Low-dose CT with deep learning regularization via proximal forward–backward splitting

Low dose X-ray computed tomography (LDCT) is desirable for reduced patient dose. This work develops image reconstruction methods with deep learning (DL) regularization for LDCT. Our methods are based on unrolling of proximal forward-backward splitting (PFBS) framework with data-driven image regularization via deep neural networks. In contrast with PFBS-IR that utilizes standard data fidelity updates via iterative reconstruction (IR) method, PFBS-AIR involves preconditioned data fidelity updates that fuse analytical reconstruction (AR) method and IR in a synergistic way, i.e., fused analytical and iterative reconstruction (AIR). The results suggest that DL-regularized methods (PFBS-IR and PFBSAIR) provided better reconstruction quality from conventional wisdoms (AR or IR). In addition, owing to AIR, PFBS-AIR noticeably outperformed PFBS-IR, and another DL-based postprocessing method FBPConvNet.

[1]  Jong Chul Ye,et al.  Deep Residual Learning for Compressed Sensing CT Reconstruction via Persistent Homology Analysis , 2016, ArXiv.

[2]  Jong Chul Ye,et al.  Multi-Scale Wavelet Domain Residual Learning for Limited-Angle CT Reconstruction , 2017, ArXiv.

[3]  Steve B. Jiang,et al.  GPU-based iterative cone-beam CT reconstruction using tight frame regularization , 2010, Physics in medicine and biology.

[4]  Steve B. Jiang,et al.  4D Computed Tomography Reconstruction from Few-Projection Data via Temporal Non-local Regularization , 2010, MICCAI.

[5]  Kilian Q. Weinberger,et al.  Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[6]  Hu Chen,et al.  Low-dose CT via convolutional neural network. , 2017, Biomedical optics express.

[7]  E. Sidky,et al.  Image reconstruction in circular cone-beam computed tomography by constrained, total-variation minimization , 2008, Physics in medicine and biology.

[8]  Luca Antiga,et al.  Automatic differentiation in PyTorch , 2017 .

[9]  Max A. Viergever,et al.  Generative Adversarial Networks for Noise Reduction in Low-Dose CT , 2017, IEEE Transactions on Medical Imaging.

[10]  Hongkai Zhao,et al.  Robust principal component analysis-based four-dimensional computed tomography , 2011, Physics in medicine and biology.

[11]  Morteza Mardani,et al.  Recurrent Generative Adversarial Networks for Proximal Learning and Automated Compressive Image Recovery , 2017, ArXiv.

[12]  Fang-Fang Yin,et al.  Principal component reconstruction (PCR) for cine CBCT with motion learning from 2D fluoroscopy , 2018, Medical physics.

[13]  Jian Sun,et al.  Deep ADMM-Net for Compressive Sensing MRI , 2016, NIPS.

[14]  Xuanqin Mou,et al.  Low-Dose CT Image Denoising Using a Generative Adversarial Network With Wasserstein Distance and Perceptual Loss , 2017, IEEE Transactions on Medical Imaging.

[15]  Michael Unser,et al.  CNN-Based Projected Gradient Descent for Consistent CT Image Reconstruction , 2017, IEEE Transactions on Medical Imaging.

[16]  Michael Unser,et al.  Deep Convolutional Neural Network for Inverse Problems in Imaging , 2016, IEEE Transactions on Image Processing.

[17]  Peng Bao,et al.  Convolutional Sparse Coding for Compressed Sensing CT Reconstruction , 2019, IEEE Transactions on Medical Imaging.

[18]  J. Froment,et al.  Total variation based Fourier reconstruction and regularization for computer tomography , 2005, IEEE Nuclear Science Symposium Conference Record, 2005.

[19]  Zhaoying Bian,et al.  Optimizing a Parameterized Plug-and-Play ADMM for Iterative Low-Dose CT Reconstruction , 2019, IEEE Transactions on Medical Imaging.

[20]  J. Moreau.  Fonctions convexes duales et points proximaux dans un espace hilbertien , 1962 .

[21]  Jeffrey A. Fessler,et al.  Statistical Image Reconstruction Using Mixed Poisson-Gaussian Noise Model for X-Ray CT , 2018, 1801.09533.

[22]  R. Glowinski,et al.  Augmented Lagrangian and Operator-Splitting Methods in Nonlinear Mechanics , 1987 .

[23]  James M. Rehg,et al.  Automated Macular Pathology Diagnosis in Retinal OCT Images Using Multi-Scale Spatial Pyramid with Local Binary Patterns , 2010, MICCAI.

[24]  Jie Tang,et al.  Prior image constrained compressed sensing (PICCS): a method to accurately reconstruct dynamic CT images from highly undersampled projection data sets. , 2008, Medical physics.

[25]  Klaus Mueller,et al.  Low-Dose CT Streak Artifacts Removal using Deep Residual Neural Network , 2017 .

[26]  Hao Gao,et al.  Fused analytical and iterative reconstruction (AIR) via modified proximal forward–backward splitting: a FDK-based iterative image reconstruction example for CBCT , 2015, Physics in medicine and biology.

[27]  Patrick L. Combettes,et al.  Signal Recovery by Proximal Forward-Backward Splitting , 2005, Multiscale Model. Simul..

[28]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[29]  Yi Zhang,et al.  AirNet: Fused Analytical and Iterative Reconstruction with Deep Neural Network Regularization for Sparse-Data CT. , 2020, Medical physics.

[30]  Hu Chen,et al.  LEARN: Learned Experts’ Assessment-Based Reconstruction Network for Sparse-Data CT , 2017, IEEE Transactions on Medical Imaging.

[31]  Steve B. Jiang,et al.  Cine Cone Beam CT Reconstruction Using Low-Rank Matrix Factorization: Algorithm and a Proof-of-Principle Study , 2012, IEEE Transactions on Medical Imaging.

[32]  Guang-Hong Chen,et al.  Synchronized multiartifact reduction with tomographic reconstruction (SMART-RECON): A statistical model based iterative image reconstruction method to eliminate limited-view artifacts and to mitigate the temporal-average artifacts in time-resolved CT. , 2015, Medical physics.

[33]  Jonas Adler,et al.  Learned Primal-Dual Reconstruction , 2017, IEEE Transactions on Medical Imaging.

[34]  Xavier Bresson,et al.  Bregmanized Nonlocal Regularization for Deconvolution and Sparse Reconstruction , 2010, SIAM J. Imaging Sci..

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

[36]  Yaoqin Xie,et al.  A Sparse-View CT Reconstruction Method Based on Combination of DenseNet and Deconvolution , 2018, IEEE Transactions on Medical Imaging.

[37]  Jong Chul Ye,et al.  A deep convolutional neural network using directional wavelets for low‐dose X‐ray CT reconstruction , 2016, Medical physics.

[38]  Dimitri P. Bertsekas,et al.  On the Douglas—Rachford splitting method and the proximal point algorithm for maximal monotone operators , 1992, Math. Program..

[39]  L. Xing,et al.  4D cone beam CT via spatiotemporal tensor framelet. , 2012, Medical physics.

[40]  Song Han,et al.  Deep Generative Adversarial Networks for Compressed Sensing Automates MRI , 2017, ArXiv.