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Impact of Different Sampling Interval in Training Data Acquisition on the Prediction Accuracy in Surrogate Signal-based Dynamic Tumor Tracking With a Gimbaled Linac
Poster Viewing Session E575
Volume 93 Number 3S Supplement 2015 Materials/Methods: The DCS is a new technology that can improve the lateral dose conformity for low energy (< 185 MeV) pencil beams in SS proton therapy. The DCS exploits the property that a collimation system which shields the entire field-of-view at all points in time, such as an aperture or MLC, is not required for SS proton therapy. Instead, the lateral dose distribution can be defined by moving trimmer blades which intercept the pencil beam as it arrives at the edge of the target. The main components of the DCS include two pairs of orthogonal trimmer blades, each controlled by a high performance linear motor capable of 2G of acceleration and mm positional accuracy. By intercepting the proton pencil beam at the edge of the target, the pencil beam dose distribution becomes asymmetric with a sharper falloff outside of the target. To assess the dosimetric gains that may be expected from such a device, single energy layer treatment plans were generated for 2D target shapes over a range of spot sizes from 3-9 mm. A depth of 38.75 mm was selected for the 2D target which corresponded to a 125 MeV proton beam after passing through a 7.5 g/cm2 range shifter. To evaluate the effectiveness of stacking multiple collimated energy layers, the 2D study was further expanded to 3D targets and treatment plans were generated for 5 patient datasets, all of which were brain patients previously treated with proton therapy. The beam parameters used to generate these 3D plans with the DCS were representative of current clinical equipment, with spot size decreasing from 8 to 4.5 mm as energy increased from 97.5 to 185 MeV. The improvement of adding the DCS was also compared to the improvement achievable when reducing the initial spot size by w33% for these brain treatments, simulating improved proton beam optics for all beam energies. All plans (2D and 3D) were normalized for equivalent target coverage. Results: Compared to un-collimated treatment plans, the plans created with the DCS yielded a reduction in the mean dose to normal tissue surrounding the 2D target of 26.2-40.6% for spot sizes of 3-9 mm, respectively. For the patient datasets, the reductions of the mean dose to a 10 mm ring surrounding the target were 3.3-9.8%. The reduction in the mean dose to the 10 mm ring for the same patients when reducing the initial spot size by 33% was 9.0-13.3%. Conclusion: The addition of the DCS offers an alternative to reducing spot size and may be a valuable solution for sites currently treating with SS proton therapy. Author Disclosure: D. Hyer: Research Grant; IBA. Patent/License Fees/ Copyright; IBA. Member of executive committee; Missouri River Valley Chapter of the AAPM. D. Wang: Research Grant; IBA. Patent/License Fees/Copyright; IBA. Member of executive committee; Missouri River Valley Chapter of the AAPM. A. Moignier: Research Grant; IBA. E. Gelover: Research Grant; IBA. L. Lin: None. M.L. Kirk: None. T.D. Solberg: Partnership; Global Radiosurgery Services. R.T. Flynn: Research Grant; IBA. Patent/License Fees/Copyright; IBA. Founder and Chief Technical Officer; pxAlpha.
Oncology and Image-applied Therapy, Kyoto University Graduate School of Medicine, Kyoto 606-8507, Japan, 2Kyoto University Graduate School of Medicine, Kyoto 606-8507, Japan Purpose/Objective(s): We verified the impact of different sampling interval (SI) in training data acquisition on the prediction accuracy in surrogate signal-based dynamic tumor-tracking (DTT) irradiation. Materials/Methods: This study enrolled the 11 sets of respiratory motions from six lung, three liver, and two pancreas cancer patients who underwent DTT irradiation with a gimbaled linear accelerator. The respiratory motion of the tumor exceeded 10 mm. The time between first respiratory motion (R1) collection and second one (R2) was 13.23.7 min. The in-house developed four-axis moving phantom was used to reproduce the 3D target and 1D surrogate motions. The prediction accuracies were estimated in eight scenarios with different SI of 80, 320, 500, and 1000 ms in training data acquisition. The prediction errors were calculated using target positions predicted from log files and ones detected on monitoring images for 30 s. Monitoring images were acquired every 500 ms. The jmeanj+2SD of the prediction errors were calculated for each scenario. Results: Table 1 summarizes the prediction errors in the left-right (LR), cranio-caudal (CC), and anterior-posterior (AP) directions. The prediction errors were (LR, CC, AP) Z (0.70.3 mm, 1.30.6 mm, 0.80.3 mm) with 80 ms SI and (0.60.3 mm, 1.40.7 mm, 0.80.3 mm) with 320 ms SI for R1. There was no significant difference in the prediction errors between 80 and 320 ms SIs (p Z 0.303, 0.304, and 0.988 for LR, CC, and AP directions). Changing the respiratory motion from R1 to R2, the prediction errors increased by 2.8 and 3.3 mm on average with 80 and 320 ms SIs in the CC direction, respectively; however, no significant difference was observed (p Z 0.140). Reconstructing the prediction model reduced the prediction errors to within 1.7 mm on average in various SIs. There were no significant differences among various SIs in training data acquisition in all directions (p>0.1). Conclusion: This study demonstrated that the prediction accuracy was substantially high even with low frequency in training data acquisition. Author Disclosure: N. Mukumoto: None. M. Nakamura: None. M. Akimoto: None. Y. Miyabe: None. K. Yokota: None. Y. Matsuo: None. T. Mizowaki: Research Grant; The Ministry of Education, Culture, Sports, Science, and Technology, Japan, Misugikai Medical Corporation. M. Hiraoka: Research Grant; Varian Medical Systems, Inc., Mitsubishi Heavy Industries, Ltd.
