Ti Bai; Dan Nguyen; Biling Wang; Steve Jiang

doi:https://doi.org/10.2991/jaims.d.210428.001

Authors

Ti Bai Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA https://orcid.org/0000-0002-6697-7434
Dan Nguyen Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA https://orcid.org/0000-0002-9590-0655
Biling Wang Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
Steve Jiang Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA

DOI:

https://doi.org/10.2991/jaims.d.210428.001

Keywords:

Low-dose CT, Deep learning, Denoise

Abstract

Low-dose computed tomography (LDCT) is clinically desirable because it reduces the radiation dose to patients. However, the quality of LDCT images is often suboptimal because of the inevitable strong quantum noise. Because of their unprecedented success in computer vision, deep learning (DL)-based techniques have been used for LDCT denoising. Despite DL models' promising ability to remove noise, researchers have observed that the resolution of DL-denoised images is compromised, which decreases their clinical value. To mitigate this problem, in this work, we developed a more effective denoiser by introducing a high-resolution network (HRNet). HRNet consists of multiple branches of subnetworks that extract multiscale features that are fused together later, which substantially enhances the quality of the generated features and improves denoising performance. Experimental results demonstrated that the introduced HRNet-based denoiser outperformed the benchmarked U-Net–based denoiser, as it provided superior image resolution preservation and comparable, if not better, noise suppression. Quantitative evaluation in terms of root-mean-squared errors (RMSEs)/structure similarity index (SSIM) showed that the HRNet-based denoiser improve these values from 113.80/0.550 (LDCT) to 55.24/0.745 (HRNet), which outperformed the 59.87/0.712 for the U-Net–based denoiser.

References

J. Wang, et al., Sinogram noise reduction for low-dose CT by statistics-based nonlinear filters, in Medical Imaging 2005: Image Processing, International Society for Optics and Photonics, San Diego, CA, USA, 2005.

A. Manduca, et al., Projection space denoising with bilateral filtering and CT noise modeling for dose reduction in CT, Med. Phys. 36 (2009), 4911–4919.

K. Dabov, et al., Image denoising with block-matching and 3D filtering, in Image Processing: Algorithms and Systems, Neural Networks, and Machine Learning, International Society for Optics and Photonics, San Jose, CA, USA, 2006.

K. Dabov, et al., Image denoising by sparse 3-D transform-domain collaborative filtering, IEEE Trans. Image Process. 16 (2007), 2080–2095.

T. Bai, et al., Z-index parameterization for volumetric CT image reconstruction via 3-D dictionary learning, IEEE Trans. Med. Imaging. 36 (2017), 2466–2478.

H. Yan, et al., Towards the clinical implementation of iterative low-dose cone-beam CT reconstruction in image-guided radiation therapy: cone/ring artifact correction and multiple GPU implementation, Med. Phys. 41 (2014), 111912.

I.A. Elbakri, J.A. Fessler, Statistical image reconstruction for polyenergetic X-ray computed tomography, IEEE Trans. Med. Imaging. 21 (2002), 89–99.

J. Wang, et al., Penalized weighted least-squares approach to sinogram noise reduction and image reconstruction for low-dose Xray computed tomography, IEEE Trans. Med. Imaging. 25 (2006), 1272–1283.

G.H. Chen, J. Tang, S. Leng, Prior Image Constrained Compressed Sensing (PICCS): a method to accurately reconstruct dynamic CT images from highly undersampled projection data sets, Med. Phys. 35 (2008), 660–663.

E.Y. Sidky, X. Pan, Image reconstruction in circular cone-beam computed tomography by constrained, total-variation minimization, Phys. Med. Biol. 53 (2008), 4777.

J. Wang, T. Li, L. Xing, Iterative image reconstruction for CBCT using edge-preserving prior, Med. Phys. 36 (2009), 252–260.

Q. Xu, et al., Low-dose X-ray CT reconstruction via dictionary learning, IEEE Trans. Med. Imaging. 31 (2012), 1682–1697.

Y. Lecun, Y. Bengio, G. Hinton, Deep learning, Nature. 521 (2015), 436–444.

J. Caballero, et al., Real-time video super-resolution with spatiotemporal networks and motion compensation, in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, 2016, pp. 2848–2857.

A. Kappeler, et al., Video super-resolution with convolutional neural networks, IEEE Trans. Comput. Imaging. 2 (2016), 109–122.

