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. 2018 Jan 23;8:1459. doi: 10.1038/s41598-017-18857-y

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© The Author(s) 2018

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Workflow of the virtual craniotomy deconvolution algorithm. Transcranial 3D OA and pulse-echo US images are simultaneously acquired. The geometry of the skull and the apparent position of the brain vasculature are extracted using the time of flight and a constant speed of sound assumptions. The OA and US signals are then transformed into the frequency domain and the ultrasound reflection spectrum is fitted using a wave model. Subsequently, the insertion loss is calculated using the skull elastic constants that reached the best fit into the reflection model. The insertion loss is employed to deconvolve the OA signal spectrum. Finally, the deconvolved spectrum is transformed back into time domain and a distortion-corrected image of the mouse brain is formed.