Artificial intelligence can improve quality of images recorded by a relatively new biomedical imaging method. This paves the way towards more accurate diagnosis and cost-effective devices.
The new approach allows for substantial reduction of the number of sensors without giving up on the resulting image quality. This makes it possible to reduce the device cost, increase imaging speed or improve diagnosis.
The team searched for a way to enhance image quality of low-cost optoacoustic devices that possess only a small number of ultrasonic sensors. To do this, they started off by using a self-developed high-end optoacoustic scanner having 512 sensors, which delivered superior-quality images. They had these pictures analyzed by an artificial neural network, which was able to learn the features of the high-quality images.
Next, the researchers discarded the majority of the sensors, so that only 128 or 32 sensors remained, with a detrimental effect on the image quality. Due to the lack of data, distortions known as streak type artefacts appeared in the images. It turned out, however, that the previously trained neural network was able to largely correct for these distortions, thus bringing the image quality closer to the measurements obtained with all the 512 sensors.