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

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.

5 pages

References in corpus (7)

• Eigenspectra optoacoustic tomography achieves quantitative blood oxygenation imaging deep in tissues
• Deep learning for biomedical photoacoustic imaging: A review
• Scalable and massively parallel Monte Carlo photon transport simulations for heterogeneous computing platforms
• Context encoding enables machine learning-based quantitative photoacoustics
• Development of a blood oxygenation phantom for photoacoustic tomography combined with online pO2 detection and flow spectrometry
• O-Net: A Convolutional Neural Network for Quantitative Photoacoustic Image Segmentation and Oximetry
• Photoacoustic monitoring of blood oxygenation during neurosurgical interventions

Cited by in corpus (6)

• Video-rate multispectral imaging in laparoscopic surgery: First-in-human application
• Semantic segmentation of multispectral photoacoustic images using deep learning
• A study of why we need to reassess full reference image quality assessment with medical images
• Photoacoustic image synthesis with generative adversarial networks
• Machine learning-based analysis of hyperspectral images for automated sepsis diagnosis
• Application-driven Validation of Posteriors in Inverse Problems