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Dosimetric effect of respiratory motion prediction error on dynamic multileaf collimator based 4D radiation delivery
S230
I. J. Radiation Oncology
● Biology ● Physics
Volume 60, Number 1, Supplement, 2004
measurements by randomly selecting the phase at the start of the treatment. The phantom and platform phases and periods were tuned to ensure synchrony of phantom and platform motion and were adjusted if necessary after each beam delivery. Results: The measurements and calculations agreed to within 3% for the ion chamber and within 2 mm for film in the static case. In the case of phantom motion alone, the higher isodose lines (80 –100%) were constricted and the lower isodose lines (40 – 60%) were expanded in the direction of motion. The change in the dose distribution was quantified using 2-D dose profiles along the directions of interest. For 4 cm peak-to-peak amplitude motion, the distance between the 90% and 70% isodose lines in regions of high gradient (⬃0.5 cm) was increased to ⬃2 cm. In regions of low dose gradient (⬃1.5 cm) there was no clinically significant change in the distance between the 90% and 70% isodose lines. For motion of given amplitude the change in the dose distribution was more severe in the SI than in the LR direction. We attribute this to the steeper fall-off in dose in the SI direction in the original plans. Ion chamber measurements in the phantom motion only case varied by up to 9% from the static case. When compensatory platform motion was applied, measured ion chamber and film results agreed to within 2% and 3mm, respectively with the static case. Minor discrepancies in the measurements may be attributed to the uncertainty in the synchrony of the platform phase and period relative to the phantom. Uncertainty in measurements resulted from i) the ability to accurately synchronize the platform motion with the phantom motion and ii) the ability to tune the period of both phantom and period. We estimate the maximum error in phase and period to be 1.25 degrees and 0.1 sec, respectively. Conclusions: We have demonstrated the feasibility of adaptive couch motion to track respiratory-induced tumor motion. When the couch motion and tumor motion are synchronized, (with careful control of the couch-motion period, amplitude and phase) it is possible to eliminate the degradation of IMRT dose distributions caused by respiratory-induced tumor motion.
164
Dosimetric Effect of Respiratory Motion Prediction Error on Dynamic Multileaf Collimator Based 4D Radiation Delivery
S. Vedam,1 A. Docef,2 P. Keall1 1 Radiation Oncology, Virgina Commonwealth University, Richmond, VA, 2Electrical Engineering, Virginia Commonwealth University, Richmond, VA Purpose/Objective: Synchronization of dynamic multileaf collimator response with respiratory motion is critical to ensure the accuracy of 4D radiation delivery. In practice however, a finite time delay (response time RT) between acquisition of tumor position and multileaf collimator (MLC) response necessitates predictive models of respiratory tumor motion to synchronize radiation delivery. Predicting a complex process such as respiratory motion introduces errors that have already been quantified. However, the dosimetric effect of such prediction errors on 4D radiation delivery has not been investigated and quantification of such dosimetric effects forms the subject of this work. Materials/Methods: Conformal and IMRT plans for a lung patient were generated for AP-PA geometry at 6 and 18 MV energies. Respiratory motion data was obtained from 60 diaphragm motion recordings of 5 patients. A linear adaptive filter was employed to predict the position of the tumor for 0 – 0.6 seconds RT. Prediction error was defined as the absolute difference between predicted and actual positions at each diaphragm position. Distributions of prediction error and actual respiratory motion were obtained according to breathing training type (free breathing/audio instructions/visual feedback). Individual dose distributions were convolved with prediction error and respiratory motion distributions. The dosimetric effect of prediction error, along a central plane dose profile was determined as a function of beam energy (6/18 MV), treatment type (conformal/ IMRT), beam direction (AP/PA), breathing training modality (free breathing/audio instructions/visual feedback) and RT (0 – 0.6 seconds). Dose difference was quantified by calculating the maximum and RMS dose error values between each set of convolved and original distributions, expressed as a percentage of maximum actual dose in the original distribution. Results: In general, as RT increased, both maximum and RMS dose error increased, indicating smaller delivery errors due to prediction