Congratulations to Mardava Gubbi! His first-author paper entitled Theoretical Framework to Predict Generalized Contrast-to-Noise Ratios of Photoacoustic Images With Applications to Computer Vision was accepted for publication in IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control.
This work is the first to the first to present a novel framework to establish relationships among photoacoustic imaging system parameters, image quality, and computer vision-based task performance. Our framework leverages gCNR to quantify the relationships between system parameters (e.g., channel SNR, laser energy) and photoacoustic image quality. Within this framework, we present a theoretical derivation of gCNR predictions based on using the statistics of the target and background signal powers, then validate these predictions on simulated, experimental, and in vivo data. This framework was then leveraged to quantify the accuracy of a photoacoustic target segmentation algorithm as a function of gCNR and demonstrate the robustness of gCNR to thresholding, with possible extensions to other computer vision-based tasks (e.g., target tracking and image classification) and to improve the overall photoacoustic imaging system design process.
Citation: Gubbi MR, Gonzalez EA, Bell MAL,Theoretical Framework to Predict Generalized Contrast-to-Noise Ratios of Photoacoustic Images With Applications to Computer Vision,” IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 69(6):2098-2114, 2022 [pdf]