Spatiotemporal singular value decomposition for denoising in photoacoustic imaging with low-energy excitation light source

TL;DR

This paper introduces a spatiotemporal SVD denoising technique for photoacoustic imaging using low-energy light sources, significantly improving image quality and enabling real-time in vivo applications.

Contribution

The study develops and validates a novel spatiotemporal SVD-based denoising method tailored for low-fluence PA imaging systems, enhancing SNR while maintaining high temporal resolution.

Findings

Enhanced SNR of PA images by up to 1.9 times.

Achieved real-time processing at 50 microseconds per frame.

Demonstrated effectiveness on both simulated and in vivo data.

Abstract

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.