This article proposes a new similarity measure for MRI/SPECT registration. Methods: The proposed similarity measure combines anatomic features that are characterized by specific binding of nuclear medicine and the distribution of image intensity that are characterized by the normalized mutual information (NMI). A preprocess, a novel two-cluster SPECT normalization algorithm, is also proposed. Results: Compared with the conventional NMI-based registration algorithm, the proposed registration framework reduces the target of registration error from >7 mm to approximately 4 mm. The error of the specific-to-non-specific ^99mTc-TRODAT-1 binding ratio (BR), a quantitative measure of TRODAT receptor binding, is also reduced from 0.45 to 0.08 in the healthy subjects and from 0.28 to 0.12 in Parkinson's disease patients. Conclusions: A suitable color map, such as "rainbow," for image display enables the two-cluster SPECT normalization algorithm to provide clinically meaningful visual contrast. In addition, registering MRI/SPECT based on the proposed similarity measure improves the accuracy compared with the conventional NMI-based algorithm.