Feasibility of a Generative Adversarial Network for Artifact Removal in Experimental Photoacoustic Imaging

Photoacoustic tomography (PAT) reconstruction is an expeditiously growing interest among biomedical researchers because of its possible transition from laboratory to clinical pre-eminence. Nonetheless, the PAT inverse problem is yet to achieve an optimal solution in rapid and precise reconstruction under practical constraints. Precisely, the sparse sampling problem and random noise are the main impediments to attaining accuracy but in support of rapid PAT reconstruction. The limitations are associated with acquiring undersampled artifacts that deteriorate the optimality of the reconstruction task. Therefore, the former achievements of fast image formation limit the modality for clinical settings. Delving into the problem, here we explore a deep learning-based generative adversarial network (GAN) to improve the image quality by denoising and removing these artifacts. The specially designed attributes and unique manner of optimizing the problem, such as incorporating the data set limitations and providing stable training performance, constitute the main motivation behind the employment of GAN. Moreover, exploitation of the U-net variant as a generator network offers robust performance in terms of quality and computational cost, which is further validated with the detailed quantitative and qualitative analysis. The quantitatively evaluated structured similarity indexing method = 0.980 ± 0.043 and peak signal-to-noise ratio = 31 ± 0.002 dB state that the proposed solution provides the high-resolution image at the output, even training with a low-quality data set.

Limited-Angle CT Reconstruction with Generative Adversarial Network Sinogram Inpainting and Unsupervised Artifact Removal

High-quality limited-angle computed tomography (CT) reconstruction is in high demand in the medical field. Being unlimited by the pairing of sinogram and the reconstructed image, unsupervised methods have attracted wide attention from researchers. The reconstruction limit of the existing unsupervised reconstruction methods, however, is to use [0°, 120°] of projection data, and the quality of the reconstruction still has room for improvement. In this paper, we propose a limited-angle CT reconstruction generative adversarial network based on sinogram inpainting and unsupervised artifact removal to further reduce the angle range limit and to improve the image quality. We collected a large number of CT lung and head images and Radon transformed them into missing sinograms. Sinogram inpainting network is developed to complete missing sinograms, based on which the filtered back projection algorithm can output images with most artifacts removed; then, these images are mapped to artifact-free images by using artifact removal network. Finally, we generated reconstruction results sized 512×512 that are comparable to full-scan reconstruction using only [0°, 90°] of limited sinogram projection data. Compared with the current unsupervised methods, the proposed method can reconstruct images of higher quality.

Three-Dimensional Nondestructive Isotope-Selective Tomographic Imaging of 208Pb Distribution via Nuclear Resonance Fluorescence

Combining the nuclear resonance fluorescence (NRF) transmission method with computed tomography (CT) can be a novel method for imaging the isotope distributions, which is indispensable in nuclear engineering. We performed an experiment to reconstruct a three-dimensional NRF-CT image with isotope selectivity of enriched lead isotope rods (208Pb) together with a set of different rods, including another enriched isotope (206Pb), iron, and aluminum rods, inserted into a cylindrical aluminum holder. Using a laser Compton scattering (LCS) gamma ray beam with a 5.528 MeV maximum energy, 2 mm beam size, and 10 photon·s−1·eV−1 flux density, which is available at the BL1U beamline in the ultraviolet synchrotron orbital radiation-III (UVSOR-III) synchrotron radiation facility at the Institute of Molecular Science at the National Institutes of Natural Sciences in Japan, and we excited the Jπ = 1− NRF level at 5.512 MeV in 208Pb. An isotope-selective three-dimensional NRF-CT image of the 208Pb isotope distribution was experimentally obtained for the first time with a pixel resolution of 4 mm in the horizontal plane.

Correcting motion artifacts in coronary computed tomography angiography images using a dual-zone cycle generative adversarial network

BACKGROUND

Coronary computed tomography angiography (CCTA) is a noninvasive imaging modality to detect and diagnose coronary artery disease. Due to the limitations of equipment and the patient's physiological condition, some CCTA images collected by 64-slice spiral computed tomography (CT) have motion artifacts in the right coronary artery, left circumflex coronary artery and other positions.

OBJECTIVE

To perform coronary artery motion artifact correction on clinical CCTA images collected by Siemens 64-slice spiral CT and evaluate the artifact correction method.

METHODS

We propose a novel method based on the generative adversarial network (GAN) to correct artifacts of CCTA clinical images. We use CCTA clinical images collected by 64-slice spiral CT as the original dataset. Pairs of regions of interest (ROIs) cropped from original dataset or images with and without motion artifacts are used to train the dual-zone GAN. When predicting the CCTA images, the network inputs only the clinical images with motion artifacts.

RESULTS

Experiments show that this network effectively corrects CCTA motion artifacts. Regardless of ROIs or images, the peak signal to noise ratio (PSNR), structural similarity (SSIM), mean square error (MSE) and mean absolute error (MAE) of the generated images are greatly improved compared to those of the input data. In addition, based on scores from physicians, the average score for the coronary artery artifact correction of the output images is higher.

CONCLUSIONS

This study demonstrates that the dual-zone GAN has the excellent ability to correct motion artifacts in the coronary arteries and maintain the overall characteristics of CCTA clinical images.