E. Montin, A. Belfatto, M. Bologna, S. Meroni, C. Cavatorta, E. Pecori, B. Diletto, M. Massimino, M. C. Oprandi, G. Poggi, F. Arrigoni, D. Peruzzo, E. Pignoli, L. Gandola, P. Cerveri, L. Mainardi

A multi-metric registration strategy for the alignment of longitudinal brain images in pediatric oncology

Medical Imaging Analysis and Diagnostics

Survival of pediatric patients with brain tumor has increased over the past 20 years, and increasing evidence of iatrogenic toxicities has been reported. In follow-ups, images are acquired at different time points where substantial changes of brain morphology occur, due to childhood physiological development and treatment effects. To address the image registration complexity, we propose two multi-metric approaches (Mplus, Mdot), combining mutual information (MI) and normalized gradient field filter (NGF). The registration performance of the proposed metrics was assessed on a simulated dataset (Brainweb) and compared with those obtained by MI and NGF separately, using mean magnitude and mean angular errors. The most promising metric (Mplus) was then selected and tested on a retrospective dataset comprising 45 pediatric patients who underwent focal radiotherapy for brain cancer. The quality of the realignment was scored by a radiation oncologist using a perceived misalignment metric (PM). All patients but one were assessed as PM ≤ 2 (good alignment), but the remaining one, severely affected by hydrocephalus and pneumocephalus at the first MRI acquisition, scored PM = 5 (unacceptable). These preliminary findings suggest that Mplus might improve the registration accuracy in complex applications such as pediatric oncology, when data are acquired throughout the years of follow-up, and is worth investigating.

This article is authored also by Synbrain data scientists and collaborators. READ THE FULL ARTICLE