A Deep Learning-Based Correction for Scanning Radius Errors in Circular-Scan Photoacoustic Tomography
Jie Yin, Yingjie Feng, Junjun He, Min Xie, Chao Tao
TL;DR
This paper introduces a deep learning method to correct scanning radius errors in circular-scan photoacoustic tomography, improving image quality and reducing artifacts.
Contribution
A novel deep learning framework, SD-ResNet, is proposed to correct image distortions caused by scanning radius errors in PAT.
Findings
SD-ResNet effectively recovers image quality across various scanning radius deviations in simulations.
Phantom experiments confirm the method reduces artifacts and restores correct source shapes.
The paired training dataset using k-Wave simulations enhances generalization and robustness.
Abstract
Circular-Scan photoacoustic tomography (PAT) can provide high-resolution images of optical absorption, but its analytical reconstructions, such as delay-and-sum (DAS), are highly sensitive to scanning radius (SR) inaccuracies, which cause severe geometric distortions and artifacts. In this work, we propose a deep learning framework, termed smooth deconvolution ResNet (SD-ResNet), to correct DAS reconstruction degradation induced by SR errors. SD-ResNet uses an ImageNet-pretrained ResNet-50 encoder and a lightweight deconvolutional decoder with additional smoothing convolutions to suppress checkerboard artifacts and restore fine structural details. A paired training dataset is generated using k-Wave simulations driven by human thoracic computed tomography (CT) slices: for each phantom, radiofrequency data are simulated once, and DAS images reconstructed with the true SR serve as ground…
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Taxonomy
TopicsPhotoacoustic and Ultrasonic Imaging · Spectroscopy and Laser Applications · Advanced X-ray and CT Imaging