Automatic image registration of optoacoustic tomography and magnetic resonance imaging based on deep learning

Yexing Hu; Berkan Lafci; Artur Luzgin; Hao Wang; Jan Klohs; Xosé Luís Deán-Ben; Ruiqing Ni; Daniel Razansky; Wuwei Ren

doi:10.1117/12.2655214

26 October 2022 Automatic image registration of optoacoustic tomography and magnetic resonance imaging based on deep learning

Yexing Hu, Berkan Lafci, Artur Luzgin, Hao Wang, Jan Klohs, Xosé Luís Deán-Ben, Ruiqing Ni, Daniel Razansky, Wuwei Ren

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Proceedings Volume 12461, First Conference on Biomedical Photonics and Cross-Fusion (BPC 2022); 1246103 (2022) https://doi.org/10.1117/12.2655214
Event: First Conference on Biomedical Photonics and Cross-Fusion (BPC 2022), 2022, Shanghai, China

Abstract

Multi-spectral optoacoustic tomography (MSOT) combines rich contrast of optical imaging and high resolution of ultrasound, and becomes an attractive biomedical research tool in the last decade. Aligning MSOT images with anatomical map provided by magnetic resonance imaging (MRI) can potentially enhance the interpretation of optoacoustic signal which mainly reflects molecular and functional information. Therefore, developing an automated algorithm of image registration between MSOT and MRI is crucial. Existing MSOT-MRI registration algorithms mostly relied on manual segmentation, which requires user-dependent experience. Herein, we developed a fully automated algorithm for MSOT-MRI registration based on deep learning (DL). This workflow consists of DL-based segmentation and image transformation. We have experimentally demonstrated the accuracy and computational efficiency of the method, paving the way towards high-throughput MSOT data analysis in close future.

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Yexing Hu, Berkan Lafci, Artur Luzgin, Hao Wang, Jan Klohs, Xosé Luís Deán-Ben, Ruiqing Ni, Daniel Razansky, and Wuwei Ren "Automatic image registration of optoacoustic tomography and magnetic resonance imaging based on deep learning", Proc. SPIE 12461, First Conference on Biomedical Photonics and Cross-Fusion (BPC 2022), 1246103 (26 October 2022); https://doi.org/10.1117/12.2655214

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KEYWORDS

Image segmentation

Magnetic resonance imaging

Image registration

Brain

Optoacoustics

Neuroimaging

Algorithm development

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