Estimation of blood oxygenation with learned spectral decoloring for quantitative photoacoustic imaging (LSD-qPAI)

TL;DR

This paper introduces LSD-qPAI, a novel method that directly estimates blood oxygenation from multispectral photoacoustic data, improving accuracy in realistic settings for functional tissue property recovery.

Contribution

LSD-qPAI is the first approach to compute blood oxygenation directly from pixel-wise initial pressure spectra, enhancing quantitative photoacoustic imaging accuracy.

Findings

Accurate sO2 estimates achieved in silico.

Plausible in vivo sO2 estimates demonstrated.

Improved robustness over traditional spectral unmixing methods.

Abstract

One of the main applications of photoacoustic (PA) imaging is the recovery of functional tissue properties, such as blood oxygenation (sO2). This is typically achieved by linear spectral unmixing of relevant chromophores from multispectral photoacoustic images. Despite the progress that has been made towards quantitative PA imaging (qPAI), most sO2 estimation methods yield poor results in realistic settings. In this work, we tackle the challenge by employing learned spectral decoloring for quantitative photoacoustic imaging (LSD-qPAI) to obtain quantitative estimates for blood oxygenation. LSD-qPAI computes sO2 directly from pixel-wise initial pressure spectra Sp0, which are vectors comprised of the initial pressure at the same spatial location over all recorded wavelengths. Initial results suggest that LSD-qPAI is able to obtain accurate sO2 estimates directly from multispectral photoacoustic measurements in silico and plausible estimates in vivo.