Dosimetric impact of motion in free-breathing and gated lung radiotherapy: a 4D Monte Carlo study of intrafraction and interfraction effects.

The purpose of this study was to investigate if interfraction and intrafraction motion in free-breathing and gated lung IMRT can lead to systematic dose differences between 3DCT and 4DCT. Dosimetric effects were studied considering the breathing pattern of three patients monitored during the course of their treatment and an in-house developed 4D Monte Carlo framework. Imaging data were taken in free-breathing and in cine mode for both 3D and 4D acquisition. Treatment planning for IMRT delivery was done based on the free-breathing data with the CORVUS (North American Scientific, Chatsworth, CA) planning system. The dose distributions as a function of phase in the breathing cycle were combined using deformable image registration. The study focused on (a) assessing the accuracy of the CORVUS pencil beam algorithm with Monte Carlo dose calculation in the lung, (b) evaluating the dosimetric effect of motion on the individual breathing phases of the respiratory cycle, and (c) assessing intrafraction and interfraction motion effects during free-breathing or gated radiotherapy. The comparison between (a) the planning system and the Monte Carlo system shows that the pencil beam algorithm underestimates the dose in low-density regions, such as lung tissue, and overestimates the dose in high-density regions, such as bone, by 5% or more of the prescribed dose (corresponding to approximately 3-5 Gy for the cases considered). For the patients studied this could have a significant impact on the dose volume histograms for the target structures depending on the margin added to the clinical target volume (CTV) to produce either the planning target (PTV) or internal target volume (ITV). The dose differences between (b) phases in the breathing cycle and the free-breathing case were shown to be negligible for all phases except for the inhale phase, where an underdosage of the tumor by as much as 9.3 Gy relative to the free-breathing was observed. The large difference was due to breathing-induced motion/deformation affecting the soft/lung tissue density and motion of the bone structures (such as the rib cage) in and out of the beam. Intrafraction and interfraction dosimetric differences between (c) free-breathing and gated delivery were found to be small. However, more significant dosimetric differences, of the order of 3%-5%, were observed between the dose calculations based on static CT (3DCT) and the ones based on time-resolved CT (4DCT). These differences are a consequence of the larger contribution of the inhale phase in the 3DCT data than in the 4DCT.

[1]  Indrin J Chetty,et al.  How extensive of a 4D dataset is needed to estimate cumulative dose distribution plan evaluation metrics in conformal lung therapy? , 2006, Medical physics.

[2]  Indrin J Chetty,et al.  The impact of breathing motion versus heterogeneity effects in lung cancer treatment planning. , 2007, Medical physics.

[3]  Jan-Jakob Sonke,et al.  Mid-ventilation CT scan construction from four-dimensional respiration-correlated CT scans for radiotherapy planning of lung cancer patients. , 2006, International journal of radiation oncology, biology, physics.

[4]  Barbara Vanderstraeten,et al.  Accuracy of patient dose calculation for lung IMRT: A comparison of Monte Carlo, convolution/superposition, and pencil beam computations. , 2006, Medical physics.

[5]  S B Jiang,et al.  Monte Carlo verification of IMRT dose distributions from a commercial treatment planning optimization system. , 2000, Physics in medicine and biology.

[6]  E. Yorke,et al.  Monte Carlo evaluation of 6 MV intensity modulated radiotherapy plans for head and neck and lung treatments. , 2002, Medical physics.

[7]  P M Evans,et al.  Assessing the effect of electron density in photon dose calculations. , 2006, Medical physics.

[8]  R. Mohan,et al.  A method for photon beam Monte Carlo multileaf collimator particle transport. , 2002, Physics in medicine and biology.

[9]  J T Booth,et al.  Set-up error & organ motion uncertainty: a review. , 1999, Australasian physical & engineering sciences in medicine.

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

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

[12]  T. Krieger,et al.  Monte Carlo- versus pencil-beam-/collapsed-cone-dose calculation in a heterogeneous multi-layer phantom , 2005, Physics in medicine and biology.

[13]  J. V. van Meerbeeck,et al.  Has 3-D conformal radiotherapy (3D CRT) improved the local tumour control for stage I non-small cell lung cancer? , 2002, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[14]  Joao Seco,et al.  Conversion of CT numbers into tissue parameters for Monte Carlo dose calculations: a multi-centre study , 2007, Physics in medicine and biology.

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

[16]  H Paganetti,et al.  Effects of organ motion on IMRT treatments with segments of few monitor units. , 2007, Medical physics.

[17]  X Allen Li,et al.  Point/counterpoint. Respiratory gating for radiation therapy is not ready for prime time. , 2007, Medical physics.

[18]  Antonio Leal,et al.  Routine IMRT verification by means of an automated Monte Carlo simulation system. , 2003, International journal of radiation oncology, biology, physics.

[19]  George T. Y. Chen,et al.  Four-dimensional image-based treatment planning: Target volume segmentation and dose calculation in the presence of respiratory motion. , 2005, International journal of radiation oncology, biology, physics.

[20]  Indra J Das,et al.  Comparison of inhomogeneity correction algorithms in small photon fields. , 2005, Medical physics.

[21]  Steve B. Jiang,et al.  Estimation of the delivered patient dose in lung IMRT treatment based on deformable registration of 4D-CT data and Monte Carlo simulations , 2006, Physics in medicine and biology.

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

[23]  A L Boyer,et al.  Modeling the extrafocal radiation and monitor chamber backscatter for photon beam dose calculation. , 2001, Medical physics.

[24]  J. Seco,et al.  Head-and-neck IMRT treatments assessed with a Monte Carlo dose calculation engine , 2005, Physics in medicine and biology.

[25]  T. Bortfeld,et al.  Correlation between CT numbers and tissue parameters needed for Monte Carlo simulations of clinical dose distributions. , 2000, Physics in medicine and biology.

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

[27]  Radhe Mohan,et al.  Four-dimensional radiotherapy planning for DMLC-based respiratory motion tracking. , 2005, Medical physics.

[28]  J. Sempau,et al.  DPM, a fast, accurate Monte Carlo code optimized for photon and electron radiotherapy treatment planning dose calculations , 2000 .

[29]  I. Kawrakow Accurate condensed history Monte Carlo simulation of electron transport. I. EGSnrc, the new EGS4 version. , 2000, Medical physics.