A Deep Learning Method for Limited-View Intravascular Photoacoustic Image Reconstruction

Abstract

Intravascular photoacoustic tomography (IVPAT) is a newly developed imaging modality in the interventional diagnosis and treatment of coronary artery diseases. Incomplete acoustic measurement caused by limitedview scanning of the detector in the vascular lumen results in under-sampling artifacts and distortion in the images reconstructed by using the standard reconstruction methods. A method for limited-view IVPAT image reconstruction based on deep learning is presented in this paper. A convolutional neural network (CNN) is constructed and trained with computer-simulated image data set. Then, the trained CNN is used to optimize the cross-sectional images of the vessel which are recovered from the incomplete photoacoustic measurements by using the standard time-reversal (TR) algorithm to obtain the images with the improved quality. Results of numerical demonstration indicate that the method can effectively reduce the image distortion and artifacts caused by the limited-view detection. Furthermore, it is superior to the compressed sensing (CS) method in recovering the unmeasured information of the imaging target with the structural similarity around 10% higher than CS reconstruction.