Investigation of a breathing surrogate prediction algorithm for prospective pulmonary gating.

PURPOSE A major challenge of four dimensional computed tomography (4DCT) in treatment planning and delivery has been the lack of respiration amplitude and phase reproducibility during image acquisition. The implementation of a prospective gating algorithm would ensure that images would be acquired only during user-specified breathing phases. This study describes the development and testing of an autoregressive moving average (ARMA) model for human respiratory phase prediction under quiet respiration conditions. METHODS A total of 47 4DCT patient datasets and synchronized respiration records was utilized in this study. Three datasets were used in model development and were removed from further evaluation of the ARMA model. The remaining 44 patient datasets were evaluated with the ARMA model for prediction time steps from 50 to 1000 ms in increments of 50 and 100 ms. Thirty-five of these datasets were further used to provide a comparison between the proposed ARMA model and a commercial algorithm with a prediction time step of 240 ms. RESULTS The optimal number of parameters for the ARMA model was based on three datasets reserved for model development. Prediction error was found to increase as the prediction time step increased. The minimum prediction time step required for prospective gating was selected to be half of the gantry rotation period. The maximum prediction time step with a conservative 95% confidence criterion was found to be 0.3 s. The ARMA model predicted peak inhalation and peak exhalation phases significantly better than the commercial algorithm. Furthermore, the commercial algorithm had numerous instances of missed breath cycles and falsely predicted breath cycles, while the proposed model did not have these errors. CONCLUSIONS An ARMA model has been successfully applied to predict human respiratory phase occurrence. For a typical CT scanner gantry rotation period of 0.4 s (0.2 s prediction time step), the absolute error was relatively small, 0.06 +/- 0.02 s at peak inhalation and 0.05 +/- 0.04 s at peak exhalation. The application of the ARMA model for prospective pulmonary gating has been demonstrated.

[1]  Martin J Murphy,et al.  Optimization of an adaptive neural network to predict breathing. , 2008, Medical physics.

[2]  Jeffrey D Bradley,et al.  Comparison of spirometry and abdominal height as four-dimensional computed tomography metrics in lung. , 2005, Medical physics.

[3]  Jan-Jakob Sonke,et al.  Exploring breathing pattern irregularity with projection-based method. , 2006, Medical physics.

[4]  C. J. Ritchie,et al.  Predictive respiratory gating: a new method to reduce motion artifacts on CT scans. , 1994, Radiology.

[5]  Gregory C Sharp,et al.  Prediction of respiratory tumour motion for real-time image-guided radiotherapy. , 2004, Physics in medicine and biology.

[6]  Parag J. Parikh,et al.  Development of the 4D Phantom for patient-specific end-to-end radiation therapy QA , 2007, SPIE Medical Imaging.

[7]  Manish Kakar,et al.  Respiratory motion prediction by using the adaptive neuro fuzzy inference system (ANFIS). , 2005, Physics in medicine and biology.

[8]  R Mohan,et al.  Predicting respiratory motion for four-dimensional radiotherapy. , 2004, Medical physics.

[9]  R. Emery,et al.  Clinical experience using respiratory gated radiation therapy: comparison of free-breathing and breath-hold techniques. , 2004, International journal of radiation oncology, biology, physics.

[10]  John A Mills,et al.  A multiple model approach to respiratory motion prediction for real-time IGRT , 2008, Physics in medicine and biology.

[11]  Peter A S Johnstone,et al.  Free breathing gated delivery (FBGD) of lung radiation therapy: analysis of factors affecting clinical patient throughput. , 2007, Lung cancer.

[12]  Cynthia H McCollough,et al.  Quality assurance for computed-tomography simulators and the computed-tomography-simulation process: report of the AAPM Radiation Therapy Committee Task Group No. 66. , 2003, Medical physics.

[13]  D Ruan,et al.  Real-time prediction of respiratory motion based on local regression methods , 2007, Physics in medicine and biology.

[14]  Jeffrey A Fessler,et al.  Mean position tracking of respiratory motion. , 2008, Medical physics.

[15]  S. T. Nichols,et al.  Application of Autoregressive Moving Average Parametric Modeling in Magnetic Resonance Image Reconstruction , 1986, IEEE Transactions on Medical Imaging.

[16]  D Ruan,et al.  Real-time profiling of respiratory motion: baseline drift, frequency variation and fundamental pattern change , 2009, Physics in medicine and biology.

[17]  I P PRIBAN,et al.  An analysis of some short‐term patterns of breathing in man at rest , 1963, The Journal of physiology.

[18]  Wei Lu,et al.  Technical note: development of a tidal volume surrogate that replaces spirometry for physiological breathing monitoring in 4D CT. , 2010, Medical physics.

[19]  J. Dempsey,et al.  Novel breathing motion model for radiotherapy. , 2005, International journal of radiation oncology, biology, physics.