ARENA: Inter-modality affine registration using evolutionary strategy.

PURPOSE: Image fusion of different imaging modalities renders valuable information to clinicians. In this paper, we proposed an automatic multimodal registration method to register intra-operative ultrasound images (US) to preoperative magnetic resonance images (MRI) in the context of image-guided neurosurgery.

METHODS: We employed refined correlation ratio as a similarity metric for our intensity-based image registration method. We deem MRI as the fixed image ([Formula: see text]) and US as the moving image ([Formula: see text]) and then transform [Formula: see text] to align with [Formula: see text]. We utilized the covariance matrix adaptation evolutionary strategy to find the optimal affine transformation in registration of [Formula: see text] to [Formula: see text].

RESULTS: We applied our method on the publicly available retrospective evaluation of cerebral tumors (RESECT) database and Montreal Neurological Institute's brain images of tumors for evaluation (BITE) database. We validated the results qualitatively and quantitatively. Qualitative validation is conducted (by the three authors) through overlaying pre- and post-registration US and MRI to allow visual assessment of the alignment. Quantitative validation is performed by utilizing the corresponding landmarks in the databases for the preoperative MRI and the intra-operative US. Average mean target registration error (mTRE) has been reduced from [Formula: see text] to [Formula: see text] in 22 patients in the RESECT database and from [Formula: see text] to [Formula: see text] in the BITE database. A nonparametric statistical analysis performed using the Wilcoxon rank sum test shows that there is a significant difference between pre- and post-registration mTREs with a p value of [Formula: see text] for the RESECT database and [Formula: see text] for the BITE database.

CONCLUSIONS: The proposed fully automatic registration method significantly improved the alignment of MRI and US images and can therefore be used to reduce the misalignment of US and MRI caused by brain shift, calibration errors, and patient to MRI transformation matrix.