First demonstration of intrafractional tumor-tracked irradiation using 2D phantom MR images on a prototype linac-MR.

PURPOSE To demonstrate intrafractional MR tumor tracking using a prototype linac-MR by delivering radiation to a moving target undergoing simulated tumor motions. METHODS A prototype linac-MR at the Cross Cancer Institute was used for intrafractional MR imaging and simultaneous beam delivery. A Varian 52-leaf MK-II multileaf collimator (MLC) was used for beam collimation. The authors used an inhouse built MR compatible motion phantom to simulate tumor motions during tracking with two different motion patterns (sine and modified cosine). Gafchromic film was inserted in the phantom to measure radiation exposure, and this film measurement was converted to dose (cGy) for further analysis. The authors demonstrated intrafractional tracking in various scenarios: [Scenario 0 (S0)] no phantom motion + no beam margin, (S1) no phantom motion + maximum beam margin, (S2) phantom motion + no beam margin, (S3) S2 + MLC tracking, and (S4) S3 + motion prediction. S0 emulates a perfect tumor tracking scenario, and its result was used as a "gold-standard" to evaluate tracking accuracy from other scenarios. The authors compared (1) time difference in phantom and MLC motion curves in S3 and S4, and (2) dose profiles (50% beam width, 80%-20% penumbra width) from scenarios S1-S4 to S0. RESULTS In S4, no observable time difference exists between the phantom and MLC motion curves, indicating that MLC tracks phantom motion accurately. Comparing S4 to S0, 50% beam width reveals minimal differences of < 0.5 mm, while the increase in 80%-20% penumbra width is limited to 0.4 and 1.7 mm in the sine and modified cosine patterns, respectively. CONCLUSIONS The authors report the first demonstration of intrafractional tumor tracking using 2D MR images. During 2 min of tracking, the authors delivered highly conformal dose to a moving target that simulates tumor motions. Compared to static target irradiation, the 50% beam width remains essentially the same (within 0.5 mm), with an increase in 80%-20% penumbra width of less than 1.7 mm in moving target irradiation. These results illustrate potential dosimetric advantages of intrafractional MR tumor tracking in treating mobile tumors as shown for the phantom case.

[1]  Uwe Oelfke,et al.  Electromagnetic real-time tumor position monitoring and dynamic multileaf collimator tracking using a Siemens 160 MLC: geometric and dosimetric accuracy of an integrated system. , 2011, International journal of radiation oncology, biology, physics.

[2]  Tufve Nyholm,et al.  Treatment planning using MRI data: an analysis of the dose calculation accuracy for different treatment regions , 2010, Radiation oncology.

[3]  Steve B. Jiang,et al.  Internal-external correlation investigations of respiratory induced motion of lung tumors. , 2007, Medical physics.

[4]  Koichi Yamazaki,et al.  Real‐time tumor‐tracking radiation therapy for lung carcinoma by the aid of insertion of a gold marker using bronchofiberscopy , 2002, Cancer.

[5]  Margrit Betke,et al.  The correlation between internal and external markers for abdominal tumors: implications for respiratory gating. , 2003, International journal of radiation oncology, biology, physics.

[6]  Paul J Keall,et al.  First demonstration of combined kV/MV image-guided real-time dynamic multileaf-collimator target tracking. , 2009, International journal of radiation oncology, biology, physics.

[7]  Herbert Cattell,et al.  Toward submillimeter accuracy in the management of intrafraction motion: the integration of real-time internal position monitoring and multileaf collimator target tracking. , 2009, International journal of radiation oncology, biology, physics.

[8]  J H Goodband,et al.  A comparison of neural network approaches for on-line prediction in IGRT. , 2008, Medical physics.

[9]  Shinichi Shimizu,et al.  Registration accuracy and possible migration of internal fiducial gold marker implanted in prostate and liver treated with real-time tumor-tracking radiation therapy (RTRT). , 2002, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[10]  Steve B. Jiang,et al.  The management of respiratory motion in radiation oncology report of AAPM Task Group 76. , 2006, Medical physics.

[11]  Holger R. Maier,et al.  The effect of internal parameters and geometry on the performance of back-propagation neural networks: an empirical study , 1998 .

