Deep learning based image reconstruction algorithm for limited-angle translational computed tomography

As a low-end computed tomography (CT) system, translational CT (TCT) is in urgent demand in developing countries. Under some circumstances, in order to reduce the scan time, decrease the X-ray radiation or scan long objects, furthermore, to avoid the inconsistency of the detector for the large angle scanning, we use the limited-angle TCT scanning mode to scan an object within a limited angular range. However, this scanning mode introduces some additional noise and limited-angle artifacts that seriously degrade the imaging quality and affect the diagnosis accuracy. To reconstruct a high-quality image for the limited-angle TCT scanning mode, we develop a limited-angle TCT image reconstruction algorithm based on a U-net convolutional neural network (CNN). First, we use the SART method to the limited-angle TCT projection data, then we import the image reconstructed by SART method to a well-trained CNN which can suppress the artifacts and preserve the structures to obtain a better reconstructed image. Some simulation experiments are implemented to demonstrate the performance of the developed algorithm for the limited-angle TCT scanning mode. Compared with some state-of-the-art methods, the developed algorithm can effectively suppress the noise and the limited-angle artifacts while preserving the image structures.

[1]  Hengyong Yu,et al.  Analytic reconstruction approach for parallel translational computed tomography. , 2015, Journal of X-ray science and technology.

[2]  Zhiqiang Chen,et al.  Anisotropic total variation for limited-angle CT reconstruction , 2010, IEEE Nuclear Science Symposuim & Medical Imaging Conference.

[3]  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.

[4]  Kees Joost Batenburg,et al.  Fast Tomographic Reconstruction From Limited Data Using Artificial Neural Networks , 2013, IEEE Transactions on Image Processing.

[5]  Per-Erik Danielsson,et al.  Scanning of logs with linear cone-beam tomography , 2003 .

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

[7]  Andrea Vedaldi,et al.  MatConvNet: Convolutional Neural Networks for MATLAB , 2014, ACM Multimedia.

[8]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

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

[10]  Jeffrey A. Fessler,et al.  Alternating Dual Updates Algorithm for X-ray CT Reconstruction on the GPU , 2015, IEEE Transactions on Computational Imaging.

[11]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[12]  A. Kak,et al.  Simultaneous Algebraic Reconstruction Technique (SART): A Superior Implementation of the Art Algorithm , 1984, Ultrasonic imaging.

[13]  Ge Wang,et al.  A Perspective on Deep Imaging , 2016, IEEE Access.

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

[15]  Xin Jin,et al.  A limited-angle CT reconstruction method based on anisotropic TV minimization , 2013, Physics in medicine and biology.

[16]  G. Herman,et al.  Algebraic reconstruction techniques (ART) for three-dimensional electron microscopy and x-ray photography. , 1970, Journal of theoretical biology.

[17]  Jeffrey A. Fessler,et al.  Alternating Direction Method of Multiplier for Tomography With Nonlocal Regularizers , 2014, IEEE Transactions on Medical Imaging.

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

[19]  Li Zeng,et al.  Wavelet tight frame and prior image-based image reconstruction from limited-angle projection data , 2017 .

[20]  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.

[21]  Kyoung Mu Lee,et al.  Accurate Image Super-Resolution Using Very Deep Convolutional Networks , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[22]  Chengxiang Wang,et al.  Edge-preserving reconstruction from sparse projections of limited-angle computed tomography using ℓ0-regularized gradient prior. , 2017, The Review of scientific instruments.

[23]  Hengyong Yu,et al.  Top-level design and pilot analysis of low-end CT scanners based on linear scanning for developing countries. , 2013, Journal of X-ray science and technology.

[24]  Li Zhang,et al.  Direct filtered-backprojection-type reconstruction from a straight-line trajectory , 2007 .

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

[26]  Trevor Darrell,et al.  Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[27]  V. Fuchs,et al.  Physicians' views of the relative importance of thirty medical innovations. , 2001, Health affairs.

[28]  Qian Wang,et al.  Low-dose spectral CT reconstruction using L0 image gradient and tensor dictionary , 2017, ArXiv.

[29]  Michael Elad,et al.  Spatially-Adaptive Reconstruction in Computed Tomography Using Neural Networks , 2013, IEEE Transactions on Medical Imaging.

[30]  Stephen M. Moore,et al.  The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository , 2013, Journal of Digital Imaging.

[31]  Heye Zhang,et al.  Reweighted Anisotropic Total Variation Minimization for Limited-Angle CT Reconstruction , 2017, IEEE Transactions on Nuclear Science.

[32]  Archan Misra,et al.  Implementation and performance evaluation of TeleMIP , 2001, ICC 2001. IEEE International Conference on Communications. Conference Record (Cat. No.01CH37240).

[33]  Chengxiang Wang,et al.  Error bounds and stability in the $l_{0}$ regularized for CT reconstruction from small projections , 2016 .

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

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

[36]  Liu Fenglin,et al.  Filtered Back-Projection Image Reconstruction Algorithm for Opposite Parallel Linear CT Scanning , 2016 .

[37]  Isaac N. Bankman,et al.  Handbook of Medical Imaging. Processing and Analysis , 2002 .

[38]  Jie Tang,et al.  Prior image constrained compressed sensing: implementation and performance evaluation. , 2011, Medical physics.

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