Elastic Fusion Enables Fusion of Intraoperative Magnetic Resonance Imaging Data with Preoperative Neuronavigation Data

Object Intra-operative magnetic resonance imaging (iMRI) was shown to optimize the extent of resection (EOR) of parenchymal brain tumors. To facilitate the use of pre-operative treatment plans after an intra-operative navigation update via iMRI, an elastic image fusion (EIF) algorithm was developed. Methods Ten MRI-iMRI data pairs of brain tumor patients were evaluated and typical anatomical landmarks were assessed. The pre-operative and intra-operative MRI scans were elastically fused by using a prototype EIF software (Elements Virtual iMRI, Brainlab AG). For each landmark pair the Euclidean distance was calculated for rigidly and elastically fused image data. Results The Euclidean distance was 2.67 ± 2.62 mm using standard RIF and 1.8 ± 1.57 mm using our EIF algorithm (p = 0.005). For landmarks near the resected lesion, which were subject to higher anatomical distortion the Euclidian distances were 4.38 ± 2.51 mm and 2.52 ± 1.9 mm, (p = 0.003). Conclusion This feasibility study shows that EIF can compensate for surgery-related brain shift in a highly significant manner even in this small number of cases. The establishment of an easy applicable and reliable EIF tool integrated in the clinical workflow could open a large variety of new options for image-guided tumor surgery.