Non-invasive and low-artifact in vivo brain imaging by using a scanning acoustic-photoacoustic dual mode microscopy

Abstract

Photoacoustic imaging is a potential candidate for in-vivo brain imaging, whereas, its imaging performance could be degraded by inhomogeneous multi-layered media, consisted of scalp and skull. In this work, we propose a low-artifact photoacoustic microscopy (LAPAM) scheme, which combines conventional acoustic-resolution photoacoustic microscopy with scanning acoustic microscopy to suppress the reflection artifacts induced by multi-layers. Based on similar propagation characteristics of photoacoustic signals and ultrasonic echoes, the ultrasonic echoes can be employed as the filters to suppress the reflection artifacts to obtain low-artifact photoacoustic images. Phantom experiment is used to validate the effectiveness of this method. Furthermore, LAPAM is applied for in-vivo imaging mouse brain without removing the scalp and the skull. Experimental results show that the proposed method successfully achieves the low-artifact brain image, which demonstrates the practical applicability of LAPAM. This work might improve the photoacoustic imaging quality in many biomedical applications, which involve tissue with complex acoustic properties, such as brain imaging through scalp and skull.

Improving photoacoustic imaging in low signal-to-noise ratio by using spatial and polarity coherence

“…Finally, an in vivo experiment was performed to demonstrate the practicability of the proposed DMPCS method for photoacoustic microscopy. For acoustic-resolution photoacoustic microscopy (AR-PAM) [35] , [36] , its resolution is determined by the acoustic focus. To improve the image quality in the out-of-focus region, AR-PAM often employs a virtual-detector-based synthetic-aperture focusing technique (VD-SAFT) [31] , [37] .…”

“…In our experiment, the laser pulses were emitted with a pulse repetition rate of 10 kHz, a pulse width of 8 ns, and a wavelength of 532 nm. The laser beam was coupled into fibers and was fixed around the periphery of a focused US transducer (Olympus NDT, V319-SU-F) [36] . As shown in Fig.…”

Deep Learning Realizes Photoacoustic Imaging Artifact Removal

“…There are various methods to remove artifacts from photoacoustic images in current studies. Traditional methods such as singular value decomposition [20] and Gaussian filters [21] can only remove some artifacts caused by strong acoustic reflection [22]. Photoacoustically guided focused ultrasound (PAFUSion) technology uses an innovative method to eliminate reflection artifacts by simulating the photoacoustic(PA) field, but it needs to meet the requirements of ultrasound and photoacoustic image matching, which may reduce the frame rate and bring errors in the case of tissue movement [23].…”