  Photoacoustic (PA) imaging is an emerging hybrid imaging modality that
combines rich optical spectroscopic contrast and high ultrasonic resolution and
thus holds tremendous promise for a wide range of pre-clinical and clinical
applications. Compact and affordable light sources such as light-emitting
diodes (LEDs) and laser diodes (LDs) are promising alternatives to bulky and
expensive solid-state laser systems that are commonly used as PA light sources.
These could accelerate the clinical translation of PA technology. However, PA
signals generated with these light sources are readily degraded by noise due to
the low optical fluence, leading to decreased signal-to-noise ratio (SNR) in PA
images. In this work, a spatiotemporal singular value decomposition (SVD) based
PA denoising method was investigated for these light sources that usually have
low fluence and high repetition rates. The proposed method leverages both
spatial and temporal correlations between radiofrequency (RF) data frames.
Validation was performed on simulations and in vivo PA data acquired from human
fingers (2D) and forearm (3D) using a LED-based system. Spatiotemporal SVD
greatly enhanced the PA signals of blood vessels corrupted by noise while
preserving a high temporal resolution to slow motions, improving the SNR of in
vivo PA images by 1.1, 0.7, and 1.9 times compared to single frame-based
wavelet denoising, averaging across 200 frames, and single frame without
denoising, respectively. The proposed method demonstrated a processing time of
around 50 \mus per frame with SVD acceleration and GPU. Thus, spatiotemporal
SVD is well suited to PA imaging systems with low-energy excitation light
sources for real-time in vivo applications.
