4 March 2021 Using deep learning for pixel-defect corrections in flat-panel radiography imaging
Eunae Lee, Eunyeong Hong, Dong Sik Kim
Author Affiliations +
Abstract

Purpose: Flat-panel radiography detectors employ thin-film transistor (TFT) panels to acquire high-quality x-ray images. Pixel defects occur due to circuit shorts or opens in the TFT panel. The defects may degrade the image quality, as well as lower the production yield, and eventually raise the production cost. Hence, it is important to develop an appropriate defect correction algorithm for acquired images. Traditional correction algorithms are based on a complicated adaptive filtering technique, which exploits neighbor pixels, to faithfully preserve the edge components. Because of the complexity of the traditional sophisticated approaches, optimizing their correction performances is difficult.

Approach: We considered various pixel-defect correction algorithms based on different deep learning models, such as the artificial neural network (ANN), convolutional neural network (CNN), concatenate CNN, and generative adversarial networks (GAN). We considered two cases of maximal defect sizes, 3  ×  3 and 5  ×  5  pixels, and conducted extensive learning experiments to find the best structures of the learning models using the mean square error (MSE) as the loss function.

Results: To conduct experiments, practical chest x-ray images were acquired from a general radiography detector. The MSE values of the correction results from ANN, CNN, concatenate CNN, and GAN were 69.40, 75.13, 68.21, and 73.77, respectively, and were much smaller than that of the conventional template match correction method.

Conclusions: A concatenate CNN showed the best defect-correction performance. However, ANN could achieve a similar correction performance with much smaller encoding complexity. Therefore, the single-layer ANN can efficiently conduct defect corrections in terms of both correction and complexity.

© 2021 Society of Photo-Optical Instrumentation Engineers (SPIE) 2329-4302/2021/$28.00 © 2021 SPIE
Eunae Lee, Eunyeong Hong, and Dong Sik Kim "Using deep learning for pixel-defect corrections in flat-panel radiography imaging," Journal of Medical Imaging 8(2), 023501 (4 March 2021). https://doi.org/10.1117/1.JMI.8.2.023501
Received: 31 March 2020; Accepted: 12 February 2021; Published: 4 March 2021
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CITATIONS
Cited by 4 patents.
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KEYWORDS
Radiography

Sensors

Gallium nitride

Algorithm development

Detection and tracking algorithms

Electrodes

Chest imaging

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