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Neural Network-Based Range Verification for Proton Therapy Based on Prompt Gamma Emissions
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Volume 99 Number 2S Supplement 2017 from all scans were recorded to determine systematic (Ʃ) and random (s) set- up errors for initial set-up, after correction and after treatment imaging, in the left-right (X), craniocaudal (Y) and anteroposterior (Z) directions. The ITV to PTV margin (M) was also calculated for after correction and after treatment imaging, using the Van Herk formula: MZ2.5 Ʃ +0.7 s Results: A summary of patient set-up errors and ITV to PTV margins, in the three orthogonal directions, is shown in Table I. Abstract 3691; Table I
Summary of set-up errors and ITV margins
CBCT initial set-up
CBCT after correction
CBCT after treatment
Directions Ʃ(mm) s(mm) Ʃ(mm) s(mm) M(mm) Ʃ(mm) s(mm) M(mm) X Y Z
2.1 5.4 2.6
2.2 3.6 2.7
0.7 1.3 0.3
1.0 1.2 0.8
2.5 4.0 1.4
1.1 1.5 1.0
1.5 1.4 1.2
3.8 4.6 3.3
Intrafraction stability was 1.1, 0.6 and 1.0 mm (systematic) and 1.2, 0.9 and 1.1 mm (random) for X, Yand Z directions, respectively. Calculated margins do not account for target delineation and organ motion uncertainties and are consistent with our current 7 mm margins in all directions. Conclusion: Frameless SBRT can be safely administrated using 4D-CBCT. This was demonstrated with small intrafraction movements after initial setup correction using imaging guidance. Our data suggest that lung SBRT should not be delivered without image guidance to correct initial set-up uncertainties owing to the small size of the lesions treated and the large dose delivered each fraction. ITV margins can safely be kept small, allowing patients to benefit of advantages of SBRT. Author Disclosure: J. Penedo: None. J. Luna: None. M. Garcia: None. J. Olivera: None. S. Gomez-Tejedor: None. M. Rincon: None. K. Aguilar: None. I. Gomez: None. L. Sanchez: None. C.M. Diaz: None. W.A. Vasquez: None. J. Vara: None.
3692 Neural Network-Based Range Verification for Proton Therapy Based on Prompt Gamma Emissions H. Peng1 and L. Xing2; 1Stanford University School of Medicine Stanford, CA, 2Department of Radiation Oncology, Stanford University, Stanford, CA Purpose/Objective(s): Online dose monitoring in proton therapy is currently being investigated with prompt-gamma detection, which is correlated with proton range and dose deposition. We propose a novel approach based on neural network. Materials/Methods: Neural networks are very good at pattern recognition and it is able to classify any data with arbitrary accuracy given enough neurons. In our study, the features are selected to be the differences of prompt gamma profiles for different proton energies and Bragg peaks. Simulations were carried out with a clinical spot-scanning proton therapy treatment using the Geant4 V9.4p01 toolkit. The proton beams tested were mono-energetic beams at 130 MeV, 125 MeV, 120 MeV and 115 MeV. A water phantom had a dimension of 10x10x30 cm3 and a voxels size of 1x1x1 mm3. The method implemented a neural network classification model comprising 2 layers and 10 neurons. The classifier’s performances were evaluated using confusion matrix and receiver operating characteristic curves (ROCs). In total, 50 data sets were generated for each proton energy (70% training, 30% testing). Two important parameters were evaluated: kernel size (to model the response of each sensing point to emissions at different depths) and detection efficiency (to model the counting statistics and sensors). Results: Without any kernel and detection model applied, the classifier is able to classify three proton energies/ranges with 100% accuracy indicating their strong correlations with the prompt gamma profiles. No significant difference is found among different kernel sizes ranging
between 1 mm and 16 mm, which relives our burden in optimizing the location of sensors as well as collimators in-between sensors in order to maintain spatial information of dose profiles. In addition, the accuracy of the classifier is found to be dependent on the detection efficiency. For a kernel size of 8 mm, the accuracy is found to be w78% for a detection efficiency of w0.1%. We believe that such efficiency is achievable for a clinical proton therapy system, relying on a number of physical factors such as beam intensity, irradiation time, the design of radiation detectors and collimators. Conclusion: The feasibility of proton range verification using neural network was proved. The proposed method will allow us to develop a costeffective online range/dose verification system for proton therapy with a limited number of sensors located at different depths. If successful, this novel system may also have potential to help improve the accuracy of patient positioning and beam condition, and ultimately allows for dosevolume adapted proton therapy. Author Disclosure: H. Peng: None. L. Xing: Honoraria; Varian Medical Systems. Royalty; Varian Medical Systems, Standard Imaging Inc..
