  Multi-spectral optoacoustic tomography (MSOT) is an emerging optical imaging
method providing multiplex molecular and functional information from the rodent
brain. It can be greatly augmented by magnetic resonance imaging (MRI) that
offers excellent soft-tissue contrast and high-resolution brain anatomy.
Nevertheless, registration of multi-modal images remains challenging, chiefly
due to the entirely different image contrast rendered by these modalities.
Previously reported registration algorithms mostly relied on manual
user-dependent brain segmentation, which compromised data interpretation and
accurate quantification. Here we propose a fully automated registration method
for MSOT-MRI multimodal imaging empowered by deep learning. The automated
workflow includes neural network-based image segmentation to generate suitable
masks, which are subsequently registered using an additional neural network.
Performance of the algorithm is showcased with datasets acquired by
cross-sectional MSOT and high-field MRI preclinical scanners. The automated
registration method is further validated with manual and half-automated
registration, demonstrating its robustness and accuracy.
