Hybrid-Domain Neural Network Processing for Sparse-View CT Reconstruction

X-ray computed tomography (CT) is one of the most widely used tools in medical imaging, industrial nondestructive testing, lesion detection, and other applications. However, decreasing the projection number to lower the X-ray radiation dose usually leads to severe streak artifacts. To improve the quality of the images reconstructed from sparse-view projection data, we developed a hybrid-domain neural network (HDNet) processing for sparse-view CT (SVCT) reconstruction in this study. The HDNet decomposes the SVCT reconstruction problem into two stages and each stage focuses on one mission, which reduces the learning difficulty of the entire network. Experiments based on the simulated and clinical datasets are performed to demonstrate the performance of the proposed method. Compared with other competitive algorithms, quantitative and qualitative results show that the proposed method makes a great improvement on artifact suppression, tiny structure restoration, and contrast retention.

[1]  Bruno De Man,et al.  An outlook on x-ray CT research and development. , 2008, Medical physics.

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

[3]  Dong Liang,et al.  An improved statistical iterative algorithm for sparse-view and limited-angle CT image reconstruction , 2017, Scientific Reports.

[4]  Zhengrong Liang,et al.  Adaptive-weighted total variation minimization for sparse data toward low-dose x-ray computed tomography image reconstruction , 2012, Physics in medicine and biology.

[5]  Jong Chul Ye,et al.  Sparse-View Spectral CT Reconstruction Using Spectral Patch-Based Low-Rank Penalty , 2015, IEEE Transactions on Medical Imaging.

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

[7]  Mathias Unberath,et al.  Deep Learning Computed Tomography: Learning Projection-Domain Weights From Image Domain in Limited Angle Problems , 2018, IEEE Transactions on Medical Imaging.

[8]  Jiliu Zhou,et al.  Accurate Sparse-Projection Image Reconstruction via Nonlocal TV Regularization , 2014, TheScientificWorldJournal.

[9]  Jian Yang,et al.  Sparse-view X-ray CT reconstruction with Gamma regularization , 2017, Neurocomputing.

[10]  Christine Toumoulin,et al.  Dictionary learning based sinogram inpainting for CT sparse reconstruction , 2014 .

[11]  Qiu Wang,et al.  A low dose simulation tool for CT systems with energy integrating detectors. , 2013, Medical physics.

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

[13]  Weiwen Wu,et al.  Limited-Angle X-Ray CT Reconstruction Using Image Gradient ℓ₀-Norm With Dictionary Learning , 2021, IEEE Transactions on Radiation and Plasma Medical Sciences.

[14]  D. Brenner,et al.  Cancer risks from diagnostic radiology. , 2008, The British journal of radiology.

[15]  Huazhong Shu,et al.  Domain Progressive 3D Residual Convolution Network to Improve Low-Dose CT Imaging , 2019, IEEE Transactions on Medical Imaging.

[16]  Matthias Bertram,et al.  Directional interpolation of sparsely sampled cone-beam CT sinogram data , 2004, 2004 2nd IEEE International Symposium on Biomedical Imaging: Nano to Macro (IEEE Cat No. 04EX821).

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

[18]  Jan-Jakob Sonke,et al.  Directional sinogram interpolation for sparse angular acquisition in cone-beam computed tomography. , 2013, Journal of X-ray science and technology.

[19]  L. Shepp,et al.  Maximum Likelihood Reconstruction for Emission Tomography , 1983, IEEE Transactions on Medical Imaging.

[20]  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.

[21]  Won-Ki Jeong,et al.  Compressed Sensing MRI Reconstruction Using a Generative Adversarial Network With a Cyclic Loss , 2017, IEEE Transactions on Medical Imaging.

[22]  Yi Zhang,et al.  REDAEP: Robust and Enhanced Denoising Autoencoding Prior for Sparse-View CT Reconstruction , 2020, IEEE Transactions on Radiation and Plasma Medical Sciences.

[23]  Yuanjun Wang,et al.  A new adaptive-weighted total variation sparse-view computed tomography image reconstruction with local improved gradient information. , 2018, Journal of X-ray science and technology.

[24]  Rama Chellappa,et al.  DuDoNet: Dual Domain Network for CT Metal Artifact Reduction , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[25]  David Zhang,et al.  FSIM: A Feature Similarity Index for Image Quality Assessment , 2011, IEEE Transactions on Image Processing.

[26]  Matthias Bertram,et al.  Directional View Interpolation for Compensation of Sparse Angular Sampling in Cone-Beam CT , 2009, IEEE Transactions on Medical Imaging.

[27]  Thomas Brox,et al.  Striving for Simplicity: The All Convolutional Net , 2014, ICLR.

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

[29]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[30]  Hairong Zheng,et al.  ADAPTIVE-NET: deep computed tomography reconstruction network with analytical domain transformation knowledge. , 2020, Quantitative imaging in medicine and surgery.

