A generative adversarial network for artifact removal in photoacoustic computed tomography with a linear-array transducer

Abstract

With balanced spatial resolution, penetration depth, and imaging speed, photoacoustic computed tomography (PACT) is promising for clinical translation such as in breast cancer screening, functional brain imaging, and surgical guidance. Typically using a linear ultrasound (US) transducer array, PACT has great flexibility for hand-held applications. However, the linear US transducer array has a limited detection angle range and frequency bandwidth, resulting in limited-view and limited-bandwidth artifacts in the reconstructed PACT images. These artifacts significantly reduce the imaging quality. To address these issues, existing solutions often have to pay the price of system complexity, cost, and/or imaging speed. Here, we propose a deep-learning-based method that explores the Wasserstein generative adversarial network with gradient penalty (WGAN-GP) to reduce the limited-view and limited-bandwidth artifacts in PACT. Compared with existing reconstruction and convolutional neural network approach, our model has shown improvement in imaging quality and resolution. Our results on simulation, phantom, and in vivo data have collectively demonstrated the feasibility of applying WGAN-GP to improve PACT’s image quality without any modification to the current imaging set-up.

Impact statement: This study has the following main impacts. It offers a promising solution for removing limited-view and limited-bandwidth artifact in PACT using a linear-array transducer and conventional image reconstruction, which have long hindered its clinical translation. Our solution shows unprecedented artifact removal ability for in vivo image, which may enable important applications such as imaging tumor angiogenesis and hypoxia. The study reports, for the first time, the use of an advanced deep-learning model based on stabilized generative adversarial network. Our results have demonstrated its superiority over other state-of-the-art deep-learning methods.

Keywords: Photoacoustic imaging; artifact removal; bioimaging; deep learning; generative adversarial network; photoacoustic computed tomography.

Figure 1.

WGAN-GP model architecture. In all layers, the first number is the number of filters. k and s denote the kernel size and stride, respectively. In up-sampling layer, size is the upsampling factor. (A color version of this figure is available in the online journal.) BN: batch normalization; ReLU: rectified linear unit.

Figure 2.

Training loss of UNet over iterations.

Figure 3.

Performance of WGAN-GP on simulated disk and TPM vascular images. Representative ground truth, time-reversal reconstruction, UNet and WGAN-GP results on (a–d) disk and (e–h) TPM vascular images. (i–l) Close-up images of the green boxes in (e–h) showing the vertical structures. The red boxes in (a–d) highlight the superior performance of DL models to recover the true disk shapes. The yellow arrows in (e–h) highlight the low-contrast structures that are reconstructed by WGAN-GP only. The dashed white arrows point out the vertical vessels recovered by the models. Scale bar: 5 mm. (A color version of this figure is available in the online journal.)

Figure 4.

Experimental performance of WGAN-GP on multiple-depth tube phantom. (a–c) Reconstructed images by time-reversal, UNet, and WGAN-GP, respectively. (d) Axial profiles along the stacked tubes, indicated by the dashed white line in (a). (e) Corresponding SNRs at each depth for different method. Scale bar: 5 mm. (A color version of this figure is available in the online journal.) PA: photoacoustic; SNR: signal-to-noise ratio; WGAN-GP: Wasserstein generative adversarial network with gradient penalty.

Figure 5.

Experimental performance of WGAN-GP on a hair phantom. (a–c) Results from time-reversal, UNet, and WGAN-GP, respectively. Red arrows denote the stripe artifacts due to limited view. (d and e) Lateral and axial profiles along the dashed red lines in (a). Scale bar: 5 mm. (A color version of this figure is available in the online journal.) PA: photoacoustic; WGAN-GP: Wasserstein generative adversarial network with gradient penalty.

Figure 6.

Experimental performance of WGAN-GP on in vivo trunk vascular images of a mouse. (a) System set-up for the experimental data. (b) Cross-sectional B-mode US image of the mouse trunk with the corresponded PACT images from (c) time-reversal, (d) UNet, and (e) WGAN-GP. (f and g) Close-up images of the region indicated by the white dashed boxes in UNet and WGAN-GP images, respectively. For CNR calculation, the green boxes denote the target, while the blue boxes indicate the background regions. The dashed white arrows highlight the vertical vessels recovered by the models. Scale bar: 7.5 mm. (A color version of this figure is available in the online journal.) CNR: contrast-to-noise ratio; PA: photoacoustic; US: ultrasound.