The confounding effects of skin colour in photoacoustic imaging
Else, T.; Loreno, C.; Groves, A.; Cox, B.; Gröhl, J.; Modolell, I.; Bohndiek, S.; Roshan, A.
Show abstract
Skin colour is known to confound readouts from optical devices that make measurements through the skin, which can adversely impact the care of patients with darker skin. Photoacoustic imaging (PAI) is making its way from the laboratory to the clinic, however, combining optics and ultrasound for deep tissue imaging leads to a complex relationship between photoacoustic-derived imaging biomarkers and skin melanin concentration. Furthermore, no generalisable correction of the confounding effects of skin colour in PAI has been demonstrated. We sought to overcome this limitation by recruiting a healthy volunteer cohort with the most diverse range of skin tones ever assembled in the field, with participants from Fitzpatrick types I to VI and with vitiligo. From this comprehensive dataset, we identified and characterised two physical mechanisms responsible for skin colour-dependent degradation in both image quality and biomarker quantification. Accompanied by detailed theoretical modelling, we demonstrated that strong light absorption by melanin leads to spectral colouring, which dominates in individuals with low skin melanin pigmentation. We further identified the backscattering of ultrasound waves generated in the skin as a major source of image artefacts for individuals with high skin melanin pigmentation. With this improved understanding of the physical basis, we were able to develop a fast and practicable correction method for spectral colouring and adapted a plane-wave ultrasound reconstruction algorithm to reveal the ultrasound scatterer distribution encoded in the photoacoustic timeseries. Our findings highlight the need for more advanced image reconstruction methods to enable equitable clinical application of PAI. One Sentence SummaryPhotoacoustic imaging is proven to suffer from measurement inaccuracies in people with darker skin, which could adversely impact patient care if not appropriately corrected.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Backscattering Amplitude in Ultrasound Localization Microscopy 93%
- MyoVision-US: an Artificial Intelligence-Powered Software for Automated Analysis of Skeletal Muscle Ultrasonography 92%
- Machine learning-enabled cancer diagnostics with widefield polarimetric second-harmonic generation microscopy 92%
Similar papers in this journal
- Ultrasound-guided Photoacoustic image Annotation Toolkit in MATLAB (PHANTOM) for preclinical applications 95%
- A Sparse Deep Learning Approach for Automatic Segmentation of Human Vasculature in Multispectral Optoacoustic Tomography 94%
- Quantification of vascular networks in photoacoustic mesoscopy 93%
Similar papers in this journal
- Photoacoustic imaging to monitor outcomes during hyperbaric oxygen therapy: Validation in a small cohort and case study in a bilateral chronic ischemic wound 94%
- A scalable, multi-wavelength, broad bandwidth frequency-domain near-infrared spectroscopy platform for real-time quantitative tissue optical imaging 94%
- A quasi-analytic solution for real-time multi-exposure speckle imaging of tissue perfusion 93%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.