Nonlinear registration of diffusion-weighted images improves clinical sensitivity of functional diffusion maps in recurrent glioblastoma treated with bevacizumab

Abstract

Diffusion-weighted imaging estimates of apparent diffusion coefficient (ADC) have shown sensitivity to brain tumor cellularity as well as response to therapy. Functional diffusion maps (fDMs) exploit these principles by examining voxelwise changes in ADC within the same patient over time. Currently, the fDM technique involves linear image registration of ADC maps from subsequent follow-up times to pretreatment ADC maps; however, misregistration of ADC maps due to geometric distortions as well as mass effect from growing tumor can confound fDM measurements. In this study, we compare the use of a nonlinear registration scheme to the current linear fDM technique in 70 patients with recurrent glioblastoma multiforme treated with bevacizumab. Results suggest that nonlinear registration of pretreatment ADC maps to post-treatment ADC maps improves the clinical predictability, sensitivity, and specificity of fDMs for both progression-free and overall survival.