3-D photoacoustic tomography reconstruction with deep learning for vasculature imaging

Abstract

Photoacoustic tomography (PAT) is a hybrid imaging modality capable of acquiring high contrast and resolution images of optical absorption at depths greater than traditional optical imaging techniques. Practical considerations with instrumentation limit the number of acoustic sensors and their “view” of the imaging target, which result in image reconstruction artifacts degrading image quality. Iterative reconstruction methods can be used to improve image quality but are computationally expensive, especially for 3-D PAT over large imaging volumes. Deep learning has emerged as an efficient alternative and is capable of achieving state-of-the-art performance. In this work, we compare the 3-D fully dense UNet convolutional neural network with the widely used time reversal reconstruction method. Simulated acoustic data was generated using the K-wave MATLAB toolbox (225 detectors, planar geometry). A public database containing 50 patient's whole-lung CT scans was used to create training (n = 40) and testing (n = 10) data. The training and testing datasets are comprised of 500 and 50 lung vasculature volumes sampled from their respective whole-lung CT scans. Using the structural similarity index, the proposed deep learning method (0.87 ± 0.04) is shown to be superior to time reversal (0.38 ± 0.11).