Multi-scale residual denoising GAN model for producing super-resolution CTA images

Computed tomography angiography (CTA) is one of the salient radiological techniques in the virtualization and diagnosis of cerebral vascular diseases. However, there are various obstacles to the acquisition of highly legible CTA images, such as the lack of high-resolution CT scanners in community hospitals. And it is time-consuming for radiologists to perform CTA post-processing. These predicaments that medical institutions face make it necessary to automatically covert cerebrovascular images of low resolution to high-quality ones by means of artificial intelligence systems. In this paper, we propose a deep learning technique to improve the resolution of blurred CTA images. We develop MRDGAN, a novel generative adversarial network (GAN) model, to address the outstanding problems in CTA images such as high-frequency noise information (black pixels) and the scarcity of useful information (blood vessel pixels). We introduce spatial and channel attention into MRDGAN’s generator to facilitate feature extraction and incorporate a multi-scale residual block and a noise reduction block to retain micro vessels’ information and eliminate the noise in the generated images. Experiment results show that the CTA images generated by our model MRDGAN outperform the state-of-the-art models SRGAN and ESRGAN in terms of quality and quantity—MRDGAN obtains the highest score (35.89) in peak signal-to-noise ratio, showing a great potential as a low-cost solution of acquiring high-resolution CTA images.

[1]  Junko Ota,et al.  Application of Super-Resolution Convolutional Neural Network for Enhancing Image Resolution in Chest CT , 2018, Journal of Digital Imaging.

[2]  K. J. Ray Liu,et al.  Digital image source coder forensics via intrinsic fingerprints , 2009, IEEE Transactions on Information Forensics and Security.

[3]  Jitendra Malik,et al.  Region-Based Convolutional Networks for Accurate Object Detection and Segmentation , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Yoshua Bengio,et al.  Generative Adversarial Nets , 2014, NIPS.

[5]  Hayit Greenspan,et al.  Super-Resolution in Medical Imaging , 2009, Comput. J..

[6]  Guang Li,et al.  CT Super-Resolution GAN Constrained by the Identical, Residual, and Cycle Learning Ensemble (GAN-CIRCLE) , 2018, IEEE Transactions on Medical Imaging.

[7]  Yu Qiao,et al.  ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks , 2018, ECCV Workshops.

[8]  Sivaram Prasad Mudunuri,et al.  Low Resolution Face Recognition Across Variations in Pose and Illumination , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Kaiming He,et al.  Feature Pyramid Networks for Object Detection , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[10]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[11]  T. M. Lillesand,et al.  Remote Sensing and Image Interpretation , 1980 .

[12]  In-So Kweon,et al.  CBAM: Convolutional Block Attention Module , 2018, ECCV.

[13]  Thomas S. Huang,et al.  Image super-resolution as sparse representation of raw image patches , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[14]  Christian Ledig,et al.  Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[15]  Mert R. Sabuncu,et al.  Medical Image Imputation From Image Collections , 2018, IEEE Transactions on Medical Imaging.

[16]  Chi-Hieu Pham,et al.  Brain MRI super-resolution using deep 3D convolutional networks , 2017, 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017).

[17]  Luc Van Gool,et al.  Anchored Neighborhood Regression for Fast Example-Based Super-Resolution , 2013, 2013 IEEE International Conference on Computer Vision.

[18]  Rob Fergus,et al.  Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks , 2015, NIPS.

[19]  Xiaoou Tang,et al.  Image Super-Resolution Using Deep Convolutional Networks , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[20]  Yongqiang Zhang,et al.  SOD-MTGAN: Small Object Detection via Multi-Task Generative Adversarial Network , 2018, ECCV.

[21]  Mirabela Rusu,et al.  An Application of Generative Adversarial Networks for Super Resolution Medical Imaging , 2018, 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA).

[22]  Hongming Shan,et al.  Multi-Contrast Super-Resolution MRI Through a Progressive Network , 2019, IEEE Transactions on Medical Imaging.

[23]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[24]  Jean-Yves Tourneret,et al.  A Tensor Factorization Method for 3-D Super Resolution With Application to Dental CT , 2018, IEEE Transactions on Medical Imaging.

[25]  Junko Ota,et al.  Super-Resolution Imaging of Mammograms Based on the Super-Resolution Convolutional Neural Network , 2017 .