Acceleration of X-ray computed tomography scanning with high-quality reconstructed volume by deblurring transmission images using convolutional neural networks

Abstract

X-ray computed tomography (CT) enables nondestructive evaluation and dimensional metrology using the reconstructed volumes of measured objects. Although the abundant applications of industrial X-ray CT exist, a high-quality reconstructed volume requires a long measurement time owing to sharp transmission images from dense views. Intensive X-rays with the exposure kept can be used to reduce the measurement time. However, the quality of the measured objects inevitably deteriorates as a result of the so-called penumbra effect caused by large focal spots in X-ray sources. This paper proposes rotational fine-tuning (RFT) for the acceleration of CT scanning with the quality kept. First, sharp transmission images from sparse views are obtained in appropriate imaging conditions. Subsequently, blurry transmission images from dense views are obtained using a larger focal spot size and a shorter exposure time. The acquired blurry images are deblurred by convolutional neural networks fine-tuned using several pairs of sharp and blurry images obtained at the corresponding projection angles, and linear interpolation integrates the deblurred images to generate the final RFT output. The proposed method indirectly reduces the measurement time because no lengthy acquisition time is required, and fine-tuning and blurring are rapid when using GPUs. In the experiments, the comparison of PSNRs between blurry and deblurred reconstructed volumes is shown. To reveal the effect of the proposed method on dimensional metrology, the improvement in the surface deviations of the stepped cylinder is also shown. Finally, the improvement of the pore spaces in a porous aluminum is shown to assess the aspect of nondestructive evaluation. Overall, experimental results demonstrate that the proposed method can perform the acceleration of X-ray CT scanning with the quality kept.