3693 4D CBCT image guidance in liver SBRT A. Perez-Rozos, A. Roman, I. Jerez Sainz, A. Otero, Y. Lupianez, and J.A. Medina; Hospital Virgen de la Victoria, Malaga, Spain Purpose/Objective(s): To analyze 4D CBCT image guided accuracy and dose volume histograms in SBRT for liver tumors. Materials/Methods: The treatment with SBRT in hepatic cancer using 4D image guidance is analyzed in this work. Patients are inmovilized employing an arm support, knee fix and a dampening system. Respiration correlated 4D CT and diagnostic CT performed using IV contrast media are used to delineate gross tumor volume (GTV). GTVs in eight 4DCT sets and two contrast sets are combined to create an internal target volume (ITV). Comparing center of mass of GTV in every respiratory phase a dampening device is selected and a set of CT images is chosen as reference for planning and simulation. In this reference CT organs at risk are contoured and it is selected to match in the daily image guided radiotherapy (IGRT). IGRT using anatomy fiducials respiration correlated 4DCBCT with automatic registration of the planning reference CT is used to setup patient in every treatment session. Results were compared for three registration techniques: 3D CBCT using mean ITV and soft tissue registration, 4dCBCT clipbox and bone anatomy registration (4D bCBCT), and 4D soft tissue registration using a 0.5 cm mask from ITV (4D mCBCT). Results: Significant differences in ITV localization were observed between 4D mCBCT and the other modalities, up to 16.0 mm 6.0 mm (3D vector). The differences between 4D bCBCT and 4D mCBCT are closer than 4DbCBCT and 3D CBCT. These differences are larger when respiratory amplitude increase. Mean respiratory amplitude was 21.1 mm 7.0 mm when measured in simulation CT, in agreement with 20.0 7.8 mm when measured in 4D CBCT. Visual comparison of reference respiratory curve and treatment daily respiratory curve makes possible to asses reproducibility of patients and tumor position reproducibility. Interobserver variability was reduced using 4D mCBCT and soft tissue automatic registration. With this protocol we can assert that interpatient variability in dose volume histograms is due mainly to treatment planning technique and tumor volume definition, and not directly correlated with motion amplitude. Conclusion: Respiration correlated 4D CBCT with soft tissue registration improves accuracy of IGRT because of the more precise target localization in the presence of respiratory motion, allowing the comparison of reference respiratory curve with treatment respiratory curve. Moreover, this method enables the realization of 4D CBCT without external surrogates with good correlation with reference 4D CT using external sensors. Author Disclosure: A. Perez-Rozos: None. A. Roman: None. I. Jerez Sainz: None. A. Otero: None. Y. Lupianez: None. J. Medina: None.
Volume 99 Number 2S Supplement 2017 from all scans were recorded to determine systematic (Ʃ) and random (s) set- up errors for initial set-up, after correction and after treatment imaging, in the left-right (X), craniocaudal (Y) and anteroposterior (Z) directions. The ITV to PTV margin (M) was also calculated for after correction and after treatment imaging, using the Van Herk formula: MZ2.5 Ʃ +0.7 s Results: A summary of patient set-up errors and ITV to PTV margins, in the three orthogonal directions, is shown in Table I. Abstract 3691; Table I
Summary of set-up errors and ITV margins
CBCT initial set-up
CBCT after correction
CBCT after treatment
Directions Ʃ(mm) s(mm) Ʃ(mm) s(mm) M(mm) Ʃ(mm) s(mm) M(mm) X Y Z
2.1 5.4 2.6
2.2 3.6 2.7
0.7 1.3 0.3
1.0 1.2 0.8
2.5 4.0 1.4
1.1 1.5 1.0
1.5 1.4 1.2
3.8 4.6 3.3
Intrafraction stability was 1.1, 0.6 and 1.0 mm (systematic) and 1.2, 0.9 and 1.1 mm (random) for X, Yand Z directions, respectively. Calculated margins do not account for target delineation and organ motion uncertainties and are consistent with our current 7 mm margins in all directions. Conclusion: Frameless SBRT can be safely administrated using 4D-CBCT. This was demonstrated with small intrafraction movements after initial setup correction using imaging guidance. Our data suggest that lung SBRT should not be delivered without image guidance to correct initial set-up uncertainties owing to the small size of the lesions treated and the large dose delivered each fraction. ITV margins can safely be kept small, allowing patients to benefit of advantages of SBRT. Author Disclosure: J. Penedo: None. J. Luna: None. M. Garcia: None. J. Olivera: None. S. Gomez-Tejedor: None. M. Rincon: None. K. Aguilar: None. I. Gomez: None. L. Sanchez: None. C.M. Diaz: None. W.A. Vasquez: None. J. Vara: None.
