Four-dimensional cone beam CT reconstruction and enhancement using a temporal nonlocal means method.

PURPOSE Four-dimensional cone beam computed tomography (4D-CBCT) has been developed to provide respiratory phase-resolved volumetric imaging in image guided radiation therapy. Conventionally, it is reconstructed by first sorting the x-ray projections into multiple respiratory phase bins according to a breathing signal extracted either from the projection images or some external surrogates, and then reconstructing a 3D CBCT image in each phase bin independently using FDK algorithm. This method requires adequate number of projections for each phase, which can be achieved using a low gantry rotation or multiple gantry rotations. Inadequate number of projections in each phase bin results in low quality 4D-CBCT images with obvious streaking artifacts. 4D-CBCT images at different breathing phases share a lot of redundant information, because they represent the same anatomy captured at slightly different temporal points. Taking this redundancy along the temporal dimension into account can in principle facilitate the reconstruction in the situation of inadequate number of projection images. In this work, the authors propose two novel 4D-CBCT algorithms: an iterative reconstruction algorithm and an enhancement algorithm, utilizing a temporal nonlocal means (TNLM) method. METHODS The authors define a TNLM energy term for a given set of 4D-CBCT images. Minimization of this term favors those 4D-CBCT images such that any anatomical features at one spatial point at one phase can be found in a nearby spatial point at neighboring phases. 4D-CBCT reconstruction is achieved by minimizing a total energy containing a data fidelity term and the TNLM energy term. As for the image enhancement, 4D-CBCT images generated by the FDK algorithm are enhanced by minimizing the TNLM function while keeping the enhanced images close to the FDK results. A forward-backward splitting algorithm and a Gauss-Jacobi iteration method are employed to solve the problems. The algorithms implementation on GPU is designed to avoid redundant and uncoalesced memory access, in order to ensure a high computational efficiency. Our algorithms have been tested on a digital NURBS-based cardiac-torso phantom and a clinical patient case. RESULTS The reconstruction algorithm and the enhancement algorithm generate visually similar 4D-CBCT images, both better than the FDK results. Quantitative evaluations indicate that, compared with the FDK results, our reconstruction method improves contrast-to-noise-ratio (CNR) by a factor of 2.56-3.13 and our enhancement method increases the CNR by 2.75-3.33 times. The enhancement method also removes over 80% of the streak artifacts from the FDK results. The total computation time is 509-683 s for the reconstruction algorithm and 524-540 s for the enhancement algorithm on an NVIDIA Tesla C1060 GPU card. CONCLUSIONS By innovatively taking the temporal redundancy among 4D-CBCT images into consideration, the proposed algorithms can produce high quality 4D-CBCT images with much less streak artifacts than the FDK results, in the situation of inadequate number of projections.

[1]  Nicole M Wink,et al.  Respiratory correlated cone-beam computed tomography on an isocentric C-arm , 2005, Physics in medicine and biology.

[2]  Junyi Xia,et al.  High performance computing for deformable image registration: Towards a new paradigm in adaptive radiotherapy. , 2008, Medical physics.

[3]  Xun Jia,et al.  GPU-based fast Monte Carlo simulation for radiotherapy dose calculation. , 2011, Physics in medicine and biology.

[4]  Jie Tian,et al.  Fast cone-beam CT image reconstruction using GPU hardware , 2008 .

[5]  Steve B. Jiang,et al.  GPU-based ultra-fast direct aperture optimization for online adaptive radiation therapy , 2010, Physics in medicine and biology.

[6]  Guang-Hong Chen,et al.  Streaking artifacts reduction in four-dimensional cone-beam computed tomography. , 2008, Medical physics.

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

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

[9]  Xun Jia,et al.  A GPU-based finite-size pencil beam algorithm with 3D-density correction for radiotherapy dose calculation. , 2011, Physics in medicine and biology.

[10]  Benoît Ozell,et al.  Fast convolution-superposition dose calculation on graphics hardware. , 2009, Medical physics.

[11]  Yin Zhang,et al.  Fixed-Point Continuation for l1-Minimization: Methodology and Convergence , 2008, SIAM J. Optim..

[12]  Stanley Osher,et al.  Image Recovery via Nonlocal Operators , 2010, J. Sci. Comput..

