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Quantifying the Targeting Accuracy of Liver Robotic Radiosurgery
Oral Scientific Sessions S117
Volume 93 Number 3S Supplement 2015 generated with similar target coverage and optimizations. The extent of normal tissue sparing was evaluated for the liver, lung, and small bowel with a two-tailed, paired sample, t-test utilized to detect differences in organ-at-risk (OAR) dose reduction. Results: Compared to plans without motion management, abdominal compression reduced the PTV volume in all patients by mean of 40.7 cm3, or 28.3% (P Z .008). The mean liver dose decreased by 12.8% (11.2 versus 12.8 Gy; P Z .004). If abdominal compression was not used, 2 patients (28.6%) would have required prescription dose deescalation from 50 Gy per RTOG 1112 based on mean liver dose >13 Gy; however, use of abdominal compression in these patients decreased mean liver dose, allowing criteria to be met for using a prescription dose of 50 Gy. The V50, V21, and V10 of the liver were lessened by an average of 12.1, 60.7, and 92.2 cm3, respectively, which equated to absolute (relative) reductions of 0.8% (25.0%), 3.4% (18.7%), and 5.0% (12.4%) (P < .04). No significant difference in small bowel dose was demonstrated secondary to a predominance of peripheral liver lesions. The average absolute (relative) lung V20 was reduced by 35 cm3 (39.5%) (P Z .05). Conclusion: Use of an abdominal compression belt can significantly reduce dose to the normal liver in patients receiving SBRT. Although this study primarily evaluated dosimetric differences in the treatment of peripheral liver tumors, abdominal compression may be particularly beneficial for central tumors to decrease dose to the duodenum and small bowel. Author Disclosure: J.W. Snider: None. J. Molitoris: None. B. Zhang: None. M.D. Chuong: None.
266 Quantifying the Targeting Accuracy of Liver Robotic Radiosurgery J. Winter,1 A. Swaminath,2 R.K.W. Wong,3 and T. Chow1; 1Juravinski Cancer Center, Hamilton, ON, Canada, 2Juravinski Cancer Centre, Hamilton, ON, Canada, 3McMaster University, Hamilton, ON, Canada Purpose/Objective(s): To estimate random uncertainties in robotic radiosurgical treatment of liver lesions using an online respiratory management system linking internal target position with an external surrogate. We focused on two key components of uncertainty: 1) residual position errors in the modeled target position based on the external surrogate and 2) residual position errors in the prediction algorithm employed to account for the 115 ms inherent temporal system lag. Materials/Methods: To provide quantitative estimates of positioning errors, we retrospectively reviewed log files generated for twenty-nine liver patients treated on a radiosurgical system. Motion tracking was achieved using orthogonal x-ray images to localize internal fiducials combined with optical tracking of the patient’s abdomen. A correlation model links target position with the external optical surrogate for dynamic robotic positioning of the linear accelerator throughout breathing, with periodic x-ray images to confirm the model. For each patient treatment fraction, we isolated x-ray images collected immediately prior to beam delivery and extracted the correlation error, which is the relative distance between the model-estimated target position and the actual position measured on the x-ray image. In addition, at each treatment image acquisition, we quantified the prediction error as the mean absolute difference between predicted position and the actual position reached after 115 ms. Last, we investigated potential correlations between breathing amplitude and the magnitude of tracking errors. Results: Correlation and prediction errors for all patients and treatment fractions are provided in Table 1. Cranial-caudal direction errors were greatest for both correlation and prediction errors. Prediction errors were considerably smaller than correlation errors in all directions. Prediction errors exhibited a strong significant linear correlation with the breathing amplitude for all directions (r Z 0.54 e 0.66, P < .001); whereas, weak significant linear correlations existed in all directions except left-right (r Z 0.04 e 0.17, P < .001). Conclusion: The salient result is that correlation errors represent the main contribution to overall random uncertainty. The 3D radial correlation errors reported here suggest that 95% of beam delivery was within 3.8 mm of the
target centroid based on correlation errors. Combining these results with estimates of end-to-end targeting errors and systematic uncertainties will help provide guidance on treatment margins. Oral Scientific Abstracts 266; Table 1 Error
Target Tracking Errors
Direction Absolute Mean (mm) STD (mm) 95th Percentile (mm)
Correlation LR AP CC 3D Radial Prediction LR AP CC 3D Radial
0.70 0.63 1.14 1.68 0.15 0.09 0.17 0.26
0.98 0.83 1.54 1.12 0.09 0.07 0.1 0.13
2.07 1.75 3.24 3.83 0.31 0.22 0.35 0.5
LR Z left-right, AP Z anterior-posterior, CC Z cranial-caudal. Author Disclosure: J. Winter: None. A. Swaminath: None. R.K. Wong: None. T. Chow: None.
