4D cone beam CT via spatiotemporal tensor framelet.

PURPOSE On-board 4D cone beam CT (4DCBCT) offers respiratory phase-resolved volumetric imaging, and improves the accuracy of target localization in image guided radiation therapy. However, the clinical utility of this technique has been greatly impeded by its degraded image quality, prolonged imaging time, and increased imaging dose. The purpose of this letter is to develop a novel iterative 4DCBCT reconstruction method for improved image quality, increased imaging speed, and reduced imaging dose. METHODS The essence of this work is to introduce the spatiotemporal tensor framelet (STF), a high-dimensional tensor generalization of the 1D framelet for 4DCBCT, to effectively take into account of highly correlated and redundant features of the patient anatomy during respiration, in a multilevel fashion with multibasis sparsifying transform. The STF-based algorithm is implemented on a GPU platform for improved computational efficiency. To evaluate the method, 4DCBCT full-fan scans were acquired within 30 s, with a gantry rotation of 200°; STF is also compared with a state-of-art reconstruction method via spatiotemporal total variation regularization. RESULTS Both the simulation and experimental results demonstrate that STF-based reconstruction achieved superior image quality. The reconstruction of 20 respiratory phases took less than 10 min on an NVIDIA Tesla C2070 GPU card. The STF codes are available at https://sites.google.com/site/spatiotemporaltensorframelet. CONCLUSIONS By effectively utilizing the spatiotemporal coherence of the patient anatomy among different respiratory phases in a multilevel fashion with multibasis sparsifying transform, the proposed STF method potentially enables fast and low-dose 4DCBCT with improved image quality.

[1]  William P. Segars,et al.  A realistic spline-based dynamic heart phantom , 1998 .

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

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

[4]  L. Xing,et al.  Overview of image-guided radiation therapy. , 2006, Medical dosimetry : official journal of the American Association of Medical Dosimetrists.

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

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

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

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

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

[10]  Jian-Feng Cai,et al.  Split Bregman Methods and Frame Based Image Restoration , 2009, Multiscale Model. Simul..

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

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

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

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

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

[16]  Andreas Maier,et al.  Spatial-temporal total variation regularization (STTVR) for 4D-CT reconstruction , 2012, Medical Imaging.

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