Four-dimensional deformable image registration using trajectory modeling

A four-dimensional deformable image registration (4D DIR) algorithm, referred to as 4D local trajectory modeling (4DLTM), is presented and applied to thoracic 4D computed tomography (4DCT) image sets. The theoretical framework on which this algorithm is built exploits the incremental continuity present in 4DCT component images to calculate a dense set of parameterized voxel trajectories through space as functions of time. The spatial accuracy of the 4DLTM algorithm is compared with an alternative registration approach in which component phase to phase (CPP) DIR is utilized to determine the full displacement between maximum inhale and exhale images. A publically available DIR reference database (http://www.dir-lab.com) is utilized for the spatial accuracy assessment. The database consists of ten 4DCT image sets and corresponding manually identified landmark points between the maximum phases. A subset of points are propagated through the expiratory 4DCT component images. Cubic polynomials were found to provide sufficient flexibility and spatial accuracy for describing the point trajectories through the expiratory phases. The resulting average spatial error between the maximum phases was 1.25 mm for the 4DLTM and 1.44 mm for the CPP. The 4DLTM method captures the long-range motion between 4DCT extremes with high spatial accuracy.

[1]  Takeo Kanade,et al.  An Iterative Image Registration Technique with an Application to Stereo Vision , 1981, IJCAI.

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

[3]  Fred L. Bookstein,et al.  Principal Warps: Thin-Plate Splines and the Decomposition of Deformations , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  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.

[5]  David J. Fleet,et al.  Performance of optical flow techniques , 1992, Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[6]  Steven S. Beauchemin,et al.  The computation of optical flow , 1995, CSUR.

[7]  Max A. Viergever,et al.  A survey of medical image registration , 1998, Medical Image Anal..

[8]  Paul A. Yushkevich,et al.  Segmentation, registration, and measurement of shape variation via image object shape , 1999, IEEE Transactions on Medical Imaging.

[9]  H. Shirato,et al.  Four-dimensional treatment planning and fluoroscopic real-time tumor tracking radiotherapy for moving tumor. , 2000, International journal of radiation oncology, biology, physics.

[10]  Ronald H. Huesman,et al.  Four-dimensional processing of deformable cardiac PET data , 2002, Medical Image Anal..

[11]  M. V. van Herk,et al.  Precise and real-time measurement of 3D tumor motion in lung due to breathing and heartbeat, measured during radiotherapy. , 2002, International journal of radiation oncology, biology, physics.

[12]  Dinggang Shen,et al.  HAMMER: hierarchical attribute matching mechanism for elastic registration , 2002, IEEE Transactions on Medical Imaging.

[13]  R. LeVeque.  Finite Volume Methods for Hyperbolic Problems: Characteristics and Riemann Problems for Linear Hyperbolic Equations , 2002 .

[14]  Patrick Pérez,et al.  Dense Estimation of Fluid Flows , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  Lewis D. Griffin,et al.  Zen and the art of medical image registration: correspondence, homology, and quality , 2003, NeuroImage.

[16]  C. Ling,et al.  Respiration-correlated spiral CT: a method of measuring respiratory-induced anatomic motion for radiation treatment planning. , 2002, Medical physics.

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

[18]  Geoffrey G. Zhang,et al.  Intrathoracic tumour motion estimation from CT imaging using the 3D optical flow method. , 2004, Physics in medicine and biology.

[19]  P. Keall.  4-dimensional computed tomography imaging and treatment planning. , 2004, Seminars in radiation oncology.

[20]  Shinichi Shimizu,et al.  Intrafractional tumor motion: lung and liver. , 2004, Seminars in radiation oncology.

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

[22]  Geoffrey G. Zhang,et al.  Elastic image mapping for 4-D dose estimation in thoracic radiotherapy. , 2005, Radiation protection dosimetry.

[23]  David J. Fleet,et al.  Performance of optical flow techniques , 1994, International Journal of Computer Vision.

[24]  George Starkschall,et al.  Semiautomated four-dimensional computed tomography segmentation using deformable models. , 2005, Medical physics.

[25]  K. Brock,et al.  Accuracy of finite element model-based multi-organ deformable image registration. , 2005, Medical physics.

[26]  J. Warren,et al.  Image deformation using moving least squares , 2006, SIGGRAPH 2006.

[27]  Tinsu Pan,et al.  Dynamic ventilation imaging from four-dimensional computed tomography , 2006, Physics in medicine and biology.

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

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

[30]  David Sarrut,et al.  Deformable registration for image-guided radiation therapy. , 2006, Zeitschrift fur medizinische Physik.

[31]  Achim Schweikard,et al.  Fully automatic detection of corresponding anatomical landmarks in volume scans of different respiratory state. , 2006, Medical physics.

[32]  M. Kessler.  Image registration and data fusion in radiation therapy. , 2006, The British journal of radiology.

[33]  Eike Rietzel,et al.  Deformable registration of 4D computed tomography data. , 2006, Medical physics.

[34]  Joe Y. Chang,et al.  4D Proton treatment planning strategy for mobile lung tumors. , 2007, International journal of radiation oncology, biology, 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]  Klaus Mueller,et al.  IOP PUBLISHING PHYSICS IN MEDICINE AND BIOLOGY , 2007 .

[37]  Lei Xing,et al.  Hybrid multiscale landmark and deformable image registration. , 2007, Mathematical biosciences and engineering : MBE.

[38]  Vladimir Pekar,et al.  Assessment of a model-based deformable image registration approach for radiation therapy planning. , 2007, International journal of radiation oncology, biology, physics.

[39]  E. Castillo.  Optical Flow Methods for the Registration of Compressible Flow Images and Images Containing Large Voxel Displacements or Artifacts , 2007 .

[40]  M van Herk,et al.  Reconstruction of a time-averaged midposition CT scan for radiotherapy planning of lung cancer patients using deformable registrationa). , 2008, Medical physics.

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

[42]  Kristy K Brock,et al.  Quality assurance of serial 3D image registration, fusion, and segmentation. , 2008, International journal of radiation oncology, biology, physics.

[43]  L. Xing,et al.  Four-dimensional image registration for image-guided radiotherapy. , 2008, International journal of radiation oncology, biology, physics.

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

[45]  Eric A. Hoffman,et al.  Registration-based estimates of local lung tissue expansion compared to xenon CT measures of specific ventilation , 2008, Medical Image Anal..

[46]  Gregory C Sharp,et al.  Evaluation of deformable registration of patient lung 4DCT with subanatomical region segmentations. , 2008, Medical physics.

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

[48]  Rudy Guerra,et al.  Determination of respiratory motion for distal esophagus cancer using four-dimensional computed tomography. , 2008, International journal of radiation oncology, biology, physics.

[49]  J Moseley,et al.  Contact surface and material nonlinearity modeling of human lungs , 2008, Physics in medicine and biology.

[50]  Rolf Bendl,et al.  Combination of intensity-based image registration with 3D simulation in radiation therapy , 2008, Physics in medicine and biology.

[51]  Yin Zhang,et al.  Compressible Image Registration for Thoracic Computed Tomography Images , 2009 .

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

[53]  R. Castillo,et al.  A framework for evaluation of deformable image registration spatial accuracy using large landmark point sets , 2009, Physics in medicine and biology.

[54]  Dinggang Shen,et al.  Estimating myocardial motion by 4D image warping , 2009, Pattern Recognit..