A motion-compensated cone-beam CT using electrical impedance tomography imaging.

Cone-beam CT (CBCT) is an imaging technique used in conjunction with radiation therapy. For example CBCT is used to verify the position of lung cancer tumours just prior to radiation treatment. The accuracy of the radiation treatment of thoracic and upper abdominal structures is heavily affected by respiratory movement. Such movement typically blurs the CBCT reconstruction and ideally should be removed. Hence motion-compensated CBCT has recently been researched for correcting image artefacts due to breathing motion. This paper presents a new dual-modality approach where CBCT is aided by using electrical impedance tomography (EIT) for motion compensation. EIT can generate images of contrasts in electrical properties. The main advantage of using EIT is its high temporal resolution. In this paper motion information is extracted from EIT images and incorporated directly in the CBCT reconstruction. In this study synthetic moving data are generated using simulated and experimental phantoms. The paper demonstrates that image blur, created as a result of motion, can be reduced through motion compensation with EIT.

[1]  Bo Zhao,et al.  Lung ventilation functional monitoring based on electrical impedance tomography , 2009 .

[2]  Andy Adler,et al.  In Vivo Impedance Imaging With Total Variation Regularization , 2010, IEEE Transactions on Medical Imaging.

[3]  Anand Rangarajan,et al.  Bayesian reconstruction of functional images using anatomical information as priors , 1993, IEEE Trans. Medical Imaging.

[4]  Ming Jiang,et al.  Blind deblurring of spiral CT images , 2003, Conference Record of Thirty-Fifth Asilomar Conference on Signals, Systems and Computers (Cat.No.01CH37256).

[5]  S. Rit,et al.  On-the-fly motion-compensated cone-beam CT using an a priori model of the respiratory motion. , 2009, Medical physics.

[6]  K. Lam,et al.  Uncertainties in CT-based radiation therapy treatment planning associated with patient breathing. , 1996, International journal of radiation oncology, biology, physics.

[7]  M. Soleimani,et al.  New iterative cone beam CT reconstruction software: Parameter optimisation and convergence study , 2010, Comput. Methods Programs Biomed..

[8]  Bostjan Likar,et al.  Comparative evaluation of similarity measures for the rigid registration of multi-modal head images. , 2007, Physics in medicine and biology.

[9]  G. Hahn,et al.  Improvements in the image quality of ventilatory tomograms by electrical impedance tomography , 2008, Physiological measurement.

[10]  A. H. Andersen.  Algebraic reconstruction in CT from limited views. , 1989, IEEE transactions on medical imaging.

[11]  S. Nebuya,et al.  Measurement of lung function using Electrical Impedance Tomography (EIT) during mechanical ventilation , 2010 .

[12]  G. Herman,et al.  Algebraic reconstruction techniques (ART) for three-dimensional electron microscopy and x-ray photography. , 1970, Journal of theoretical biology.

[13]  H McCann,et al.  3D simulation of EIT for monitoring impedance variations within the human head. , 2000, Physiological measurement.

[14]  D. S. Holder,et al.  Electrical impedance tomography (EIT) of brain function , 2005, Brain Topography.

[15]  F. Natterer.  The Mathematics of Computerized Tomography , 1986 .

[16]  D. Ros,et al.  The influence of a relaxation parameter on SPECT iterative reconstruction algorithms. , 1996, Physics in medicine and biology.

[17]  J.L. Coatrieux.  A look at... shape and function from motion in medical imaging: Part I , 2005, IEEE Engineering in Medicine and Biology Magazine.

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

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

[20]  G Hahn,et al.  Imaging pathologic pulmonary air and fluid accumulation by functional and absolute EIT , 2006, Physiological measurement.

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

[22]  M. Goitein.  Organ and tumor motion: an overview. , 2004, Seminars in radiation oncology.

[23]  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.

[24]  George T. Y. Chen,et al.  Artifacts in computed tomography scanning of moving objects. , 2004, Seminars in radiation oncology.

[25]  Charles L. Byrne.  The Algebraic Reconstruction Technique , 2007 .

[26]  Ming Jiang,et al.  Convergence Studies on Iterative Algorithms for Image Reconstruction , 2003, IEEE Trans. Medical Imaging.

[27]  Li Zheng,et al.  The Processing of the Degraded Medical Digital Image's Image Enhancement , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[28]  William R B Lionheart,et al.  A Matlab toolkit for three-dimensional electrical impedance tomography: a contribution to the Electrical Impedance and Diffuse Optical Reconstruction Software project , 2002 .

[29]  Marc Bodenstein,et al.  Principles of electrical impedance tomography and its clinical application , 2009, Critical care medicine.

[30]  Sartaj Sahni,et al.  Leaf sequencing algorithms for segmented multileaf collimation. , 2003, Physics in medicine and biology.

[31]  Robert M. Lewitt,et al.  A comparison of transform and iterative reconstruction techniques for a volume-imaging PET scanner with a large axial acceptance angle , 1995 .

[32]  I Frerichs,et al.  Electrical impedance tomography (EIT) in applications related to lung and ventilation: a review of experimental and clinical activities. , 2000, Physiological measurement.

[33]  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.

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

[35]  B H Brown,et al.  Clinical applications of electrical impedance tomography. , 1993, Journal of medical engineering & technology.

[36]  B.H. Brown,et al.  A real-time electrical impedance tomography system for clinical use-design and preliminary results , 1995, IEEE Transactions on Biomedical Engineering.

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

[38]  T W Griffin,et al.  Cone-beam CT for radiotherapy applications. , 1995, Physics in medicine and biology.

[39]  G.J. Saulnier,et al.  A real-time electrical impedance tomograph , 1995, IEEE Transactions on Biomedical Engineering.

[40]  J. G. Alessi,et al.  The Algebraic Reconstruction Technique (ART) , 1997, Proceedings of the 1997 Particle Accelerator Conference (Cat. No.97CH36167).

[41]  S. Leonhardt,et al.  Bedside measurement of changes in lung impedance to monitor alveolar ventilation in dependent and non-dependent parts by electrical impedance tomography during a positive end-expiratory pressure trial in mechanically ventilated intensive care unit patients , 2010, Critical care.

[42]  R H Bayford,et al.  Bioimpedance tomography (electrical impedance tomography). , 2006, Annual review of biomedical engineering.

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

[44]  Steve B. Jiang,et al.  Effects of intra-fraction motion on IMRT dose delivery: statistical analysis and simulation. , 2002, Physics in medicine and biology.

[45]  Avinash C. Kak,et al.  Principles of computerized tomographic imaging , 2001, Classics in applied mathematics.

[46]  Zhenyu Guo,et al.  A review of electrical impedance techniques for breast cancer detection. , 2003, Medical engineering & physics.

[47]  Steve B. Jiang,et al.  An experimental investigation on intra-fractional organ motion effects in lung IMRT treatments. , 2003, Physics in medicine and biology.