[12]  John V Frangioni,et al.  New technologies for human cancer imaging. , 2008, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[13]  B. Fallone,et al.  Patient specific treatment verifications for helical tomotherapy treatment plans. , 2005, Medical physics.

[14]  B. Fallone,et al.  Radio frequency noise from an MLC: a feasibility study of the use of an MLC for linac-MR systems , 2010, Physics in medicine and biology.

[15]  J Yun,et al.  Brushed permanent magnet DC MLC motor operation in an external magnetic field. , 2010, Medical physics.

[16]  Cynthia Ménard,et al.  Imaging in Radiation Oncology: A Perspective , 2010, The oncologist.

[17]  Satyapal Rathee,et al.  An artificial neural network (ANN)-based lung-tumor motion predictor for intrafractional MR tumor tracking. , 2012, Medical physics.

[18]  J. Wild,et al.  Lung morphology assessment with balanced steady-state free precession MR imaging compared with CT. , 2012, Radiology.

[19]  S P M Crijns,et al.  Proof of concept of MRI-guided tracked radiation delivery: tracking one-dimensional motion , 2012, Physics in medicine and biology.

[20]  Hans-Peter Meinzer,et al.  Quantification of lung tumor volume and rotation at 3D dynamic parallel MR imaging with view sharing: preliminary results. , 2006, Radiology.

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

[22]  T. Landberg,et al.  What margins should be added to the clinical target volume in radiotherapy treatment planning for lung cancer? , 1998, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[23]  Y. Tsunashima,et al.  Correlation between the respiratory waveform measured using a respiratory sensor and 3D tumor motion in gated radiotherapy. , 2004, International journal of radiation oncology, biology, physics.

[24]  Steve Webb,et al.  Dosimetric investigation of lung tumor motion compensation with a robotic respiratory tracking system: an experimental study. , 2008, Medical physics.

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

[26]  Uwe Oelfke,et al.  Real-time tumor tracking: automatic compensation of target motion using the Siemens 160 MLC. , 2010, Medical physics.

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

[28]  Paul J Keall,et al.  Integration of real-time internal electromagnetic position monitoring coupled with dynamic multileaf collimator tracking: an intensity-modulated radiation therapy feasibility study. , 2009, International journal of radiation oncology, biology, physics.

[29]  S Nill,et al.  The comparative performance of four respiratory motion predictors for real-time tumour tracking , 2011, Physics in medicine and biology.

[30]  R. Mohan,et al.  Quantifying the predictability of diaphragm motion during respiration with a noninvasive external marker. , 2003, Medical physics.

[31]  Etienne Barnard,et al.  Avoiding false local minima by proper initialization of connections , 1992, IEEE Trans. Neural Networks.

[32]  Satyapal Rathee,et al.  Evaluation of a lung tumor autocontouring algorithm for intrafractional tumor tracking using low-field MRI: a phantom study. , 2012, Medical physics.

[33]  P. Keall,et al.  Four-dimensional IMRT treatment planning using a DMLC motion-tracking algorithm , 2009, Physics in medicine and biology.

[34]  B. Fallone,et al.  First MR images obtained during megavoltage photon irradiation from a prototype integrated linac-MR system. , 2009, Medical physics.

[35]  Paul Keall,et al.  Real-time DMLC IMRT delivery for mobile and deforming targets. , 2005, Medical physics.

[36]  Gregory C Sharp,et al.  Speed and amplitude of lung tumor motion precisely detected in four-dimensional setup and in real-time tumor-tracking radiotherapy. , 2006, International journal of radiation oncology, biology, physics.

[37]  Hans-Ulrich Kauczor,et al.  Measurement of tumor diameter-dependent mobility of lung tumors by dynamic MRI. , 2004, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[38]  Koichi Yamazaki,et al.  Insertion and fixation of fiducial markers for setup and tracking of lung tumors in radiotherapy. , 2005, International journal of radiation oncology, biology, physics.

[39]  V S Khoo,et al.  New developments in MRI for target volume delineation in radiotherapy. , 2006, The British journal of radiology.

[40]  E. Larsen,et al.  A method for incorporating organ motion due to breathing into 3D dose calculations. , 1999, Medical physics.

[41]  R D Franich,et al.  Robust calculation of effective atomic numbers: the Auto-Z(eff) software. , 2012, Medical physics.