X.J. Mao, C. Shen, Y.B. Yang, Image restoration using very deep convolutional encoder-decoder networks with symmetric skip connections, in Advances in Neural Information Processing Systems 29 (NIPS 2016), Barcelona, Spain, 2016.

D. Ulyanov, A. Vedaldi, V. Lempitsky, Deep Image Prior, In Proceedings of the IEEE conference on computer vision and pattern recognition, (2018), pp. 9446–9454.

J. Lehtinen, et al., Noise2noise: learning image restoration without clean data, In ICML., Stockholm, Sweden, 2018. arXiv preprint arXiv:1803.04189, 2018.

A. Krizhevsky, I. Sutskever, G.E. Hinton, Imagenet classification with deep convolutional neural networks, in Advances in Neural Information Processing Systems, Lake Tahoe, Nevada, 2012, pp. 1097–1105.

K. Simonyan, A. Zisserman, Very deep convolutional networks for large-scale image recognition, (2014). arXiv preprint arXiv:1409.1556.

R. Girshick, Fast R-CNN, in 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile, 2015.

K. He, et al., Deep residual learning for image recognition, in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 2015.

K. He, et al., Mask R-CNN, IEEE Trans. Pattern Anal. Mach. Intell. 42 (2020), 386–397.

H. Chen, et al., Low-dose CT with a residual encoder-decoder convolutional neural network, IEEE Trans. Med. Imaging. 36 (2017), 2524–2535.

H. Chen, et al., Low-dose CT via convolutional neural network, Biomed. Optics Express. 8 (2017), 679–694.

H. Shan, et al., 3-D convolutional encoder-decoder network for low-dose CT via transfer learning from a 2-D trained network, IEEE Trans. Med. Imaging. 37 (2018), 1522–1534.

Q. Yang, et al., Low-dose CT image denoising using a generative adversarial network with wasserstein distance and perceptual loss, IEEE Trans. Med. Imaging. 37 (2018), 1348–1357.

C. You, et al., Structurally-sensitive multi-scale deep neural network for low-dose CT denoising, IEEE Access. 6 (2018), 41839–41855.

H. Shan, et al., Competitive performance of a modularized deep neural network compared to commercial algorithms for low-dose CT image reconstruction, Nat. Mach. Intell. 1 (2019), 269–269.

T. Bai, et al., Probabilistic self-learning framework for low-dose CT denoising, arXiv:2006.00327, 2020.

G. Wang, S. Li, Low-dose CT image denoising using parallel-clone networks, arXiv:2005.06724v1, 2020.

K. Simonyan, A. Zisserman, Very deep convolutional networks for large-scale image recognition, arXiv:1409.1556, 2014.

C. Szegedy, et al., Inception-v4, inception-ResNet and the impact of residual connections on learning, In Proceedings of the AAAI Conference on Artificial Intelligence, San Francisco, California, USA, 2017.

T.-Y. Lin, et al., Feature pyramid networks for object detection, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, 2016.

J. Hu, L. Shen, G. Sun, Squeeze-and-excitation networks, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 2018.

G. Huang, et al., Densely connected convolutional networks, in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, 2016.

S. Xie, et al., Aggregated residual transformations for deep neural networks, in IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA, 2017.

O. Ronneberger, P. Fischer, T. Brox, U-Net: convolutional networks for biomedical image segmentation, in International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany, 2015.

K. Sun, et al., Deep high-resolution representation learning for human pose estimation, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA, 2019.

J. Wang, et al., Deep high-resolution representation learning for visual recognition, IEEE Trans. Pattern Anal. Mach. Intell. (2020).

D. Ulyanov, A. Vedaldi, V. Lempitsky, Instance normalization: the missing ingredient for fast stylization, arXiv preprint arXiv:1607.08022, 2016.

B. Chen, et al., Development and validation of an open data format for CT projection data, Med. Phys. 42 (2015), 6964–6972.

D.P. Kingma, J. Ba, Adam: a method for stochastic optimization, (2014). arXiv preprint arXiv:1412.6980.

W. Zhou, et al., Image quality assessment: from error visibility to structural similarity, IEEE Trans. Image Process. 13 (2004), 600–612.

Deep High-Resolution Network for Low-Dose X-Ray CT Denoising

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