HDN: Hybrid Deep-Learning and Non-Line-of-Sight Reconstruction Framework for Transcranial Photoacoustic Imaging of Human Brain

TL;DR

This paper introduces HDN, a novel hybrid deep-learning framework that improves transcranial photoacoustic brain imaging by correcting signal distortion and enhancing image reconstruction through a two-part process.

Contribution

The paper presents a new hybrid deep-learning and non-line-of-sight reconstruction framework for photoacoustic imaging, outperforming traditional and existing deep-learning methods.

Findings

HDN achieves higher structural similarity index (0.661) than traditional (0.157) and deep-learning (0.305) methods.

HDN effectively corrects distorted signals and reconstructs clearer brain images.

Test results demonstrate HDN's superior performance in simulated brain imaging scenarios.

Abstract

Photoacoustic imaging combines the high contrast of optical imaging with the deep penetration depth of ultrasonic imaging, showing great potential in cerebrovascular disease detection. However, the ultrasonic wave suffers strong attenuation and multi-scattering when it passes through the skull tissue, resulting in the distortion of the collected photoacoustic signal. In this paper, inspired by the principles of deep learning and non-line-of-sight imaging, we propose an image reconstruction framework named HDN (Hybrid Deep-learning and Non-line-of-sight), which consists of the signal extraction part and difference utilization part. The signal extraction part is used to correct the distorted signal and reconstruct an initial image. The difference utilization part is used to make further use of the signal difference between the distorted signal and corrected signal, reconstructing the residual image between the initial image and the target image. The test results on a photoacoustic digital brain simulation dataset show that compared with the traditional method (delay-and-sum) and deep-learning-based method (UNet), the HDN achieved superior performance in both signal correction and image reconstruction. Specifically for the structural similarity index, the HDN reached 0.661 in imaging results, compared to 0.157 for the delay-and-sum method and 0.305 for the deep-learning-based method.