ArticleInternational journal of computer assisted radiology and surgery2019
ARENA: Inter-modality affine registration using evolutionary strategy.
Article in International journal of computer assisted radiology and surgery, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 16 citations in OpenAlex.
- Advances in TEE-Centric Intraprocedural Multimodal Image Guidance for Congenital and Structural Heart Disease.Diagnostics (Basel, Switzerland) · 2023Review
- Robust landmark-based brain shift correction with a Siamese neural network in ultrasound-guided brain tumor resection.International journal of computer assisted radiology and surgery · 2023Article
- Multimodal 3D ultrasound and CT in image-guided spinal surgery: public database and new registration algorithms.International journal of computer assisted radiology and surgery · 2021Review
- The Essential Role of Open Data and Software for the Future of Ultrasound-Based Neuronavigation.Frontiers in oncology · 2020Article
- Brain Shift in Neuronavigation of Brain Tumors: An Updated Review of Intra-Operative Ultrasound Applications.Frontiers in oncology · 2020Review
- Deformable MRI-Ultrasound registration using correlation-based attribute matching for brain shift correction: Accuracy and generality in multi-site data.NeuroImage · 2019Article
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Authors and funding
3 authors at 3 institutions in 2 countries.
Funding
Abstract
purposeImage 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.
methodsWe 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].
resultsWe 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.
conclusionsThe 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.
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