Registration-Based Reconstruction of Four-Dimensional Cone Beam Computed Tomography

We present a new method for reconstruction of 4-D cone beam computed tomography from an undersampled set of X-ray projections. The novelty of the proposed method lies in utilizing optical flow based registration to facilitate that each temporal phase is reconstructed from the full set of acquired projections. The reconstruction of each phase thus exhibits limited aliasing despite significant intra-phase undersampling. The method is fully self-contained. Initially an approximate 4-D volume is reconstructed and an inter-phase registration based hereon. A subsequent reconstruction pass integrates the optical flow estimation in a cost function formulation in which the X-ray projections from all temporal phases are considered for the reconstruction of each individual phase. Quantitative and qualitative evaluations were performed through reconstruction of both a numerical phantom and a clinical dataset. The obtained reconstructions are compared to the state-of-the-art alternatives of total variation regularization and prior image constrained compressed sensing. Our studies show that the proposed method is the better overall “compromise” in the depiction of both moving and stationary anatomical structures.

[1]  Paul J Keall,et al.  First demonstration of combined kV/MV image-guided real-time dynamic multileaf-collimator target tracking. , 2009, International journal of radiation oncology, biology, physics.

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

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

[4]  Jin Sung Kim,et al.  Fast compressed sensing-based CBCT reconstruction using Barzilai-Borwein formulation for application to on-line IGRT. , 2012, Medical physics.

[5]  Berthold K. P. Horn,et al.  Determining Optical Flow , 1981, Other Conferences.

[6]  Jiang Hsieh,et al.  Computed Tomography: Principles, Design, Artifacts, and Recent Advances, Fourth Edition , 2022 .

[7]  Kari Tanderup,et al.  Acceleration and validation of optical flow based deformable registration for image-guided radiotherapy , 2008, Acta oncologica.

[8]  M. Kuo,et al.  C-arm cone-beam CT: general principles and technical considerations for use in interventional radiology. , 2009, Journal of vascular and interventional radiology : JVIR.

[9]  Jong Chul Ye,et al.  k‐t FOCUSS: A general compressed sensing framework for high resolution dynamic MRI , 2009, Magnetic resonance in medicine.

[10]  Michael Schacht Hansen,et al.  Gadgetron: An open source framework for medical image reconstruction , 2013, Magnetic resonance in medicine.

[11]  Martin Szegedi,et al.  4D CT image reconstruction with diffeomorphic motion model , 2012, Medical Image Anal..

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

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

[14]  Berthold K. P. Horn,et al.  Determining Optical Flow , 1981, Other Conferences.

[15]  Graeme C. Mc Kinnon,et al.  Towards Imaging the Beating Heart Usefully with a Conventional CT Scanner , 1981, IEEE Transactions on Biomedical Engineering.

[16]  E. Sidky,et al.  Image reconstruction in circular cone-beam computed tomography by constrained, total-variation minimization , 2008, Physics in medicine and biology.

[17]  Colin Studholme,et al.  Registration-based approach for reconstruction of high-resolution in utero fetal MR brain images. , 2006, Academic radiology.

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

[19]  Guang-Hong Chen,et al.  Time-Resolved Interventional Cardiac C-arm Cone-Beam CT: An Application of the PICCS Algorithm , 2012, IEEE Transactions on Medical Imaging.

[20]  Gengsheng Lawrence Zeng,et al.  Unmatched projector/backprojector pairs in an iterative reconstruction algorithm , 2000, IEEE Transactions on Medical Imaging.

[21]  Hayit Greenspan,et al.  MRI inter-slice reconstruction using super-resolution , 2002 .

[22]  Stanley Osher,et al.  A split Bregman method for non-negative sparsity penalized least squares with applications to hyperspectral demixing , 2010, 2010 IEEE International Conference on Image Processing.

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

[24]  Marcus Brehm,et al.  Self-adapting cyclic registration for motion-compensated cone-beam CT in image-guided radiation therapy. , 2012, Medical physics.

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

[26]  Steve Webb,et al.  Obtaining breathing patterns from any sequential thoracic x-ray image set , 2009, Physics in medicine and biology.

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

[28]  Fang-Fang Yin,et al.  A novel technique for markerless, self-sorted 4D-CBCT: feasibility study. , 2012, Medical physics.

[29]  Nassir Navab,et al.  Joint Reconstruction of Image and Motion in Gated Positron Emission Tomography , 2010, IEEE Transactions on Medical Imaging.

[30]  Jan-Jakob Sonke,et al.  Quantification of the variability of diaphragm motion and implications for treatment margin construction. , 2012, International journal of radiation oncology, biology, physics.

[31]  Søren Holdt Jensen,et al.  Implementation of an optimal first-order method for strongly convex total variation regularization , 2011, ArXiv.

[32]  Takeo Kanade,et al.  Adapting optical-flow to measure object motion in reflectance and x-ray image sequences (abstract only) , 1984, COMG.

[33]  Kay Nehrke,et al.  k‐t PCA: Temporally constrained k‐t BLAST reconstruction using principal component analysis , 2009, Magnetic resonance in medicine.

[34]  David R. Gilland,et al.  Estimation of images and nonrigid deformations in gated emission CT , 2006, IEEE Transactions on Medical Imaging.

[35]  David L Donoho,et al.  Compressed sensing , 2006, IEEE Transactions on Information Theory.

[36]  J. Ehrhardt,et al.  An optical flow based method for improved reconstruction of 4D CT data sets acquired during free breathing. , 2007, Medical physics.

[37]  Ruola Ning,et al.  Flat panel detector-based cone-beam volume CT angiography imaging: system evaluation , 2000, IEEE Transactions on Medical Imaging.

[38]  W. Segars,et al.  4D XCAT phantom for multimodality imaging research. , 2010, Medical physics.

[39]  V. Boldea,et al.  Simulation of four-dimensional CT images from deformable registration between inhale and exhale breath-hold CT scans. , 2006, Medical physics.

[40]  Peter Kellman,et al.  Retrospective reconstruction of high temporal resolution cine images from real‐time MRI using iterative motion correction , 2012, Magnetic resonance in medicine.