3444 The Quality of VMAT Plans for Spine SABR According to the Collimator Angle S. Son, D.H. Kim, and J.K. Mun; Seoul National University Hospital, Seoul, South Korea
3443 Impact of Different Sampling Interval in Training Data Acquisition on the Prediction Accuracy in Surrogate Signal-based Dynamic Tumor Tracking With a Gimbaled Linac N. Mukumoto,1 M. Nakamura,1 M. Akimoto,1 Y. Miyabe,1 K. Yokota,1 Y. Matsuo,2 T. Mizowaki,2 and M. Hiraoka2; 1Department of Radiation
Purpose/Objective(s): The purpose of this study was to evaluate the quality of volumetric modulated arc therapy (VMAT) plans for stereotactic
Poster Viewing Abstracts 3443; Table 1 Prediction errors for 11 sets of respiratory motions under the eight scenarios of various sampling intervals in training data acquisition in the LR, CC, and AP directions. S1 Sampling interval in training data acquisition [ms] Respiratory motion For training For evaluation LR (mm) CC (mm) AP (mm)
S2
S3
S4
S5
S6
S7
S8
80
320
80
320
80
320
500
1000
R1 R1 0.7 0.3 1.3 0.6 0.8 0.3
R1 R1 0.6 0.3 1.4 0.7 0.8 0.3
R1 R2 1.5 0.9 4.1 2.3 2.3 1.8
R1 R2 1.5 1.0 4.7 2.9 2.1 1.3
R2 R2 0.6 0.3 1.6 0.6 0.7 0.4
R2 R2 0.7 0.4 1.7 0.7 0.9 0.4
R2 R2 0.6 0.4 1.5 0.5 0.7 0.4
R2 R2 0.6 0.4 1.7 0.7 0.7 0.3
Abbreviations: S*Zscenario number, R1Zrespiratory motion collected in early phase, R2Zrespiratory motion collected in later phase, LRZleft-right; CCZcranio-caudal, APZanterior-posterior.
Volume 93 Number 3S Supplement 2015 Materials/Methods: The DCS is a new technology that can improve the lateral dose conformity for low energy (< 185 MeV) pencil beams in SS proton therapy. The DCS exploits the property that a collimation system which shields the entire field-of-view at all points in time, such as an aperture or MLC, is not required for SS proton therapy. Instead, the lateral dose distribution can be defined by moving trimmer blades which intercept the pencil beam as it arrives at the edge of the target. The main components of the DCS include two pairs of orthogonal trimmer blades, each controlled by a high performance linear motor capable of 2G of acceleration and mm positional accuracy. By intercepting the proton pencil beam at the edge of the target, the pencil beam dose distribution becomes asymmetric with a sharper falloff outside of the target. To assess the dosimetric gains that may be expected from such a device, single energy layer treatment plans were generated for 2D target shapes over a range of spot sizes from 3-9 mm. A depth of 38.75 mm was selected for the 2D target which corresponded to a 125 MeV proton beam after passing through a 7.5 g/cm2 range shifter. To evaluate the effectiveness of stacking multiple collimated energy layers, the 2D study was further expanded to 3D targets and treatment plans were generated for 5 patient datasets, all of which were brain patients previously treated with proton therapy. The beam parameters used to generate these 3D plans with the DCS were representative of current clinical equipment, with spot size decreasing from 8 to 4.5 mm as energy increased from 97.5 to 185 MeV. The improvement of adding the DCS was also compared to the improvement achievable when reducing the initial spot size by w33% for these brain treatments, simulating improved proton beam optics for all beam energies. All plans (2D and 3D) were normalized for equivalent target coverage. Results: Compared to un-collimated treatment plans, the plans created with the DCS yielded a reduction in the mean dose to normal tissue surrounding the 2D target of 26.2-40.6% for spot sizes of 3-9 mm, respectively. For the patient datasets, the reductions of the mean dose to a 10 mm ring surrounding the target were 3.3-9.8%. The reduction in the mean dose to the 10 mm ring for the same patients when reducing the initial spot size by 33% was 9.0-13.3%. Conclusion: The addition of the DCS offers an alternative to reducing spot size and may be a valuable solution for sites currently treating with SS proton therapy. Author Disclosure: D. Hyer: Research Grant; IBA. Patent/License Fees/ Copyright; IBA. Member of executive committee; Missouri River Valley Chapter of the AAPM. D. Wang: Research Grant; IBA. Patent/License Fees/Copyright; IBA. Member of executive committee; Missouri River Valley Chapter of the AAPM. A. Moignier: Research Grant; IBA. E. Gelover: Research Grant; IBA. L. Lin: None. M.L. Kirk: None. T.D. Solberg: Partnership; Global Radiosurgery Services. R.T. Flynn: Research Grant; IBA. Patent/License Fees/Copyright; IBA. Founder and Chief Technical Officer; pxAlpha.
