Learning to Reconstruct Computed Tomography Images Directly From Sinogram Data Under A Variety of Data Acquisition Conditions

Computed tomography (CT) is widely used in medical diagnosis and non-destructive detection. Image reconstruction in CT aims to accurately recover pixel values from measured line integrals, i.e., the summed pixel values along straight lines. Provided that the acquired data satisfy the data sufficiency condition as well as other conditions regarding the view angle sampling interval and the severity of transverse data truncation, researchers have discovered many solutions to accurately reconstruct the image. However, if these conditions are violated, accurate image reconstruction from line integrals remains an intellectual challenge. In this paper, a deep learning method with a common network architecture, termed iCT-Net, was developed and trained to accurately reconstruct images for previously solved and unsolved CT reconstruction problems with high quantitative accuracy. Particularly, accurate reconstructions were achieved for the case when the sparse view reconstruction problem (i.e., compressed sensing problem) is entangled with the classical interior tomographic problems.

[1]  Jing Wang,et al.  Statistical Iterative CBCT Reconstruction Based on Neural Network , 2018, IEEE Transactions on Medical Imaging.

[2]  Hiroyuki Kudo,et al.  Image reconstruction from fan-beam projections on less than a short scan , 2002, Physics in medicine and biology.

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

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

[5]  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.

[6]  Jürgen Schmidhuber,et al.  Deep learning in neural networks: An overview , 2014, Neural Networks.

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

[8]  Jin Keun Seo,et al.  CT sinogram‐consistency learning for metal‐induced beam hardening correction , 2017, Medical physics.

[9]  Hongming Shan,et al.  3-D Convolutional Encoder-Decoder Network for Low-Dose CT via Transfer Learning From a 2-D Trained Network , 2018, IEEE Transactions on Medical Imaging.

[10]  R. Siddon.  Fast calculation of the exact radiological path for a three-dimensional CT array. , 1985, Medical physics.

[11]  Hengyong Yu,et al.  Compressed sensing based interior tomography , 2009, Physics in medicine and biology.

[12]  S. Huber,et al.  Learning phase transitions by confusion , 2016, Nature Physics.

[13]  Hengyong Yu,et al.  A General Local Reconstruction Approach Based on a Truncated Hilbert Transform , 2007, Int. J. Biomed. Imaging.

[14]  Tobias Würfl,et al.  Precision Learning: Reconstruction Filter Kernel Discretization , 2017 .

[15]  Jeffrey A. Fessler,et al.  Image Reconstruction is a New Frontier of Machine Learning , 2018, IEEE Transactions on Medical Imaging.

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

[17]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[18]  S. Leng,et al.  Fan-beam and cone-beam image reconstruction via filtering the backprojection image of differentiated projection data , 2004, Physics in medicine and biology.

[19]  Jianwei Chen,et al.  Deconvolution-Based Backproject-Filter (BPF) Computed Tomography Image Reconstruction Method Using Deep Learning Technique , 2018 .

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

[21]  Emmanuel J. Candès,et al.  Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information , 2004, IEEE Transactions on Information Theory.

[22]  Zoubin Ghahramani,et al.  Probabilistic machine learning and artificial intelligence , 2015, Nature.

[23]  Jin Liu,et al.  Artifact Removal using Improved GoogLeNet for Sparse-view CT Reconstruction , 2018, Scientific Reports.

[24]  Xiaochuan Pan,et al.  Image reconstruction in regions-of-interest from truncated projections in a reduced fan-beam scan , 2005, Physics in medicine and biology.

[25]  Ke Li,et al.  Reduced anatomical clutter in digital breast tomosynthesis with statistical iterative reconstruction. , 2018, Medical physics.

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

[27]  Jong Chul Ye,et al.  Cycle‐consistent adversarial denoising network for multiphase coronary CT angiography , 2018, Medical physics.

[28]  Jeffrey A. Fessler,et al.  PWLS-ULTRA: An Efficient Clustering and Learning-Based Approach for Low-Dose 3D CT Image Reconstruction , 2017, IEEE Transactions on Medical Imaging.

