Respiratory motion correction utilizing geometric sensitivity in 3D PET: A simulation study

The PET image quality can be degraded due to respiratory motion. In this paper, we present a new data-driven method for respiratory motion compensation that utilizes the geometric sensitivity feature of a 3D-PET scanner system operating in list event acquisition mode. The count rate from a given organ will depend on the axial location of the organ due to the geometric sensitivity. As a result the respiratory transforms to the reference frame can be determined from count rate changes which are determined to suitable temporal resolution from the list-mode data stream. This method only uses LOR events and is non-invasive, no additional hardware device systems are required and there is no additional preparation required. Simulation results demonstrate that the geometric sensitivity correction (GSC) method may reduce motion degradation.

[1]  K. Langen,et al.  Organ motion and its management. , 2001, International journal of radiation oncology, biology, physics.

[2]  William Paul Segars,et al.  Development of a new dynamic NURBS-based cardiac-torso (NCAT) phantom , 2001 .

[3]  S. Nekolla,et al.  Postacquisition Detection of Tumor Motion in the Lung and Upper Abdomen Using List-Mode PET Data: A Feasibility Study , 2007, Journal of Nuclear Medicine.

[4]  C. Ling,et al.  Effect of respiratory gating on reducing lung motion artifacts in PET imaging of lung cancer. , 2002, Medical physics.

[5]  Yuji Nakamoto,et al.  Clinically significant inaccurate localization of lesions with PET/CT: frequency in 300 patients. , 2003, Journal of nuclear medicine : official publication, Society of Nuclear Medicine.

[6]  C. L. Le Rest,et al.  Validation of a Monte Carlo simulation of the Philips Allegro/GEMINI PET systems using GATE , 2006, Physics in medicine and biology.

[7]  Michael A. King,et al.  Correction of the Respiratory Motion of the Heart by Tracking of the Center of Mass of Thresholded Projections: A Simulation Study Using the Dynamic MCAT Phantom. , 2001 .

[8]  R. Huesman,et al.  Fine-scale motion detection using intrinsic list mode PET information , 2001, Proceedings IEEE Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA 2001).

[9]  Dale L Bailey,et al.  Externally triggered gating of nuclear medicine acquisitions: a useful method for partitioning data , 2005, Physics in medicine and biology.

[10]  Jianfeng He,et al.  A Novel Method for Respiratory Motion Gated With Geometric Sensitivity of the Scanner in 3D PET , 2008, IEEE Transactions on Nuclear Science.

[11]  Frederic H Fahey,et al.  Data acquisition in PET imaging. , 2002, Journal of nuclear medicine technology.

[12]  M. V. van Herk,et al.  Respiratory correlated cone beam CT. , 2005, Medical physics.

[13]  P.H. Pretorius,et al.  Correction of the respiratory motion of the heart by tracking of the center of mass of thresholded projections: a simulation study using the dynamic MCAT phantom , 2001, 2001 IEEE Nuclear Science Symposium Conference Record (Cat. No.01CH37310).

[14]  Michael E. Phelps,et al.  PET: Molecular Imaging and Its Biological Applications , 2004 .