[31]  Jianhua Ma,et al.  Nonlocal Prior Bayesian Tomographic Reconstruction , 2008, Journal of Mathematical Imaging and Vision.

[32]  Yoshua Bengio,et al.  Understanding the difficulty of training deep feedforward neural networks , 2010, AISTATS.

[33]  Linghong Zhou,et al.  Few-view CT reconstruction via a novel non-local means algorithm. , 2016, Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics.

[34]  Lei Zhang,et al.  Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising , 2016, IEEE Transactions on Image Processing.

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

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

[37]  Feng Lin,et al.  Low-Dose CT With a Residual Encoder-Decoder Convolutional Neural Network , 2017, IEEE Transactions on Medical Imaging.

[38]  Huazhong Shu,et al.  Artifact Suppressed Dictionary Learning for Low-Dose CT Image Processing , 2014, IEEE Transactions on Medical Imaging.

[39]  Soumith Chintala,et al.  Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks , 2015, ICLR.

[40]  Shipeng Xie,et al.  Artifact Removal in Sparse-Angle CT Based on Feature Fusion Residual Network , 2021, IEEE Transactions on Radiation and Plasma Medical Sciences.

[41]  Xiaochuan Pan,et al.  Evaluation of sparse-view reconstruction from flat-panel-detector cone-beam CT , 2010, Physics in medicine and biology.

[42]  Huazhong Shu,et al.  Median prior constrained TV algorithm for sparse view low-dose CT reconstruction , 2015, Comput. Biol. Medicine.

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

[44]  Thomas Flohr,et al.  Iterative reconstruction in image space (IRIS) and lesion detection in abdominal CT , 2010, Medical Imaging.

[45]  Jaejun Yoo,et al.  Deep Convolutional Framelet Denosing for Low-Dose CT via Wavelet Residual Network , 2018, IEEE Transactions on Medical Imaging.

[46]  Jianhua Ma,et al.  Total Variation-Stokes Strategy for Sparse-View X-ray CT Image Reconstruction , 2014, IEEE Transactions on Medical Imaging.

[47]  E. Sidky,et al.  Accurate image reconstruction from few-views and limited-angle data in divergent-beam CT , 2009, 0904.4495.

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

[49]  Hengyong Yu,et al.  Image-Domain Material Decomposition for Spectral CT Using a Generalized Dictionary Learning , 2021, IEEE Transactions on Radiation and Plasma Medical Sciences.

[50]  Jong Chul Ye,et al.  Framing U-Net via Deep Convolutional Framelets: Application to Sparse-View CT , 2017, IEEE Transactions on Medical Imaging.

[51]  T Humphries,et al.  Superiorized algorithm for reconstruction of CT images from sparse-view and limited-angle polyenergetic data , 2017, Physics in medicine and biology.

[52]  Yudong Zhang,et al.  Structure-Adaptive Fuzzy Estimation for Random-Valued Impulse Noise Suppression , 2018, IEEE Transactions on Circuits and Systems for Video Technology.

[53]  Yuxiang Xing,et al.  Improve angular resolution for sparse-view CT with residual convolutional neural network , 2018, Medical Imaging.

[54]  Ming Li,et al.  Smoothed l 0 Norm Regularization for Sparse-View X-Ray CT Reconstruction , 2016, BioMed research international.

[55]  Limin Luo,et al.  SISTER: Spectral-Image Similarity-Based Tensor With Enhanced-Sparsity Reconstruction for Sparse-View Multi-Energy CT , 2020, IEEE Transactions on Computational Imaging.

[56]  Qian Wang,et al.  Non-Local Low-Rank Cube-Based Tensor Factorization for Spectral CT Reconstruction , 2018, IEEE Transactions on Medical Imaging.

[57]  Jin Liu,et al.  3D Feature Constrained Reconstruction for Low-Dose CT Imaging , 2018, IEEE Transactions on Circuits and Systems for Video Technology.

[58]  Zhengrong Liang,et al.  Recent development of Low-dose X-ray Cone-beam computed tomography , 2010 .

[59]  Jong Chul Ye,et al.  Sparse-view X-ray spectral CT reconstruction using annihilating filter-based low rank hankel matrix approach , 2016, 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI).

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

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

[62]  Jin Liu,et al.  Discriminative Feature Representation to Improve Projection Data Inconsistency for Low Dose CT Imaging , 2017, IEEE Transactions on Medical Imaging.

[63]  Qian Wang,et al.  Low-dose spectral CT reconstruction using image gradient ℓ 0-norm and tensor dictionary. , 2018, Applied mathematical modelling.

[64]  L. Tanoue.  Computed Tomography — An Increasing Source of Radiation Exposure , 2009 .

[65]  Andrew L. Maas.  Rectifier Nonlinearities Improve Neural Network Acoustic Models , 2013 .

[66]  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.