REGISTRATION OF DYNAMIC DOPAMINE D-2 RECEPTOR IMAGES USING PRINCIPAL COMPONENT ANALYSIS

This paper describes a novel technique for registering a dynamic seque nce of single-photon emission tomography (SPET) dopamine D-2 receptor images, using principal component analysis (PCA). Conventional methods for registering images, such as count difference and correlation coef ficient algorithms, fail to take into account the dynamic nature of th e data, resulting in large systematic errors when registering time-var ying images. However, by using principal component analysis to extract the temporal structure of the image sequence, misregistration can be quantified by examining the distribution of eigenvalues. The registrat ion procedures were tested using a computer-generated dynamic phantom derived from a high-resolution magnetic resonance image of a realistic brain phantom. Each method was also applied to clinical SPET images o f dopamine D-2 receptors, using the ligands iodine-123 iodobenzamide a nd iodine-123 epidepride, to investigate the influence of misregistrat ion on kinetic modelling parameters and the binding potential. The PCA technique gave highly significant (P<0.001) improvements in image reg istration, leading to alignment errors in x and y of about 25% of the alternative methods, with reductions in autocorrelations over time. It could also be applied to align image sequences which the other method s failed completely to register, particularly I-123-epidepride scans. The PCA method produced data of much greater quality for subsequent ki netic modelling, with an improvement of nearly 50% in the chi(2) of th e fit to the compartmental model, and provided superior quality regist ration of particularly difficult dynamic sequences.