Photoacoustic Tomography (PAT) is a useful tool for fast 3D imaging that provides structural, molecular, and functional in vivo information. It is capable of producing 3D images using a multi-element hemispherical array transducer. PAT images can be enhanced a great number of ultrasonic transducer components with multiplexers, but this can result in high costs and slow temporal resolution because of using multiplexers. In this research, we present a deep learning solution to improve both the spatial and temporal resolution in PAT. We demonstrated that the trained neural network enhanced the image quality of a quarter-cluster-sampled data of static whole-body imaging. Our approach increased limited-view aperture and the spatial resolution by around three and two times, respectively. Additionally, it allowed to improve temporal resolution by four times without multiplexing. Our method also demonstrated excellent performance in contrast-enhanced PA imaging, enabling molecular imaging. Our strategy has the potential to enable high spatial and temporal resolution observation of biodynamics in 3D PAT without being limited by hardware constraints.
Photoacoustic computed tomography (PACT) has emerged as a practical tool for fast 3D imaging with optical contrast that give morphological, functional, and molecular in vivo information. The spatiotemporal resolution of the PACT system are decided by the composed hardware specification. Hence, to achieve better image quality and faster imaging speed, the high-specification hardware should be supported, but it leads to huge costs. Here, we propose a new solution to overcome the inherently trade-offs between imaging speed and image quality based on a neural network, a 3D progressive U-shaped enhancement network (3D-pU-net). In our approach, a hemispherical transducer array-based PACT system was used for the system configuration, and we could obtain accurate high-quality reference images with all elements of the array. Cluster sampling, which was used for input data, is not affected by imaging speed degradation, but the image quality is degraded. We demonstrated that the trained 3D-pU-net enhanced the image quality of cluster-sampled data of static whole-body imaging. Furthermore, the network also performed a wide range of applications such as dynamic observation of contrast agent kinetics. In this study, we showed that the 3D-pU-net could improve the anatomical contrast and spatial resolution by overcoming the limited-view effect. This proposed approach can help a variety of PACT applications in practical settings, allowing for the development of useful and economical imaging equipment.
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