Photoacoustic tomography is an emerging technique in the field of biomedical imaging that combines the advantages of optics and acoustics. Unlike conventional imaging modalities, which are limited in their ability to provide diverse types of diagnostic information, PAT can offer structural, functional, and molecular biometric imaging using a single modality. This advantage can be utilized in that multiparametric information is required to analyze various aspects of tumor. In this study, we have developed a versatile PAT system for tumor imaging which can provide high-definition and high-speed images. Our system can identify tumor characteristics such as angiogenesis, pharmacokinetics, and physiological functions for both primary and metastatic tumors. During 14 days after tumor cell inoculations, we visualize the angiogenesis and increased tortuosity of blood vessels surrounding the tumor. Additionally, we quantify changes in the oxygen saturation within the entire tumor region, revealing the presence of hypoxia in the tumor core. These findings highlight the ability of our system to provide valuable functional information about tumor physiology. Moreover, our versatile PAT system allows us to observe the organ accumulation characteristics of different contrast agents. With its ability to generate high-quality structural, functional, and molecular photoacoustic images, our system holds promise for advancing the field of biomedical imaging.
Enhancing the monitoring of dynamic changes in organs is crucial for understanding biological processes and diseases. Current small-animal imaging techniques have limitations in contrast, sensitivity, and spatial/temporal resolution. We propose a rapid rotary-scanning photoacoustic computed tomography (PACT) approach that addresses these limitations. Using a rapid rotary-scanning technique with a hemispherical transducer array, we monitor dynamic change in mice. Leveraging the near-infrared spectral window, our method enables visualization of deep-seated structures across multiple planes in living mammalian organs. Our results demonstrate high image quality, rich spectroscopic contrast, and improved temporal resolution. PACT holds significant potential as a valuable tool for studying pharmacokinetics in preclinical research, offering insights into complex biological processes and facilitating the development of targeted therapeutics.
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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