4.7 Article

A Novel 2-D Synthetic Aperture Focusing Technique for Acoustic-Resolution Photoacoustic Microscopy

Journal

IEEE TRANSACTIONS ON MEDICAL IMAGING
Volume 38, Issue 1, Pages 250-260

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TMI.2018.2861400

Keywords

Image enhancement/restoration (noise and artifact reduction); microscopy; optoacoustic/photoacoustic imaging; vessels

Funding

  1. Ministry of Science and ICT (MSIT) [IITP-2018-2011-1-00783]
  2. Korea Health Industry Development Institute (KHIDI) - Ministry of Health Welfare [HI15C1817]
  3. NRF Pioneer Research Center Program of the MSIP (MSIT and Future Planning) [NRF-2014M3C1A3017229, NRF-2017M3C1A3037762]
  4. National Research Foundation of Korea (NRF) through the Korea Government (MSIP) [2011-0030075]

Ask authors/readers for more resources

Acoustic-resolution photoacoustic microscopy (AR-PAM) is a promising technology for vascular or tumor-targeted molecular imaging. Unique advantages of AR-PM are its non-invasive, non-ionizing real-time, and deeper imaging depth. AR-PAM typically uses an ultrasound transducer with a high acoustic numerical aperture (NA) to enable deeper imaging depth. While high NA achieves good lateral resolution in the focal plane but significantly degrades the lateral resolution in the out-of-focus region. Synthetic aperture focusing technique (SAFT) has been introduced to overcome this out-of-focus degradation by synthesizing the correlated signals. Several 2-D SAFTs have been also reported to improve degraded resolution in all directions. However, the resolution enhancement of the previously reported 2-D SAFTs are suboptimal and are not equivalent to the 1-D SAFT performance under an ideal condition with the sample orientation perpendicular to the synthetic aperture direction. In this paper, we present a new 2-D SAFT called 2-D directional SAFT that improves the lateral resolution significantly and we compare our results against 1-D SAFT under ideal condition. We applied this algorithm to phantom and in vivo images to show the improvement in image quality. We also implement this algorithm in a graphical processing unit to achieve high performance to show the practicality of implementing this new algorithm in a system.

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