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Abstract Photoacoustic (PA) imaging can map the physiological conditions of tissues and track the biodistribution of contrast agents. Ultrasound localization microscopy (ULM) with microbubbles provides deep-tissue super-resolution blood vessel images and blood velocity maps. The integration of these techniques offers a potential tool for oncological applications, such as visualizing tumor hypoxia and vasculature. Additionally, because PA imaging and ULM can share the same ultrasound detector and data-acquisition system, PA images can seamlessly fuse with ULM images without requiring post co-registration processing. However, the acquisition time of ULM is considerably longer (several minutes per frame) compared to PA imaging (10 milliseconds to 100 milliseconds per frame), limiting the simultaneous acquisition of the two imaging modalities. To address this issue, we developed a deep learning model based on U-Net to accelerate the frame rate of ULM using information from sparse microbubble locations and power Doppler images. By enabling fast ULM acquisition, we developed an interleaved PA/fast ULM imaging technique to simultaneously capture photoacoustic and ULM images. Our experiments demonstrated up to a 16 times improvement in acquisition speed with U-Net, utilizing in vivo mouse brain and skin tumor data. The hybrid imaging sequence achieved super-resolution vascular and tissue oxygenation imaging with less than 2 seconds of acquisition time per frame in vivo. The implementation of this rapid dual imaging scheme enables the longitudinal monitoring of 3D tumor microvasculature and hypoxia conditions using a common linear ultrasound transducer. We monitored tumor growth of epidermoid carcinoma (A431) in a mouse model from Day 7 to Day 17 of tumor implantation, and the results reveal a significant decrease in tumor vasculature density and oxygen saturation as the tumor grew larger over time. The results indicate a positive relationship between oxygen saturation and vessel density. Our findings showcase the potential of this innovative technique for advancing deep tissue, high-resolution, multi-biological parameters imaging in oncological research. Citation Format: Shensheng Zhao, Sayantani Basu, Roy H. Campbell, Yang Zhao, Yun-Sheng Chen. Hybrid photoacoustic imaging and fast ultrasound localization microscopy to probe the tumor microenvironment abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts) ; 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84 (6Suppl): Abstract nr 4164.
Zhao et al. (Fri,) studied this question.