Oncology and Image-applied Therapy, Kyoto University Graduate School of Medicine, Kyoto 606-8507, Japan, 2Kyoto University Graduate School of Medicine, Kyoto 606-8507, Japan Purpose/Objective(s): We verified the impact of different sampling interval (SI) in training data acquisition on the prediction accuracy in surrogate signal-based dynamic tumor-tracking (DTT) irradiation. Materials/Methods: This study enrolled the 11 sets of respiratory motions from six lung, three liver, and two pancreas cancer patients who underwent DTT irradiation with a gimbaled linear accelerator. The respiratory motion of the tumor exceeded 10 mm. The time between first respiratory motion (R1) collection and second one (R2) was 13.23.7 min. The in-house developed four-axis moving phantom was used to reproduce the 3D target and 1D surrogate motions. The prediction accuracies were estimated in eight scenarios with different SI of 80, 320, 500, and 1000 ms in training data acquisition. The prediction errors were calculated using target positions predicted from log files and ones detected on monitoring images for 30 s. Monitoring images were acquired every 500 ms. The jmeanj+2SD of the prediction errors were calculated for each scenario. Results: Table 1 summarizes the prediction errors in the left-right (LR), cranio-caudal (CC), and anterior-posterior (AP) directions. The prediction errors were (LR, CC, AP) Z (0.70.3 mm, 1.30.6 mm, 0.80.3 mm) with 80 ms SI and (0.60.3 mm, 1.40.7 mm, 0.80.3 mm) with 320 ms SI for R1. There was no significant difference in the prediction errors between 80 and 320 ms SIs (p Z 0.303, 0.304, and 0.988 for LR, CC, and AP directions). Changing the respiratory motion from R1 to R2, the prediction errors increased by 2.8 and 3.3 mm on average with 80 and 320 ms SIs in the CC direction, respectively; however, no significant difference was observed (p Z 0.140). Reconstructing the prediction model reduced the prediction errors to within 1.7 mm on average in various SIs. There were no significant differences among various SIs in training data acquisition in all directions (p>0.1). Conclusion: This study demonstrated that the prediction accuracy was substantially high even with low frequency in training data acquisition. Author Disclosure: N. Mukumoto: None. M. Nakamura: None. M. Akimoto: None. Y. Miyabe: None. K. Yokota: None. Y. Matsuo: None. T. Mizowaki: Research Grant; The Ministry of Education, Culture, Sports, Science, and Technology, Japan, Misugikai Medical Corporation. M. Hiraoka: Research Grant; Varian Medical Systems, Inc., Mitsubishi Heavy Industries, Ltd.
3444 The Quality of VMAT Plans for Spine SABR According to the Collimator Angle S. Son, D.H. Kim, and J.K. Mun; Seoul National University Hospital, Seoul, South Korea
3443 Impact of Different Sampling Interval in Training Data Acquisition on the Prediction Accuracy in Surrogate Signal-based Dynamic Tumor Tracking With a Gimbaled Linac N. Mukumoto,1 M. Nakamura,1 M. Akimoto,1 Y. Miyabe,1 K. Yokota,1 Y. Matsuo,2 T. Mizowaki,2 and M. Hiraoka2; 1Department of Radiation
Purpose/Objective(s): The purpose of this study was to evaluate the quality of volumetric modulated arc therapy (VMAT) plans for stereotactic
Poster Viewing Abstracts 3443; Table 1 Prediction errors for 11 sets of respiratory motions under the eight scenarios of various sampling intervals in training data acquisition in the LR, CC, and AP directions. S1 Sampling interval in training data acquisition [ms] Respiratory motion For training For evaluation LR (mm) CC (mm) AP (mm)
S2
S3
S4
S5
S6
S7
S8
80
320
80
320
80
320
500
1000
R1 R1 0.7 0.3 1.3 0.6 0.8 0.3
R1 R1 0.6 0.3 1.4 0.7 0.8 0.3
R1 R2 1.5 0.9 4.1 2.3 2.3 1.8
R1 R2 1.5 1.0 4.7 2.9 2.1 1.3
R2 R2 0.6 0.3 1.6 0.6 0.7 0.4
R2 R2 0.7 0.4 1.7 0.7 0.9 0.4
R2 R2 0.6 0.4 1.5 0.5 0.7 0.4
R2 R2 0.6 0.4 1.7 0.7 0.7 0.3
Abbreviations: S*Zscenario number, R1Zrespiratory motion collected in early phase, R2Zrespiratory motion collected in later phase, LRZleft-right; CCZcranio-caudal, APZanterior-posterior.