[13]  Benjamin M. W. Tsui,et al.  Modeling respiratory mechanics in the MCAT and spline-based MCAT phantoms , 1999 .

[14]  S. Leng,et al.  High temporal resolution and streak-free four-dimensional cone-beam computed tomography , 2008, Physics in medicine and biology.

[15]  P. Munro,et al.  Four-dimensional cone-beam computed tomography using an on-board imager. , 2006, Medical physics.

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

[17]  L. Xing,et al.  Optimizing 4D cone-beam CT acquisition protocol for external beam radiotherapy. , 2007, International journal of radiation oncology, biology, physics.

[18]  Jing Wang,et al.  SU‐E‐J‐16: Noise Reduction with Detail Preservation for Low‐Dose KV CBCT Using Non‐Local Means: Simulated Patient Study , 2011 .

[19]  T. Pan,et al.  Autoadaptive phase-correlated (AAPC) reconstruction for 4D CBCT. , 2009, Medical physics.

[20]  Xun Jia,et al.  GPU-based fast gamma index calculation. , 2011, Physics in medicine and biology.

[21]  Steve B Jiang,et al.  Implementation and evaluation of various demons deformable image registration algorithms on a GPU. , 2010, Physics in medicine and biology.

[22]  Guy Gilboa,et al.  Nonlocal Operators with Applications to Image Processing , 2008, Multiscale Model. Simul..

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

[24]  Ruijiang Li,et al.  GPU-based Cone Beam CT Reconstruction via Total Variation Regularization , 2010 .

[25]  Steve B. Jiang,et al.  Development of a GPU-based Monte Carlo dose calculation code for coupled electron–photon transport , 2009, Physics in medicine and biology.

[26]  Steve B. Jiang,et al.  GPU-based fast cone beam CT reconstruction from undersampled and noisy projection data via total variation , 2010 .

[27]  Steve B. Jiang,et al.  GPU-based ultrafast IMRT plan optimization , 2009, Physics in medicine and biology.

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

[29]  Gene H. Golub,et al.  Matrix computations , 1983 .

[30]  Lei Xing,et al.  Enhanced 4D cone-beam CT with inter-phase motion model. , 2007, Medical physics.

[31]  L. Feldkamp,et al.  Practical cone-beam algorithm , 1984 .

[32]  Uwe Oelfke,et al.  Linac-integrated 4D cone beam CT: first experimental results , 2006, Physics in medicine and biology.

[33]  J. Wong,et al.  Flat-panel cone-beam computed tomography for image-guided radiation therapy. , 2002, International journal of radiation oncology, biology, physics.

[34]  K. Rosenzweig,et al.  Correction of motion artifacts in cone-beam CT using a patient-specific respiratory motion model. , 2010, Medical physics.

[35]  G C Sharp,et al.  GPU-based streaming architectures for fast cone-beam CT image reconstruction and demons deformable registration , 2007, Physics in medicine and biology.

[36]  Tinsu Pan,et al.  Four-dimensional volume-of-interest reconstruction for cone-beam computed tomography-guided radiation therapy. , 2011, Medical physics.

[37]  Steve B. Jiang,et al.  Fast Monte Carlo simulation for patient-specific CT/CBCT imaging dose calculation , 2011, Physics in medicine and biology.

[38]  M. V. van Herk,et al.  Respiratory correlated cone beam CT. , 2005, Medical physics.

[39]  Fang Xu,et al.  Accelerating popular tomographic reconstruction algorithms on commodity PC graphics hardware , 2005, IEEE Transactions on Nuclear Science.

[40]  T. M. Guerrero,et al.  Four-dimensional cone beam CT with adaptive gantry rotation and adaptive data sampling. , 2007, Medical physics.

[41]  Steve B Jiang,et al.  GPU-based ultra-fast dose calculation using a finite size pencil beam model. , 2009, Physics in medicine and biology.

[42]  P. Munro,et al.  Four-dimensional cone beam CT with adaptive gantry rotation and adaptive data sampling. , 2007, Medical physics.

[43]  Pierrick Coupé,et al.  Fast Non Local Means Denoising for 3D MR Images , 2006, MICCAI.

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

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

[46]  M. Hestenes,et al.  Methods of conjugate gradients for solving linear systems , 1952 .