Ultrasound-enhanced Unet model for quantitative photoacoustic tomography of ovarian lesions
Yun Zou, Eghbal Amidi, Hongbo Luo, Quing Zhu
Abstract
. Here, we propose a novel ultrasound-enhanced Unet model (US-Unet) that reconstructs optical absorption distribution from PAT data. A pre-trained ResNet-18 extracts the US features typically identified as morphologies of suspicious ovarian lesions, and a Unet is implemented to reconstruct optical absorption coefficient maps, using the initial pressure and US features extracted by ResNet-18. To test this US-Unet model, we calculated the blood oxygenation saturation values and total hemoglobin concentrations from 655 regions of interest (ROIs) (421 benign, 200 malignant, and 34 borderline ROIs) obtained from clinical images of 35 patients with ovarian/adnexal lesions. A logistic regression model was used to compute the ROC, the area under the ROC curve (AUC) was 0.94, and the accuracy was 0.89. To the best of our knowledge, this is the first study to reconstruct quantitative PAT with PA signals and US-based structural features.