Computer assessment of neurodegeneration in Parkinson disease using data fusion techniques with MR images

Abstract

Rationale and Objectives. Recently developed MR imaging techniques using inversion recovery are a sensitive tool to identify and quantify morphologic changes in the substantia nigra due to neurodegeneration. Using a semi-automated computer segmentation technique to isolate the substantia nigra pars compacta (SN C), we propose a colored image fusion technique to visually assess the sites of damage in the SN C and integrate the information obtained from two implemented inversion-recovery sequences. Patients and Methods. Six patients and six age-matched control subjects were scanned using a combination of two MR imaging inversion-recovery (IR) pulse sequences. A subgroup of them was used to develop our technique. Images were blended together into a final (RGBA) image, where A stands for the α channel describing transparency. Results. Abnormalities in the SN C can be accurately assessed in location, shape, and variations of signal intensities within the segmented SN C by varying the transparency (α) channel of the color fusion image. Several previous findings such as the lateral–medial gradient of signal change and a ventral–dorsal broadening of the pars compacta are accompanied by an overall mild-to-severe heterogeneity of neurodegeneration patterns. Conclusion. Color fusion techniques revealed subtle changes in the neurodegeneration of the substantia nigra in Parkinson disease, which can be helpful for an objective and hence effective visual assessment of disease progression.