Low-dose 4D cone-beam CT via joint spatiotemporal regularization of tensor framelet and nonlocal total variation.

Excessive radiation exposure is still a major concern in 4D cone-beam computed tomography (4D-CBCT) due to its prolonged scanning duration. Radiation dose can be effectively reduced by either under-sampling the x-ray projections or reducing the x-ray flux. However, 4D-CBCT reconstruction under such low-dose protocols is prone to image artifacts and noise. In this work, we propose a novel joint regularization-based iterative reconstruction method for low-dose 4D-CBCT. To tackle the under-sampling problem, we employ spatiotemporal tensor framelet (STF) regularization to take advantage of the spatiotemporal coherence of the patient anatomy in 4D images. To simultaneously suppress the image noise caused by photon starvation, we also incorporate spatiotemporal nonlocal total variation (SNTV) regularization to make use of the nonlocal self-recursiveness of anatomical structures in the spatial and temporal domains. Under the joint STF-SNTV regularization, the proposed iterative reconstruction approach is evaluated first using two digital phantoms and then using physical experiment data in the low-dose context of both under-sampled and noisy projections. Compared with existing approaches via either STF or SNTV regularization alone, the presented hybrid approach achieves improved image quality, and is particularly effective for the reconstruction of low-dose 4D-CBCT data that are not only sparse but noisy.

[1]  P. J. La Riviere.  Reduction of noise-induced streak artifacts in X-ray computed tomography through penalized-likelihood sinogram smoothing , 2003 .

[2]  Xuejun Gu,et al.  Simultaneous motion estimation and image reconstruction (SMEIR) for 4D cone-beam CT , 2013, 2013 IEEE Nuclear Science Symposium and Medical Imaging Conference (2013 NSS/MIC).

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

[4]  Zhiqiang Chen,et al.  A tensor PRISM algorithm for multi-energy CT reconstruction and comparative studies. , 2014, Journal of X-ray science and technology.

[5]  Steve B. Jiang,et al.  Real-time volumetric image reconstruction and 3D tumor localization based on a single x-ray projection image for lung cancer radiotherapy. , 2010, Medical physics.

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

[7]  Jing Wang,et al.  Statistical image reconstruction for low-dose CT using nonlocal means-based regularization , 2014, Comput. Medical Imaging Graph..

[8]  Steve B. Jiang,et al.  Low-dose 4DCT reconstruction via temporal nonlocal means. , 2010, Medical physics.

[9]  Pierrick Coupé,et al.  An Optimized Blockwise Nonlocal Means Denoising Filter for 3-D Magnetic Resonance Images , 2008, IEEE Transactions on Medical Imaging.

[10]  A. Bovik,et al.  A universal image quality index , 2002, IEEE Signal Processing Letters.

[11]  Jianhua Ma,et al.  Statistical image reconstruction for low-dose CT using nonlocal means-based regularization. Part II: An adaptive approach , 2015, Comput. Medical Imaging Graph..

[12]  J. Qi.  Statistical image reconstruction for lesion detection , 2004, Conference Record of the Thirty-Eighth Asilomar Conference on Signals, Systems and Computers, 2004..

[13]  Geoffrey D Hugo,et al.  Dosimetric advantages of four-dimensional adaptive image-guided radiotherapy for lung tumors using online cone-beam computed tomography. , 2008, International journal of radiation oncology, biology, physics.

[14]  Varun P. Gopi,et al.  MR image reconstruction based on framelets and nonlocal total variation using split Bregman method , 2013, International Journal of Computer Assisted Radiology and Surgery.

[15]  Hao Gao.  Fast parallel algorithms for the x-ray transform and its adjoint. , 2012, Medical physics.

[16]  Steve B. Jiang,et al.  Real-time volumetric image reconstruction and 3D tumor localization based on a single x-ray projection image for lung cancer radiotherapy. , 2010, Medical physics.

[17]  S. Osher,et al.  Multi-energy CT based on a prior rank, intensity and sparsity model (PRISM) , 2011, Inverse problems.

[18]  Steve B. Jiang,et al.  Low-dose 4DCT reconstruction via temporal nonlocal means. , 2010, Medical physics.

[19]  A. Boyer,et al.  Radiation dose reduction in four-dimensional computed tomography. , 2005, Medical physics.

[20]  L Xing,et al.  Motion correction for improved target localization with on-board cone-beam computed tomography , 2006, Physics in medicine and biology.

[21]  A. Akan,et al.  Iterative image reconstruction using non-local means with total variation from insufficient projection data. , 2016, Journal of X-ray science and technology.

[22]  Tom Goldstein,et al.  The Split Bregman Method for L1-Regularized Problems , 2009, SIAM J. Imaging Sci..

[23]  Jie Tang,et al.  Time-resolved cardiac interventional cone-beam CT reconstruction from fully truncated projections using the prior image constrained compressed sensing (PICCS) algorithm , 2012, Physics in medicine and biology.

[24]  Patrick J. La Rivière,et al.  Reduction of noise-induced streak artifacts in X-ray computed tomography through spline-based penalized-likelihood sinogram smoothing , 2005, IEEE Transactions on Medical Imaging.

[25]  Philip J. Withers,et al.  Temporal sparsity exploiting nonlocal regularization for 4D computed tomography reconstruction , 2016, Journal of X-ray science and technology.

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

[27]  David Thomas,et al.  5D respiratory motion model based image reconstruction algorithm for 4D cone-beam computed tomography , 2015 .

[28]  Xuanqin Mou,et al.  Tensor-based dictionary learning for dynamic tomographic reconstruction , 2015, Physics in medicine and biology.

[29]  Xiaoqun Zhang,et al.  TICMR: Total Image Constrained Material Reconstruction via Nonlocal Total Variation Regularization for Spectral CT , 2016, IEEE Transactions on Medical Imaging.

[30]  Adam S. Wang,et al.  Non-local total-variation (NLTV) minimization combined with reweighted L1-norm for compressed sensing CT reconstruction , 2016, Physics in medicine and biology.

[31]  Jean-Michel Morel,et al.  A Review of Image Denoising Algorithms, with a New One , 2005, Multiscale Model. Simul..

[32]  Daniel A Low,et al.  Megavoltage CT imaging quality improvement on TomoTherapy via tensor framelet. , 2013, Medical physics.

[33]  Stefan Sawall,et al.  Iterative 4D cardiac micro-CT image reconstruction using an adaptive spatio-temporal sparsity prior , 2012, Physics in medicine and biology.

[34]  Ruijiang Li,et al.  4DCT and 4D Cone-Beam CT Reconstruction Using Temporal Regularizations , 2015 .

[35]  D. Lalush,et al.  A realistic spline-based dynamic heart phantom , 1998, 1998 IEEE Nuclear Science Symposium Conference Record. 1998 IEEE Nuclear Science Symposium and Medical Imaging Conference (Cat. No.98CH36255).

[36]  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.