Estimation of blood oxygenation with learned spectral decoloring for quantitative photoacoustic imaging (LSD-qPAI)
This addresses the problem of poor sO2 estimation in quantitative photoacoustic imaging for medical applications, representing an incremental improvement over existing methods.
The paper tackled the challenge of accurately estimating blood oxygenation (sO2) in realistic photoacoustic imaging settings by introducing LSD-qPAI, which computes sO2 directly from pixel-wise initial pressure spectra, achieving accurate estimates in silico and plausible ones in vivo.
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.