3692 Neural Network-Based Range Verification for Proton Therapy Based on Prompt Gamma Emissions H. Peng1 and L. Xing2; 1Stanford University School of Medicine Stanford, CA, 2Department of Radiation Oncology, Stanford University, Stanford, CA Purpose/Objective(s): Online dose monitoring in proton therapy is currently being investigated with prompt-gamma detection, which is correlated with proton range and dose deposition. We propose a novel approach based on neural network. Materials/Methods: Neural networks are very good at pattern recognition and it is able to classify any data with arbitrary accuracy given enough neurons. In our study, the features are selected to be the differences of prompt gamma profiles for different proton energies and Bragg peaks. Simulations were carried out with a clinical spot-scanning proton therapy treatment using the Geant4 V9.4p01 toolkit. The proton beams tested were mono-energetic beams at 130 MeV, 125 MeV, 120 MeV and 115 MeV. A water phantom had a dimension of 10x10x30 cm3 and a voxels size of 1x1x1 mm3. The method implemented a neural network classification model comprising 2 layers and 10 neurons. The classifier’s performances were evaluated using confusion matrix and receiver operating characteristic curves (ROCs). In total, 50 data sets were generated for each proton energy (70% training, 30% testing). Two important parameters were evaluated: kernel size (to model the response of each sensing point to emissions at different depths) and detection efficiency (to model the counting statistics and sensors). Results: Without any kernel and detection model applied, the classifier is able to classify three proton energies/ranges with 100% accuracy indicating their strong correlations with the prompt gamma profiles. No significant difference is found among different kernel sizes ranging
between 1 mm and 16 mm, which relives our burden in optimizing the location of sensors as well as collimators in-between sensors in order to maintain spatial information of dose profiles. In addition, the accuracy of the classifier is found to be dependent on the detection efficiency. For a kernel size of 8 mm, the accuracy is found to be w78% for a detection efficiency of w0.1%. We believe that such efficiency is achievable for a clinical proton therapy system, relying on a number of physical factors such as beam intensity, irradiation time, the design of radiation detectors and collimators. Conclusion: The feasibility of proton range verification using neural network was proved. The proposed method will allow us to develop a costeffective online range/dose verification system for proton therapy with a limited number of sensors located at different depths. If successful, this novel system may also have potential to help improve the accuracy of patient positioning and beam condition, and ultimately allows for dosevolume adapted proton therapy. Author Disclosure: H. Peng: None. L. Xing: Honoraria; Varian Medical Systems. Royalty; Varian Medical Systems, Standard Imaging Inc..
3693 4D CBCT image guidance in liver SBRT A. Perez-Rozos, A. Roman, I. Jerez Sainz, A. Otero, Y. Lupianez, and J.A. Medina; Hospital Virgen de la Victoria, Malaga, Spain Purpose/Objective(s): To analyze 4D CBCT image guided accuracy and dose volume histograms in SBRT for liver tumors. Materials/Methods: The treatment with SBRT in hepatic cancer using 4D image guidance is analyzed in this work. Patients are inmovilized employing an arm support, knee fix and a dampening system. Respiration correlated 4D CT and diagnostic CT performed using IV contrast media are used to delineate gross tumor volume (GTV). GTVs in eight 4DCT sets and two contrast sets are combined to create an internal target volume (ITV). Comparing center of mass of GTV in every respiratory phase a dampening device is selected and a set of CT images is chosen as reference for planning and simulation. In this reference CT organs at risk are contoured and it is selected to match in the daily image guided radiotherapy (IGRT). IGRT using anatomy fiducials respiration correlated 4DCBCT with automatic registration of the planning reference CT is used to setup patient in every treatment session. Results were compared for three registration techniques: 3D CBCT using mean ITV and soft tissue registration, 4dCBCT clipbox and bone anatomy registration (4D bCBCT), and 4D soft tissue registration using a 0.5 cm mask from ITV (4D mCBCT). Results: Significant differences in ITV localization were observed between 4D mCBCT and the other modalities, up to 16.0 mm 6.0 mm (3D vector). The differences between 4D bCBCT and 4D mCBCT are closer than 4DbCBCT and 3D CBCT. These differences are larger when respiratory amplitude increase. Mean respiratory amplitude was 21.1 mm 7.0 mm when measured in simulation CT, in agreement with 20.0 7.8 mm when measured in 4D CBCT. Visual comparison of reference respiratory curve and treatment daily respiratory curve makes possible to asses reproducibility of patients and tumor position reproducibility. Interobserver variability was reduced using 4D mCBCT and soft tissue automatic registration. With this protocol we can assert that interpatient variability in dose volume histograms is due mainly to treatment planning technique and tumor volume definition, and not directly correlated with motion amplitude. Conclusion: Respiration correlated 4D CBCT with soft tissue registration improves accuracy of IGRT because of the more precise target localization in the presence of respiratory motion, allowing the comparison of reference respiratory curve with treatment respiratory curve. Moreover, this method enables the realization of 4D CBCT without external surrogates with good correlation with reference 4D CT using external sensors. Author Disclosure: A. Perez-Rozos: None. A. Roman: None. I. Jerez Sainz: None. A. Otero: None. Y. Lupianez: None. J. Medina: None.