at smaller RT (RMS error dose: 0.2, 0.7, 1.2% at 0.2, 0.4, 0.6 seconds RT for 6 MV conformal and 0.4, 1.2, 2.1% at 0.2, 0.4, 0.6 seconds RT for 6 MV IMRT beam). Direct convolution of the corresponding dose distribution with actual respiratory motion yielded an RMS dose error of 2.2% for the conformal beam and 4% for the IMRT beam. Maximum and RMS dose error were smaller for 18 MV beams (3.8, 0.9% respectively at RT⫽0.6 seconds, free breathing) than 6 MV beams (4.8, 1.2% of respectively at RT⫽0.6 seconds, free breathing). Such a decrease is attributed to the larger electron spread in lung for the 18 MV beam energy. Effects of prediction error were more pronounced for IMRT beams as compared to conformal beams (maximum and RMS dose errors of 4.8%, 1.2% for conformal vs. 11.4%, 2.1% for IMRT, 6MV, free breathing, 0.6 seconds RT), due to non-uniform IMRT dose distributions. Breathing training reflected similar trends for dosimetric effects due to prediction error. However, due to larger prediction error with increased respiration amplitude associated with audio instructions, maximum dose errors of up to 8% (conformal) and 16% (IMRT) were observed. Conclusions: Predicting respiratory motion during 4D radiation delivery introduces dosimetric errors that are dependent on several factors, most importantly the response time. Even for relatively small response times of 0.6 seconds into the future, dosimetric errors due to prediction could approach delivery errors when respiratory motion is not accounted for at all. With current prediction methods, response times for 4D radiation delivery should be less than 0.4 seconds. Alternatively, better prediction models would facilitate 4D radiation delivery for longer response times.
165
A Study of Interplay Between Dynamics of Breathing Motion and Segmental MLC-IMRT Delivery for Motion-Pattern-Based 4D Lung IMRT
S. X. Chang,1 T. Cullip,1 D. Schulman,1 G. Mageras,2 P. Keall,3 S. Joshi1 1 Radiation Oncology, University of North Carolina, Chapel Hill, NC, 2Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, 3Radiation Oncology, Medical Colledge of Virginia Hospitals, Richmond, VA Purpose/Objective: There is a known interplay between intra-fractional organ motion and dynamic MLC IMRT delivery for ungated radiation delivery techniques. Motion-pattern-based 4D IMRT can optimize the cumulative dosimetry throughout the motion cycle while ignoring the interplay between the dynamics of MLC treatment and organ motion. A recent work by Jiang
I. J. Radiation Oncology
● Biology ● Physics
Volume 60, Number 1, Supplement, 2004
measurements by randomly selecting the phase at the start of the treatment. The phantom and platform phases and periods were tuned to ensure synchrony of phantom and platform motion and were adjusted if necessary after each beam delivery. Results: The measurements and calculations agreed to within 3% for the ion chamber and within 2 mm for film in the static case. In the case of phantom motion alone, the higher isodose lines (80 –100%) were constricted and the lower isodose lines (40 – 60%) were expanded in the direction of motion. The change in the dose distribution was quantified using 2-D dose profiles along the directions of interest. For 4 cm peak-to-peak amplitude motion, the distance between the 90% and 70% isodose lines in regions of high gradient (⬃0.5 cm) was increased to ⬃2 cm. In regions of low dose gradient (⬃1.5 cm) there was no clinically significant change in the distance between the 90% and 70% isodose lines. For motion of given amplitude the change in the dose distribution was more severe in the SI than in the LR direction. We attribute this to the steeper fall-off in dose in the SI direction in the original plans. Ion chamber measurements in the phantom motion only case varied by up to 9% from the static case. When compensatory platform motion was applied, measured ion chamber and film results agreed to within 2% and 3mm, respectively with the static case. Minor discrepancies in the measurements may be attributed to the uncertainty in the synchrony of the platform phase and period relative to the phantom. Uncertainty in measurements resulted from i) the ability to accurately synchronize the platform motion with the phantom motion and ii) the ability to tune the period of both phantom and period. We estimate the maximum error in phase and period to be 1.25 degrees and 0.1 sec, respectively. Conclusions: We have demonstrated the feasibility of adaptive couch motion to track respiratory-induced tumor motion. When the couch motion and tumor motion are synchronized, (with careful control of the couch-motion period, amplitude and phase) it is possible to eliminate the degradation of IMRT dose distributions caused by respiratory-induced tumor motion.
