Deep learning for biomedical photoacoustic imaging: A review

Photoacoustic imaging (PAI) is a promising emerging modality that enables spatially resolved of optical tissue properties up to several centimeters deep in tissue, creating the potential for numerous exciting clinical applications. However, extraction relevant parameters from raw data requires solving inverse image reconstruction problems, which have proven extremely difficult solve. The application learning methods has recently exploded popularity, leading impressive successes context medical and also finding first use field PAI. Deep possess unique advantages can facilitate translation PAI, such as fast computation times fact they be adapted any given problem. In this review, we examine current state art regarding PAI identify directions research will help reach goal applicability