267 A Multi-organ Meshing Method for Sliding Motion Modeling in 4DCBCT Reconstruction Z. Zhong,1 X. Gu,1 P. Iyengar,1 W. Mao,1 X. Guo,2 and J. Wang1; 1 University of Texas Southwestern Medical Center, Dallas, TX, 2University of Texas at Dallas, Richardson, TX Purpose/Objective(s): In the simultaneous motion estimation and image reconstruction (SMEIR) algorithm for 4D cone beam CT (4D-CBCT), the motion model was obtained by enforcing a global smoothness regularization term on the motion fields. The objective of this work was to enhance the performance of the SMEIR for 4D-CBCT by using a multi-organ meshing model to explicitly consider the discontinuity in the motion fields between different organs during the respiration. Materials/Methods: In the SMEIR algorithm, the deformable motion between a reference phase 4D-CBCT and other phases was obtained by matching the 4D-CBCT projections at each phase with the corresponding forward projections of the deformed reference phase directly. In this work, the sliding motion between lung and thoracic cage was considered by a multi-organ meshing model. Specifically, lung and thoracic cage were first segmented from a reference phase 4D-CBCT. Multi-organ tetrahedral meshes were then created from the segmented images to control different motions for different organs during the respiration. In the multi-organ meshes, continuous motion was enforced within each organ and along the normal direction of the organ interface, while discontinuous motion was allowed along the tangential direction of the organ interface. The updated motion model was then used in the motion compensated image reconstruction step in the SMEIR algorithm. The performance of the proposed method was evaluated through both digital phantom and patient studies. Results: Our multi-organ meshing method could capture well the organ surfaces and the sliding motion between different organs. The proposed multi-organ, mesh-based algorithm outperformed the homogeneous meshbased method in image reconstruction accuracy and organ motion accuracy in both the digital phantom and lung cancer patient studies. In the 4D NCAT digital phantom, normalized cross correlations (NCCs) between reconstructed image and the ground truth image for homogeneous and multi-organ, mesh-based methods were 0.9893 and 0.9920, respectively; the maximum motion errors along the interface between the lung and thoracic cage for homogeneous and multi-organ, mesh-based methods were 6.12 mm, and 0.95 mm, respectively. Conclusion: The multi-organ, mesh-based 4D-CBCT reconstruction method can estimate the motion fields accurately by considering the sliding motion between different organs. Both image quality and motion estimating accuracy are improved by the proposed method as compared to traditional homogeneous mesh-based approach. Author Disclosure: Z. Zhong: None. X. Gu: None. P. Iyengar: None. W. Mao: None. X. Guo: None. J. Wang: None.
Volume 93 Number 3S Supplement 2015 generated with similar target coverage and optimizations. The extent of normal tissue sparing was evaluated for the liver, lung, and small bowel with a two-tailed, paired sample, t-test utilized to detect differences in organ-at-risk (OAR) dose reduction. Results: Compared to plans without motion management, abdominal compression reduced the PTV volume in all patients by mean of 40.7 cm3, or 28.3% (P Z .008). The mean liver dose decreased by 12.8% (11.2 versus 12.8 Gy; P Z .004). If abdominal compression was not used, 2 patients (28.6%) would have required prescription dose deescalation from 50 Gy per RTOG 1112 based on mean liver dose >13 Gy; however, use of abdominal compression in these patients decreased mean liver dose, allowing criteria to be met for using a prescription dose of 50 Gy. The V50, V21, and V10 of the liver were lessened by an average of 12.1, 60.7, and 92.2 cm3, respectively, which equated to absolute (relative) reductions of 0.8% (25.0%), 3.4% (18.7%), and 5.0% (12.4%) (P < .04). No significant difference in small bowel dose was demonstrated secondary to a predominance of peripheral liver lesions. The average absolute (relative) lung V20 was reduced by 35 cm3 (39.5%) (P Z .05). Conclusion: Use of an abdominal compression belt can significantly reduce dose to the normal liver in patients receiving SBRT. Although this study primarily evaluated dosimetric differences in the treatment of peripheral liver tumors, abdominal compression may be particularly beneficial for central tumors to decrease dose to the duodenum and small bowel. Author Disclosure: J.W. Snider: None. J. Molitoris: None. B. Zhang: None. M.D. Chuong: None.
