Regions of interest extraction from SPECT images for neural degeneration assessment using multimodality image fusion

摘要

The aging population highlights the importance of early diagnosis of neurodegenerative diseases in the elderly. Current diagnoses of such diseases rely on visual assessment of the neuron activity of the specific regions in the brain revealed by SPECT imaging with a specific tracer, 99mTc-TRODAT-1. However, due to the difficulties in defining the regions of interest (ROI) in SPECT images, efficient indices are lacking for quantitative analysis. In this study, we performed simultaneous CT and SPECT scans and used the CT images as the medium to register the MR and SPECT images, such that the ROI delineated in the MR image can be mapped onto the SPECT image in the corresponding area. A robust registration scheme is proposed, including coarse registration using principal axes alignment and then fine-tuning the registration using a combination of maximal cross-section area detection and the general Hough transform. The results from three clinical datasets all show improved accuracy of registration as compared with the results obtained using conventional principal axes alignment alone. Based on these registration results, a correct ROI can be defined in the SPECT images and ROI-based quantitative indices can be further derived.