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In optoacoustics, image quality depends on the number and distribution of sensors used by the device; the more sensors and the more broadly they are arranged, the better the quality.
To improve image quality in low-cost optoacoustic devices with only a small number of ultrasonic sensors, researchers at ETH Zurich and the University of Zurich turned to machine learning. They developed a framework for the efficient recovery of image quality from sparse optoacoustic data using a deep convolutional neural network and demonstrated their approach with whole body mouse imaging in vivo.
To generate accurate, high-resolution reference images for training, the team began by developing a high-end optoacoustic scanner with 512 sensors. An…READ MORE