164
Dosimetric Effect of Respiratory Motion Prediction Error on Dynamic Multileaf Collimator Based 4D Radiation Delivery
S. Vedam,1 A. Docef,2 P. Keall1 1 Radiation Oncology, Virgina Commonwealth University, Richmond, VA, 2Electrical Engineering, Virginia Commonwealth University, Richmond, VA Purpose/Objective: Synchronization of dynamic multileaf collimator response with respiratory motion is critical to ensure the accuracy of 4D radiation delivery. In practice however, a finite time delay (response time RT) between acquisition of tumor position and multileaf collimator (MLC) response necessitates predictive models of respiratory tumor motion to synchronize radiation delivery. Predicting a complex process such as respiratory motion introduces errors that have already been quantified. However, the dosimetric effect of such prediction errors on 4D radiation delivery has not been investigated and quantification of such dosimetric effects forms the subject of this work. Materials/Methods: Conformal and IMRT plans for a lung patient were generated for AP-PA geometry at 6 and 18 MV energies. Respiratory motion data was obtained from 60 diaphragm motion recordings of 5 patients. A linear adaptive filter was employed to predict the position of the tumor for 0 – 0.6 seconds RT. Prediction error was defined as the absolute difference between predicted and actual positions at each diaphragm position. Distributions of prediction error and actual respiratory motion were obtained according to breathing training type (free breathing/audio instructions/visual feedback). Individual dose distributions were convolved with prediction error and respiratory motion distributions. The dosimetric effect of prediction error, along a central plane dose profile was determined as a function of beam energy (6/18 MV), treatment type (conformal/ IMRT), beam direction (AP/PA), breathing training modality (free breathing/audio instructions/visual feedback) and RT (0 – 0.6 seconds). Dose difference was quantified by calculating the maximum and RMS dose error values between each set of convolved and original distributions, expressed as a percentage of maximum actual dose in the original distribution. Results: In general, as RT increased, both maximum and RMS dose error increased, indicating smaller delivery errors due to prediction at smaller RT (RMS error dose: 0.2, 0.7, 1.2% at 0.2, 0.4, 0.6 seconds RT for 6 MV conformal and 0.4, 1.2, 2.1% at 0.2, 0.4, 0.6 seconds RT for 6 MV IMRT beam). Direct convolution of the corresponding dose distribution with actual respiratory motion yielded an RMS dose error of 2.2% for the conformal beam and 4% for the IMRT beam. Maximum and RMS dose error were smaller for 18 MV beams (3.8, 0.9% respectively at RT⫽0.6 seconds, free breathing) than 6 MV beams (4.8, 1.2% of respectively at RT⫽0.6 seconds, free breathing). Such a decrease is attributed to the larger electron spread in lung for the 18 MV beam energy. Effects of prediction error were more pronounced for IMRT beams as compared to conformal beams (maximum and RMS dose errors of 4.8%, 1.2% for conformal vs. 11.4%, 2.1% for IMRT, 6MV, free breathing, 0.6 seconds RT), due to non-uniform IMRT dose distributions. Breathing training reflected similar trends for dosimetric effects due to prediction error. However, due to larger prediction error with increased respiration amplitude associated with audio instructions, maximum dose errors of up to 8% (conformal) and 16% (IMRT) were observed. Conclusions: Predicting respiratory motion during 4D radiation delivery introduces dosimetric errors that are dependent on several factors, most importantly the response time. Even for relatively small response times of 0.6 seconds into the future, dosimetric errors due to prediction could approach delivery errors when respiratory motion is not accounted for at all. With current prediction methods, response times for 4D radiation delivery should be less than 0.4 seconds. Alternatively, better prediction models would facilitate 4D radiation delivery for longer response times.
165
A Study of Interplay Between Dynamics of Breathing Motion and Segmental MLC-IMRT Delivery for Motion-Pattern-Based 4D Lung IMRT
S. X. Chang,1 T. Cullip,1 D. Schulman,1 G. Mageras,2 P. Keall,3 S. Joshi1 1 Radiation Oncology, University of North Carolina, Chapel Hill, NC, 2Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, 3Radiation Oncology, Medical Colledge of Virginia Hospitals, Richmond, VA Purpose/Objective: There is a known interplay between intra-fractional organ motion and dynamic MLC IMRT delivery for ungated radiation delivery techniques. Motion-pattern-based 4D IMRT can optimize the cumulative dosimetry throughout the motion cycle while ignoring the interplay between the dynamics of MLC treatment and organ motion. A recent work by Jiang