Deep Learning Enhances Multiparametric Dynamic Volumetric Photoacoustic Computed Tomography In Vivo (DL‐PACT)

Abstract

Abstract Photoacoustic computed tomography (PACT) has become a premier preclinical and clinical imaging modality. Although PACT's image quality can be dramatically improved with a large number of ultrasound (US) transducer elements and associated multiplexed data acquisition systems, the associated high system cost and/or slow temporal resolution are significant problems. Here, a deep learning‐based approach is demonstrated that qualitatively and quantitively diminishes the limited‐view artifacts that reduce image quality and improves the slow temporal resolution. This deep learning‐enhanced multiparametric dynamic volumetric PACT approach, called DL‐PACT, requires only a clustered subset of many US transducer elements on the conventional multiparametric PACT. Using DL‐PACT, high‐quality static structural and dynamic contrast‐enhanced whole‐body images as well as dynamic functional brain images of live animals and humans are successfully acquired, all in a relatively fast and cost‐effective manner. It is believed that the strategy can significantly advance the use of PACT technology for preclinical and clinical applications such as neurology, cardiology, pharmacology, endocrinology, and oncology.

Keywords

• deep learning
• multiparametric imaging
• photoacoustic imaging