Breast Tomosynthesis Reconstruction Using Artificial Neural Networks with Deep Learning
Resumo
The Filtered Backprojection (FBP) algorithm for Computed Tomography (CT) reconstruction can be mapped entire in an Artificial Neural Network (ANN), with the backprojection (BP) operation simulated analytically in a layer and the Ram-Lak filter simulated as a convolutional layer. Thus, this work adapt the BP layer for DBT reconstruction, making possible the use of FBP simulated as a ANN to reconstruct DBT images. For evaluation, Structural Similarity Index Measure (SSIM) and Peak Signal-to-Noise Ratio (PSNR) metrics were calculated to measure the improvement of the images made by the ANN, regarding a dataset containing 100 virtual breast phantoms to perform the experiments. We shown that making the Ram-Lak layer trainable, the reconstructed image can be improved in terms of noise reduction. And, considering an additional post-filtering step performed by Denoising Convolutional Neural Network (DnCNN), it shown comparable and superior results than a stateof-the-art DBT reconstruction method, averaging 37.644 dB and 0.869 values of PSNR and SSIM, respectively. Finally, this study enables additional proposals of ANN with Deep Learning models for DBT reconstruction and denoising.Referências
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T. Würfl, F. C. Ghesu, V. Christlein, and A. Maier, “Deep learning computed tomography,” in International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, 2016, pp. 432–440.
T. Würfl, M. Hoffmann, V. Christlein, K. Breininger, Y. Huang, M. Unberath, and A. K. Maier, “Deep learning computed tomography: Learning projection-domain weights from image domain in limited angle problems,” IEEE transactions on medical imaging, vol. 37, no. 6, pp. 1454–1463, 2018.
D. L. Parker, “Optimal short scan convolution reconstruction for fan beam ct,” Medical physics, vol. 9, no. 2, pp. 254–257, 1982.
C. Shen, Y. Gonzalez, L. Chen, S. B. Jiang, and X. Jia, “Intelligent parameter tuning in optimization-based iterative ct reconstruction via deep reinforcement learning,” arXiv preprint arXiv:1711.00414, 2017.
B. Chen, K. Xiang, Z. Gong, J. Wang, and S. Tan, “Statistical iterative cbct reconstruction based on neural network,” IEEE transactions on medical imaging, vol. 37, no. 6, pp. 1511–1521, 2018.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 770–778.
M. D. Zeiler, G. W. Taylor, and R. Fergus, “Adaptive deconvolutional networks for mid and high level feature learning,” in Computer Vision (ICCV), 2011 IEEE International Conference on. IEEE, 2011, pp. 2018–2025.
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick, “Microsoft coco: Common objects in context,” in European conference on computer vision. Springer, 2014, pp. 740–755.
H. Chen, Y. Zhang, Y. Chen, J. Zhang, W. Zhang, H. Sun, Y. Lv, P. Liao, J. Zhou, and G. Wang, “Learn: Learned experts’ assessmentbased reconstruction network for sparse-data ct,” IEEE transactions on medical imaging, 2018.
S. Roth and M. J. Black, “Fields of experts,” International Journal of Computer Vision, vol. 82, no. 2, p. 205, 2009.
Q. Yang, P. Yan, Y. Zhang, H. Yu, Y. Shi, X. Mou, M. K. Kalra, Y. Zhang, L. Sun, and G. Wang, “Low-dose ct image denoising using a generative adversarial network with wasserstein distance and perceptual loss,” IEEE transactions on medical imaging, vol. 37, no. 6, pp. 1348– 1357, 2018.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” in Advances in neural information processing systems, 2014, pp. 2672– 2680.
A. C. Kak and M. Slaney, Principles of computerized tomographic imaging. IEEE press New York, 1988.
B. Z. Des Plantes, “Eine neue methode zur differenzierung in der roentgenographie (planigraphie),” Acta Radiologica, no. 2, pp. 182–192, 1932.
R. B. Vimieiro, L. R. Borges, and M. A. Vieira, “Open-source reconstruction toolbox for digital breast tomosynthesis,” in XXVI Brazilian Congress on Biomedical Engineering. Springer, 2019, pp. 349–354.
B. Barufaldi, D. Higginbotham, P. R. Bakic, and A. D. Maidment, “Openvct: a gpu-accelerated virtual clinical trial pipeline for mammography and digital breast tomosynthesis,” in Medical Imaging 2018: Physics of Medical Imaging, vol. 10573. International Society for Optics and Photonics, 2018, p. 1057358.
Z. Wang and A. C. Bovik, “Mean squared error: Love it or leave it? a new look at signal fidelity measures,” IEEE signal processing magazine, vol. 26, no. 1, pp. 98–117, 2009.
K. Zhang, W. Zuo, Y. Chen, D. Meng, and L. Zhang, “Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising,” IEEE Transactions on Image Processing, vol. 26, no. 7, pp. 3142–3155, 2017.
D. H. Salvadeo, R. B. Vimieiro, M. A. Vieira, and A. D. Maidment, “Bayesian reconstruction for digital breast tomosynthesis using a nonlocal gaussian markov random field a priori model,” in Medical Imaging 2019: Physics of Medical Imaging, vol. 10948. International Society for Optics and Photonics, 2019, p. 109485C.
M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard et al., “Tensorflow: a system for largescale machine learning.” in OSDI, vol. 16, 2016, pp. 265–283.
L. R. Borges, P. R. Bakic, A. Foi, A. D. Maidment, and M. A. Vieira, “Pipeline for effective denoising of digital mammography and digital breast tomosynthesis,” in Medical Imaging 2017: Physics of Medical Imaging, vol. 10132. International Society for Optics and Photonics, 2017, p. 1013206.
M. A. Vieira, H. C. de Oliveira, P. F. Nunes, L. R. Borges, P. R. Bakic, B. Barufaldi, R. J. Acciavatti, and A. D. Maidment, “Feasibility study of dose reduction in digital breast tomosynthesis using non-local denoising algorithms,” in Medical Imaging 2015: Physics of Medical Imaging, vol. 9412. International Society for Optics and Photonics, 2015, p. 94122C.
D. C. Scarparo, D. H. P. Salvadeo, D. C. G. Pedronette, B. Barufaldi, and A. D. Maidment, “Evaluation of denoising digital breast tomosynthesis data in both projection and image domains and a study of noise model on digital breast tomosynthesis image domain,” Journal of Medical Imaging, vol. 6, no. 3, p. 031410, 2019.
F. J. Anscombe, “The transformation of poisson, binomial and negativebinomial data,” Biometrika, vol. 35, no. 3/4, pp. 246–254, 1948.
Publicado
18/10/2021
Como Citar
PAULA, Davi Duarte de; SALVADRO, Denis H. P..
Breast Tomosynthesis Reconstruction Using Artificial Neural Networks with Deep Learning. In: WORKSHOP DE TESES E DISSERTAÇÕES - CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI), 34. , 2021, Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
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p. 105-111.
DOI: https://doi.org/10.5753/sibgrapi.est.2021.20021.
