A deep neural network for real-time optoacoustic image reconstruction with adjustable speed of sound

Multispectral optoacoustic tomography is a high-resolution functional imaging modality that can non-invasively access a broad range of pathophysiological phenomena. Real-time imaging would enable translation of multispectral optoacoustic tomography into clinical imaging, visualize dynamic pathophysiological changes associated with disease progression and enable in situ diagnoses. Model-based reconstruction affords state-of-the-art optoacoustic images but cannot be used for real-time imaging. On the other hand, deep learning enables fast reconstruction of optoacoustic images, but the lack of experimental ground-truth training data leads to reduced image quality for in vivo scans. In this work we achieve accurate optoacoustic image reconstruction in 31 ms per image for arbitrary (experimental) input data by expressing model-based reconstruction with a deep neural network. The proposed deep learning framework, DeepMB, generalizes to experimental test data through training on optoacoustic signals synthesized from real-world images and ground truth optoacoustic images generated by model-based reconstruction. Based on qualitative and quantitative evaluation on a diverse dataset of in vivo images, we show that DeepMB reconstructs images approximately 1,000-times faster than the iterative model-based reference method while affording near-identical image qualities. Accurate and real-time image reconstructions with DeepMB can enable full access to the high-resolution and multispectral contrast of handheld optoacoustic tomography, thus adoption into clinical routines. State-of-the-art image reconstruction for multispectral optoacoustic tomography is currently too slow for clinical applications. Dehner, Zahnd et al. propose a deep learning framework to reconstruct optoacoustic images in real-time while maintaining similar quality.