Dense Dilated UNet: Deep Learning for 3D Photoacoustic Tomography Image Reconstruction

Understand

In photoacoustic tomography (PAT), the acoustic pressure waves produced by optical excitation are measured by an array of detectors and used to reconstruct an image.

• Sparse spatial sampling and limited-view detection are two common challenges faced in PAT.
• Reconstructing from incomplete data using standard methods results in severe streaking artifacts and blurring.
• We propose a modified convolutional neural network (CNN) architecture termed Dense Dilation UNet (DD-UNet) for correcting artifacts in 3D PAT.