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• W2308954192 abstract "Image-based ultrasound to magnetic resonance image (US-MRI) registration can be an invaluable tool in image-guided neuronavigation systems. State-of-the-art commercial and research systems utilize image-based registration to assist in functions such as brain-shift correction, image fusion, and probe calibration. Since traditional US-MRI registration techniques use reconstructed US volumes or a series of tracked US slices, the functionality of this approach can be compromised by the limitations of optical or magnetic tracking systems in the neurosurgical operating room. These drawbacks include ergonomic issues, line-of-sight/magnetic interference, and maintenance of the sterile field. For those seeking a US vendor-agnostic system, these issues are compounded with the challenge of instrumenting the probe without permanent modification and calibrating the probe face to the tracking tool. To address these challenges, this paper explores the feasibility of a real-time US-MRI volume registration in a small virtual craniotomy site using a single slice. We employ the Linear Correlation of Linear Combination (LC2) similarity metric in its patch-based form on data from MNI’s Brain Images for Tumour Evaluation (BITE) dataset as a PyCUDA enabled Python module in Slicer. By retaining the original orientation information, we are able to improve on the poses using this approach. To further assist the challenge of US-MRI registration, we also present the BOXLC2 metric which demonstrates a speed improvement to LC2, while retaining a similar accuracy in this context." @default.
• W2308954192 created "2016-06-24" @default.
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• W2308954192 date "2016-03-18" @default.
• W2308954192 modified "2023-09-23" @default.
• W2308954192 title "Single slice US-MRI registration for neurosurgical MRI-guided US" @default.
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• W2308954192 doi "https://doi.org/10.1117/12.2214265" @default.
• W2308954192 hasPublicationYear "2016" @default.
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