Estimation of the delivered patient dose in lung IMRT treatment based on deformable registration of 4D-CT data and Monte Carlo simulations

The purpose of this study is to accurately estimate the difference between the planned and the delivered dose due to respiratory motion and free breathing helical CT artefacts for lung IMRT treatments, and to estimate the impact of this difference on clinical outcome. Six patients with representative tumour motion, size and position were selected for this retrospective study. For each patient, we had acquired both a free breathing helical CT and a ten-phase 4D-CT scan. A commercial treatment planning system was used to create four IMRT plans for each patient. The first two plans were based on the GTV as contoured on the free breathing helical CT set, with a GTV to PTV expansion of 1.5 cm and 2.0 cm, respectively. The third plan was based on the ITV, a composite volume formed by the union of the CTV volumes contoured on free breathing helical CT, end-of-inhale (EOI) and end-of-exhale (EOE) 4D-CT. The fourth plan was based on GTV contoured on the EOE 4D-CT. The prescribed dose was 60 Gy for all four plans. Fluence maps and beam setup parameters of the IMRT plans were used by the Monte Carlo dose calculation engine MCSIM for absolute dose calculation on both the free breathing CT and 4D-CT data. CT deformable registration between the breathing phases was performed to estimate the motion trajectory for both the tumour and healthy tissue. Then, a composite dose distribution over the whole breathing cycle was calculated as a final estimate of the delivered dose. EUD values were computed on the basis of the composite dose for all four plans. For the patient with the largest motion effect, the difference in the EUD of CTV between the planed and the delivered doses was 33, 11, 1 and 0 Gy for the first, second, third and fourth plan, respectively. The number of breathing phases required for accurate dose prediction was also investigated. With the advent of 4D-CT, deformable registration and Monte Carlo simulations, it is feasible to perform an accurate calculation of the delivered dose, and compare our delivered dose with doses estimated using prior techniques.

[1]  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.

[2]  T. Guerrero,et al.  Acquiring 4D thoracic CT scans using a multislice helical method. , 2004, Physics in medicine and biology.

[3]  S B Jiang,et al.  Validation of a Monte Carlo dose calculation tool for radiotherapy treatment planning , 2000, Physics in medicine and biology.

[4]  James A Purdy,et al.  Current ICRU definitions of volumes: limitations and future directions. , 2004, Seminars in radiation oncology.

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

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

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

[8]  P Keall,et al.  Dosimetric impact of geometric errors due to respiratory motion prediction on dynamic multileaf collimator-based four-dimensional radiation delivery. , 2005, Medical physics.

[9]  H Shirato,et al.  Impact of respiratory movement on the computed tomographic images of small lung tumors in three-dimensional (3D) radiotherapy. , 2000, International journal of radiation oncology, biology, physics.

[10]  George Starkschall,et al.  Semiautomated four-dimensional computed tomography segmentation using deformable models. , 2005, Medical physics.

[11]  T. Mackie,et al.  Fast free-form deformable registration via calculus of variations , 2004, Physics in medicine and biology.

[12]  A. Niemierko Reporting and analyzing dose distributions: a concept of equivalent uniform dose. , 1997, Medical physics.

[13]  M Gambaccini,et al.  Dual-energy tissue cancellation in mammography with quasi-monochromatic x-rays. , 2002, Physics in medicine and biology.

[14]  Tinsu Pan,et al.  Four-dimensional computed tomography: image formation and clinical protocol. , 2005, Medical physics.

[15]  Suresh Senan,et al.  Four-dimensional CT scans for treatment planning in stereotactic radiotherapy for stage I lung cancer. , 2004, International journal of radiation oncology, biology, physics.

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

[17]  C. Ma,et al.  A Monte Carlo dose calculation tool for radiotherapy treatment planning. , 2002, Physics in medicine and biology.

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

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

[20]  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.

[21]  Andrew Jackson,et al.  A new method of incorporating systematic uncertainties in intensity-modulated radiotherapy optimization. , 2005, Medical physics.

[22]  Joe Y. Chang,et al.  Validation of an accelerated ‘demons’ algorithm for deformable image registration in radiation therapy , 2005, Physics in medicine and biology.

[23]  Steve B Jiang,et al.  On dose distribution comparison. , 2006, Physics in medicine and biology.

[24]  M Engelsman,et al.  Impact of simple tissue inhomogeneity correction algorithms on conformal radiotherapy of lung tumours. , 2001, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[25]  Eric C Ford,et al.  Measurement of lung tumor motion using respiration-correlated CT. , 2004, International journal of radiation oncology, biology, physics.

[26]  Kawal S. Rhode,et al.  Tissue deformation and shape models in image-guided interventions: a discussion paper , 2005, Medical Image Anal..

[27]  Shinichi Shimizu,et al.  Intrafractional tumor motion: lung and liver. , 2004, Seminars in radiation oncology.

[28]  Stanley J. Rosenthal,et al.  Moving targets: detection and tracking of internal organ motion for treatment planning and patient set-up. , 2004, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[29]  Hiroki Shirato,et al.  The effect of tumor location and respiratory function on tumor movement estimated by real-time tracking radiotherapy (RTRT) system. , 2005, International journal of radiation oncology, biology, physics.

[30]  Jun Duan,et al.  Validation of target volume and position in respiratory gated CT planning and treatment. , 2003, Medical physics.

[31]  K. Brock,et al.  Accuracy of finite element model-based multi-organ deformable image registration. , 2005, Medical physics.

[32]  Sasa Mutic,et al.  Quantitation of the reconstruction quality of a four-dimensional computed tomography process for lung cancer patients. , 2005, Medical physics.

[33]  C. Ling,et al.  Respiration-correlated spiral CT: a method of measuring respiratory-induced anatomic motion for radiation treatment planning. , 2002, Medical physics.

[34]  T. Pan Comparison of helical and cine acquisitions for 4D-CT imaging with multislice CT. , 2005, Medical physics.

[35]  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.

[36]  David Sarrut,et al.  Nonrigid registration method to assess reproducibility of breath-holding with ABC in lung cancer. , 2004, International journal of radiation oncology, biology, physics.