4D CT image reconstruction with diffeomorphic motion model

Four-dimensional (4D) respiratory correlated computed tomography (RCCT) has been widely used for studying organ motion. Most current RCCT imaging algorithms use binning techniques that are susceptible to artifacts and challenge the quantitative analysis of organ motion. In this paper, we develop an algorithm for analyzing organ motion which uses the raw, time-stamped imaging data to reconstruct images while simultaneously estimating deformation in the subject's anatomy. This results in reduction of artifacts and facilitates a reduction in dose to the patient during scanning while providing equivalent or better image quality as compared to RCCT. The framework also incorporates fundamental physical properties of organ motion, such as the conservation of local tissue volume. We demonstrate that this approach is accurate and robust against noise and irregular breathing patterns. We present results for a simulated cone beam CT phantom, as well as a detailed real porcine liver phantom study demonstrating accuracy and robustness of the algorithm. An example of applying this algorithm to real patient image data is also presented.

[1]  T. Pan,et al.  4D-CT imaging of a volume influenced by respiratory motion on multi-slice CT. , 2004, Medical physics.

[2]  Y. Vardi,et al.  From image deblurring to optimal investments : maximum likelihood solutions for positive linear inverse problems , 1993 .

[3]  Jeffrey A. Fessler,et al.  Discriminative sliding preserving regularization in medical image registration , 2009, 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

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

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

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

[7]  Jeffrey A. Fessler,et al.  Estimating 3-D Respiratory Motion From Orbiting Views by Tomographic Image Registration , 2007, IEEE Transactions on Medical Imaging.

[8]  Hengyong Yu,et al.  Data Consistency Based Rigid Motion Artifact Reduction in Fan-Beam CT , 2007, IEEE Transactions on Medical Imaging.

[9]  R. Castillo,et al.  Four-dimensional deformable image registration using trajectory modeling , 2010, Physics in medicine and biology.

[10]  P Rassiah-Szegedi,et al.  A proto-type design of a real-tissue phantom for the validation of deformation algorithms and 4D dose calculations. , 2010, Physics in medicine and biology.

[11]  Farida Cheriet,et al.  Large deformation registration of contrast-enhanced images with volume-preserving constraint , 2007, SPIE Medical Imaging.

[12]  Jos Starn.  A Simple Fluid Solver Based on the FFT , 2001, J. Graphics, GPU, & Game Tools.

[13]  Michael I. Miller,et al.  Group Actions, Homeomorphisms, and Matching: A General Framework , 2004, International Journal of Computer Vision.

[14]  Sarang Joshi,et al.  Medical image analysis via frechet means of diffeomorphisms , 2008 .

[15]  C. Ling,et al.  Evaluation of an automated deformable image matching method for quantifying lung motion in respiration-correlated CT images. , 2006, Medical physics.

[16]  Prema Rassiah-Szegedi,et al.  SU‐FF‐J‐128: Characterization of Liver Motion Based On Implanted Markers , 2009 .

[17]  Tinsu Pan,et al.  Four-dimensional computed tomography: image formation and clinical protocol. , 2005, Medical physics.

[18]  Jan-Jakob Sonke,et al.  On-the-Fly Motion-Compensated Cone-Beam CT Using an a Priori Motion Model , 2008, MICCAI.

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

[20]  R. Mohan,et al.  Acquiring a four-dimensional computed tomography dataset using an external respiratory signal. , 2003, Physics in medicine and biology.

[21]  Michael I. Miller,et al.  Deformable templates using large deformation kinematics , 1996, IEEE Trans. Image Process..

[22]  Ali R. Khan,et al.  Representation of time-varying shapes in the large deformation diffeomorphic framework , 2008, 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[23]  Dennis DeTurck,et al.  Vector Calculus and the Topology of Domains in 3-Space , 2002, Am. Math. Mon..

[24]  Weiguo Lu,et al.  Tomographic motion detection and correction directly in sinogram space. , 2002, Physics in medicine and biology.

[25]  R. Leahy,et al.  Computation of 3-D velocity fields from 3-D cine CT images of a human heart. , 1991, IEEE transactions on medical imaging.

[26]  G Malandain,et al.  Model-based respiratory motion compensation for emission tomography image reconstruction , 2007, Physics in medicine and biology.

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

[28]  Alain Trouvé,et al.  Computing Large Deformation Metric Mappings via Geodesic Flows of Diffeomorphisms , 2005, International Journal of Computer Vision.

[29]  P J Keall,et al.  Accuracy in the localization of thoracic and abdominal tumors using respiratory displacement, velocity, and phase. , 2009, Medical physics.

[30]  E Yorke,et al.  Phase and amplitude binning for 4D-CT imaging , 2006, Physics in medicine and biology.

[31]  P. Dupuis,et al.  Large deviations for stochastic flows of diffeomorphisms , 2010, 1002.4295.

[32]  S. Kalnicki,et al.  Abdominal organ motion measured using 4D CT. , 2006, International journal of radiation oncology, biology, physics.

[33]  Steve B. Jiang,et al.  4D-CT lung motion estimation with deformable registration: quantification of motion nonlinearity and hysteresis. , 2008, Medical physics.

[34]  Sarang Joshi,et al.  Large deformation three-dimensional image registration in image-guided radiation therapy , 2005, Physics in medicine and biology.

[35]  H. Kuo.  Gaussian Measures in Banach Spaces , 1975 .

[36]  Paul Dupuis,et al.  Variational problems on ows of di eomorphisms for image matching , 1998 .

[37]  H. Krebs,et al.  Untersuchungen uber die Harnstoffbildung im Tierkörper , 1932 .

[38]  Benjamin M. W. Tsui,et al.  Noise properties of filtered-backprojection and ML-EM reconstructed emission tomographic images , 1992 .

[39]  V. Arnold.  Mathematical Methods of Classical Mechanics , 1974 .

[40]  Nicholas Ayache,et al.  Medical Image Analysis: Progress over Two Decades and the Challenges Ahead , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[41]  U. Grenander,et al.  Structural Image Restoration through Deformable Templates , 1991 .

[42]  P. Thomas Fletcher,et al.  4D MAP Image Reconstruction Incorporating Organ Motion , 2009, IPMI.

[43]  Torsten Rohlfing,et al.  Volume-preserving nonrigid registration of MR breast images using free-form deformation with an incompressibility constraint , 2003, IEEE Transactions on Medical Imaging.