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Updated: Mar 29, 2026

Dual Raster-Scanning Photoacoustic Small-Animal Imager for Vascular Visualization

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Published on: July 15, 2020

A Deep Learning-Based Correction for Scanning Radius Errors in Circular-Scan Photoacoustic Tomography.

Jie Yin¹, Yingjie Feng², Junjun He³

¹School of Electrical and Control Engineering, Nanjing Polytechnic Institute, Nanjing 210048, China.

Journal of Imaging

|March 27, 2026

Summary

This summary is machine-generated.

A new deep learning method, smooth deconvolution ResNet (SD-ResNet), corrects image distortions in photoacoustic tomography (PAT) caused by scanning radius errors. This approach significantly improves image quality and artifact reduction in PAT imaging.

Keywords:

ResNet artifact correction deep learning photoacoustic tomography scanning radius error

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Area of Science:

Medical Imaging
Biomedical Engineering
Artificial Intelligence

Background:

Circular-Scan photoacoustic tomography (PAT) offers high-resolution optical absorption imaging.
Analytical reconstructions like delay-and-sum (DAS) are sensitive to scanning radius (SR) inaccuracies, leading to geometric distortions and artifacts.

Purpose of the Study:

To develop a deep learning framework to correct DAS reconstruction degradation caused by SR errors in PAT.
To enhance the robustness and accuracy of PAT imaging under SR uncertainties.

Main Methods:

Proposed a smooth deconvolution ResNet (SD-ResNet) framework utilizing an ImageNet-pretrained ResNet-50 encoder and a deconvolutional decoder.
Generated a paired training dataset using k-Wave simulations with varying SR values based on human thoracic CT slices.
Incorporated smoothing convolutions to suppress checkerboard artifacts and restore fine structural details.

Main Results:

SD-ResNet effectively recovered image quality across a range of SR deviations in silico.
Phantom experiments demonstrated substantial artifact reduction and recovery of correct source shapes under practical SR mismatches.
The method proved robust in addressing SR-related artifacts in PAT.

Conclusions:

SD-ResNet offers a powerful and robust solution for SR-error-resilient PAT imaging.
The deep learning approach significantly enhances the reliability and diagnostic value of PAT reconstructions.
This work provides a valuable tool for improving the clinical applicability of photoacoustic tomography.