This study delves into the largely uncharted domain of biases in photoacoustic imaging, spotlighting potential shortcut learning as a key issue in reliable machine learning. Our focus is on hardware variation biases. We identify device-specific traits that create detectable fingerprints in photoacoustic images, demonstrate machine learning's capability to use these discrepancies to determine the device that acquired the image, and highlight their potential impact on machine learning model predictions in downstream tasks, such as disease classification.
Peripheral artery disease (PAD) is widespread among the elderly population where narrowing arteries in lower limbs are causing a lack of perfusion. This work explores the benefit of volumetric photoacoustic imaging (v-PAI) over conventional 2D PAI for PAD diagnosis and monitoring. To this end, we leverage the recently proposed approach of Tattoo tomography, which generates a v-PAI representation from a set of 2D PAI slices. Preliminary results of the ongoing study indicate that v-PAI can increase the sensitivity of early-stage PAD detection. Conclusively our Tattoo approach has the potential to become a valuable tool in PAD diagnostics.
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