Deep learning technology boosts the speed of localization of photoacoustic imaging

After a lightning strike, thunder can be heard for a short period of time afterward. This is due to the fact that the surrounding material that was struck by light absorbs the light, and as a result of the conversion of this light into heat, the material expands and produces a sound. This imaging technique is known as photoacoustic imaging (PAI), which uses this phenomenon to take photographs of the inside of the body, is being explored as a new premier medical imaging device in various preclinical and clinical applications.

PAI technology has recently been using the ‘localization imaging’ method, which involves imaging the same area multiple times in order to achieve super high spatial resolution beyond physical limitation regardless of the imaging depth. However, this superior spatial resolution is achieved by sacrificing temporal resolution since multiple frames, each containing the localization target, must be superimposed to form a sufficiently sampled high-density super resolution image. This has made it challenging to employ for research that needs an immediate reaction.

In a new paper published Light Science & Application, a team of scientists led by Professor Chulhong Kim, Professor Seungchul Lee, Ph.D. candidate Jongbeom Kim, and MS Gyuwon Kim from the Department of Electrical Engineering, Convergence IT Engineering, and Mechanical Engineering, from Pohang University of Science and Technology (POSTECH), South Korea, and together with the team of Professor Lihong Wang and Postdoctoral Research Associate Lei. Li from the California Institute of Technology (Caltech), USA, has developed an AI-based localization PAI for solving the disadvantages of slow imaging speed. By using deep learning to boost imaging speed and reduce the amount of laser beams on the body, it has been able to address all three of these issues simultaneously: slow imaging speed, low spatial resolution, and burden on the body.

Using deep learning technology, the research team was able to reduce the number of images used in this method by more than 10 folds and increase the imaging speed by 12 folds. Localization of the imaging times photoacoustic microscopy and photoacoustic computed tomography were reduced from 30 seconds to 2.5 seconds and from 30 minutes to 2.5 minutes, respectively.

This advancement opens up a new possibility of localization of PAI techniques into various preclinical or clinical applications, which requires both high speed and fine spatial resolution, such as the immediateaneous drug and hemodynamic responses of studies. Above all, a major advantage of this technology is the fact that it significantly minimizes both laser beam exposure to the living body and the imaging time, which reduces the burden on patients.


Light Publishing Center, Changchun Institute of Optics, Fine Mechanics and Physics, CAS

Journal reference:

Kim, J., et al. (2022) Deep learning acceleration of multiscale superresolution localization photoacoustic imaging. Light: Science & ApplicationsGeneral Chat Chat Lounge

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