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Advanced Science 10(1), 2202089, 2023

https://onlinelibrary.wiley.com/doi/full/10.1002/advs.202202089

■ Researchers

Seongwook Choi, Soo Young Lee

Department of Mechanical Engineering, Pohang University of Science and Technology (POSTECH)

Park C. -S., Park K., Lee H. J., Lee S., 

■ Abstract

 Photoacoustic computed tomography (PACT) has become a premier preclinical and clinical imaging modality. Although PACT is 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.