[29]  Hengyong Yu,et al.  Convolutional Neural Network Based Metal Artifact Reduction in X-Ray Computed Tomography , 2017, IEEE Transactions on Medical Imaging.

[30]  Demis Hassabis,et al.  Mastering the game of Go with deep neural networks and tree search , 2016, Nature.

[31]  Paul Babyn,et al.  Sharpness-Aware Low-Dose CT Denoising Using Conditional Generative Adversarial Network , 2017, Journal of Digital Imaging.

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

[33]  Charles A. Bouman,et al.  DEEP BACK PROJECTION FOR SPARSE-VIEW CT RECONSTRUCTION , 2018, 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP).

[34]  Rolf Clackdoyle,et al.  A large class of inversion formulae for the 2D Radon transform of functions of compact support , 2004 .

[35]  Yoshua Bengio.  Machines Who Learn. , 2016, Scientific American.

[36]  Mark A. Anastasio,et al.  Deep Learning-Guided Image Reconstruction from Incomplete Data , 2017, ArXiv.

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

[38]  J. Radon.  On the determination of functions from their integral values along certain manifolds , 1986, IEEE Transactions on Medical Imaging.

[39]  Martín Abadi,et al.  TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems , 2016, ArXiv.

[40]  Jongha Lee,et al.  Deep-Neural-Network-Based Sinogram Synthesis for Sparse-View CT Image Reconstruction , 2018, IEEE Transactions on Radiation and Plasma Medical Sciences.

[41]  Jong Chul Ye,et al.  Deep Learning Interior Tomography for Region-of-Interest Reconstruction , 2017, ArXiv.

[42]  Quanzheng Li,et al.  Iterative Low-Dose CT Reconstruction With Priors Trained by Artificial Neural Network , 2017, IEEE Transactions on Medical Imaging.

[43]  M. Glas,et al.  Principles of Computerized Tomographic Imaging , 2000 .

[44]  Guang-Hong Chen,et al.  A new framework of image reconstruction from fan beam projections. , 2003, Medical physics.

[45]  Jong Chul Ye,et al.  Deep Learning Reconstruction for 9-View Dual Energy CT Baggage Scanner , 2018, ArXiv.

[46]  Steve B. Jiang,et al.  Intelligent Parameter Tuning in Optimization-Based Iterative CT Reconstruction via Deep Reinforcement Learning , 2017, IEEE Transactions on Medical Imaging.

[47]  Andrea J. Liu,et al.  A structural approach to relaxation in glassy liquids , 2015, Nature Physics.

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

[49]  Rolf Clackdoyle,et al.  Tomographic Reconstruction in the 21st Century , 2010, IEEE Signal Processing Magazine.

[50]  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.

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

[52]  Junyan Rong,et al.  Low-Dose Lung CT Image Restoration Using Adaptive Prior Features From Full-Dose Training Database , 2017, IEEE Transactions on Medical Imaging.

[53]  Demis Hassabis,et al.  Mastering the game of Go without human knowledge , 2017, Nature.

[54]  F. Natterer.  The Mathematics of Computerized Tomography , 1986 .

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

[56]  Michael I. Jordan,et al.  Machine learning: Trends, perspectives, and prospects , 2015, Science.

[57]  T. Zhuang,et al.  New families of exact fan-beam and cone-beam image reconstruction formulae via filtering the backprojection image of differentiated projection data along singly measured lines , 2006 .

[58]  Shane Legg,et al.  Human-level control through deep reinforcement learning , 2015, Nature.

[59]  Mike Preuss,et al.  Planning chemical syntheses with deep neural networks and symbolic AI , 2017, Nature.

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

[61]  S. Helgason,et al.  Integral geometry, Radon transforms and complex analysis , 1998 .

[62]  F. Noo,et al.  A two-step Hilbert transform method for 2D image reconstruction. , 2004, Physics in medicine and biology.

[63]  Bruce R. Rosen,et al.  Image reconstruction by domain-transform manifold learning , 2017, Nature.

[64]  Guang-Hong Chen,et al.  Development and evaluation of an exact fan-beam reconstruction algorithm using an equal weighting scheme via locally compensated filtered backprojection (LCFBP). , 2006, Medical physics.