Respiratory motion estimation from slowly rotating x-ray projections: theory and simulation.

Understanding the movement of tumors caused by respiratory motion is very important for conformal radiatherapy. However, respiratory motion is very difficult to study by conventional x-ray CT imaging since object motion causes inconsistent projection views, leading to artifacts in reconstructed images. We propose to estimate the parameters of a nonrigid, free breathing motion model from a set of projection views of the thorax that are acquired using a slowly rotating cone-beam CT scanner. This approach involves deforming a motion-free reference thorax volume according to the estimated parameters and comparing its projections to the corresponding measured projection views. The parameters are optimized by minimizing a regularized squared error cost function. Simulation results with a fan-beam geometry show good agreement between the estimated motion and the true motion, which supports the potential of this approach for estimating four-dimensional (three-dimensional spatial + temporal) respiratory motion.

[1]  Michael Unser,et al.  Fast parametric elastic image registration , 2003, IEEE Trans. Image Process..

[2]  J Simon,et al.  Image reconstruction and performance evaluation for ECG-gated spiral scanning with a 16-slice CT system. , 2003, Medical physics.

[3]  Cameron J. Ritchie,et al.  Respiratory compensation in projection imaging using a magnification and displacement model , 1996, IEEE Trans. Medical Imaging.

[4]  Pierre Grangeat,et al.  Exact reconstruction in 2D dynamic CT: compensation of time-dependent affine deformations. , 2004, Physics in medicine and biology.

[5]  H Anno,et al.  Minimum scan speeds for suppression of motion artifacts in CT. , 1992, Radiology.

[6]  I. El Naqa,et al.  Automated breathing motion tracking for 4D computed tomography , 2003, 2003 IEEE Nuclear Science Symposium. Conference Record (IEEE Cat. No.03CH37515).

[7]  A C Dhanantwari,et al.  Correcting organ motion artifacts in x-ray CT medical imaging systems by adaptive processing. I. Theory. , 2001, Medical physics.

[8]  Daniel Rueckert,et al.  Nonrigid registration using free-form deformations: application to breast MR images , 1999, IEEE Transactions on Medical Imaging.

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

[10]  Michael B Sharpe,et al.  Significant reductions in heart and lung doses using deep inspiration breath hold with active breathing control and intensity-modulated radiation therapy for patients treated with locoregional breast irradiation. , 2003, International journal of radiation oncology, biology, physics.

[11]  G. Christensen,et al.  A method for the reconstruction of four-dimensional synchronized CT scans acquired during free breathing. , 2003, Medical physics.

[12]  E.A. Hoffman,et al.  High-speed three-dimensional X-ray computed tomography: The dynamic spatial reconstructor , 1983, Proceedings of the IEEE.

[13]  Ge Wang,et al.  Preliminary study on helical CT algorithms for patient motion estimation and compensation , 1995, IEEE Trans. Medical Imaging.

[14]  R. Mohan,et al.  Motion adaptive x-ray therapy: a feasibility study , 2001, Physics in medicine and biology.

[15]  Michael Unser,et al.  Splines: a perfect fit for signal and image processing , 1999, IEEE Signal Process. Mag..

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

[17]  H Hu,et al.  Multi-slice helical CT: scan and reconstruction. , 1999, Medical physics.

[18]  J. Wong,et al.  The use of active breathing control (ABC) to reduce margin for breathing motion. , 1999, International journal of radiation oncology, biology, physics.