Limited View and Sparse Photoacoustic Tomography for Neuroimaging with Deep Learning

TL;DR

This paper introduces PixelDL, a deep learning method that improves photoacoustic tomography image reconstruction from limited views, achieving real-time performance and reducing artifacts compared to traditional iterative methods.

Contribution

The paper presents a novel pixelwise deep learning approach that combines physics-guided interpolation with CNN-based reconstruction for photoacoustic imaging.

Findings

PixelDL achieves comparable quality to iterative methods.

PixelDL outperforms other CNN approaches in artifact correction.

Enables real-time photoacoustic image reconstruction.

Abstract

Photoacoustic tomography (PAT) is a nonionizing 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 and geometry limit the number of available acoustic sensors and their view of the imaging target, which result in significant image reconstruction artifacts degrading image quality. Iterative reconstruction methods can be used to reduce artifacts but are computationally expensive. In this work, we propose a novel deep learning approach termed pixelwise deep learning (PixelDL) that first employs pixelwise interpolation governed by the physics of photoacoustic wave propagation and then uses a convolution neural network to directly reconstruct an image. Simulated photoacoustic data from synthetic vasculature phantom and mouse-brain vasculature were used for training and testing, respectively. Results demonstrated that PixelDL achieved comparable performance to iterative methods and outperformed other CNN-based approaches for correcting artifacts. PixelDL is a computationally efficient approach that enables for realtime PAT rendering and for improved image quality, quantification, and interpretation.