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

OBJECTIVE: Intraoperative magnetic resonance imaging (iMRI) has been shown to optimize the extent of resection of parenchymal brain tumors. To facilitate the use of preoperative treatment plans after an intraoperative navigation update via iMRI, an elastic image fusion (EIF) algorithm was developed. METHODS: Ten MRI-iMRI data pairs of patients with brain tumor were evaluated and typical anatomic landmarks were assessed. The pre- and iMRI 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 rigid image fusion and 1.8 ± 1.57 mm using our EIF algorithm (P = 0.005). For landmarks near the resected lesion, which were subject to higher anatomic distortion, the Euclidian distances were 4.38 ± 2.51 and 2.52 ± 1.9 mm (P = 0.003). CONCLUSIONS: 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.      «

OBJECTIVE: Intraoperative magnetic resonance imaging (iMRI) has been shown to optimize the extent of resection of parenchymal brain tumors. To facilitate the use of preoperative treatment plans after an intraoperative navigation update via iMRI, an elastic image fusion (EIF) algorithm was developed. METHODS: Ten MRI-iMRI data pairs of patients with brain tumor were evaluated and typical anatomic landmarks were assessed. The pre- and iMRI scans were elastically fused by using a prototype EIF soft...     »