Improved quantification of photoacoustic biomarkers through residual-based noise masking

Spectroscopic photoacoustic (PA) imaging enables non-invasive, label-free estimation of in vivo biomarkers such as tissue oxygen saturation (SO₂). Quantitative accuracy is limited by low signal-to-noise ratio (SNR) in deeper regions from fluence attenuation and heterogeneous absorber concentration. In these regions, electronic noise can dominate PA spectra, yielding physiologically-plausible but spurious SO₂ estimates. Since biomarkers are reported as values averaged over a region-of-interest (ROI), including noise-dominated pixels introduces systematic bias and reduces sensitivity to physiological changes. We present a pixel-wise noise masking framework: at each pixel, a linear least-squares residual compares the measured PA spectrum and a reference noise spectrum, and residual statistics within a signal-free ROI define a masking threshold. Unlike denoising, this approach excludes unreliable pixels prior to ROI-based estimation without modifying measured signals. Phantom and in vivo experiments demonstrate improved sensitivity to SO₂ dynamics following noise masking, highlighting the need for robust noise exclusion in quantitative PA imaging.