266 Quantifying the Targeting Accuracy of Liver Robotic Radiosurgery J. Winter,1 A. Swaminath,2 R.K.W. Wong,3 and T. Chow1; 1Juravinski Cancer Center, Hamilton, ON, Canada, 2Juravinski Cancer Centre, Hamilton, ON, Canada, 3McMaster University, Hamilton, ON, Canada Purpose/Objective(s): To estimate random uncertainties in robotic radiosurgical treatment of liver lesions using an online respiratory management system linking internal target position with an external surrogate. We focused on two key components of uncertainty: 1) residual position errors in the modeled target position based on the external surrogate and 2) residual position errors in the prediction algorithm employed to account for the 115 ms inherent temporal system lag. Materials/Methods: To provide quantitative estimates of positioning errors, we retrospectively reviewed log files generated for twenty-nine liver patients treated on a radiosurgical system. Motion tracking was achieved using orthogonal x-ray images to localize internal fiducials combined with optical tracking of the patient’s abdomen. A correlation model links target position with the external optical surrogate for dynamic robotic positioning of the linear accelerator throughout breathing, with periodic x-ray images to confirm the model. For each patient treatment fraction, we isolated x-ray images collected immediately prior to beam delivery and extracted the correlation error, which is the relative distance between the model-estimated target position and the actual position measured on the x-ray image. In addition, at each treatment image acquisition, we quantified the prediction error as the mean absolute difference between predicted position and the actual position reached after 115 ms. Last, we investigated potential correlations between breathing amplitude and the magnitude of tracking errors. Results: Correlation and prediction errors for all patients and treatment fractions are provided in Table 1. Cranial-caudal direction errors were greatest for both correlation and prediction errors. Prediction errors were considerably smaller than correlation errors in all directions. Prediction errors exhibited a strong significant linear correlation with the breathing amplitude for all directions (r Z 0.54 e 0.66, P < .001); whereas, weak significant linear correlations existed in all directions except left-right (r Z 0.04 e 0.17, P < .001). Conclusion: The salient result is that correlation errors represent the main contribution to overall random uncertainty. The 3D radial correlation errors reported here suggest that 95% of beam delivery was within 3.8 mm of the
target centroid based on correlation errors. Combining these results with estimates of end-to-end targeting errors and systematic uncertainties will help provide guidance on treatment margins. Oral Scientific Abstracts 266; Table 1 Error
Target Tracking Errors
Direction Absolute Mean (mm) STD (mm) 95th Percentile (mm)
Correlation LR AP CC 3D Radial Prediction LR AP CC 3D Radial
0.70 0.63 1.14 1.68 0.15 0.09 0.17 0.26
0.98 0.83 1.54 1.12 0.09 0.07 0.1 0.13
2.07 1.75 3.24 3.83 0.31 0.22 0.35 0.5
LR Z left-right, AP Z anterior-posterior, CC Z cranial-caudal. Author Disclosure: J. Winter: None. A. Swaminath: None. R.K. Wong: None. T. Chow: None.
267 A Multi-organ Meshing Method for Sliding Motion Modeling in 4DCBCT Reconstruction Z. Zhong,1 X. Gu,1 P. Iyengar,1 W. Mao,1 X. Guo,2 and J. Wang1; 1 University of Texas Southwestern Medical Center, Dallas, TX, 2University of Texas at Dallas, Richardson, TX Purpose/Objective(s): In the simultaneous motion estimation and image reconstruction (SMEIR) algorithm for 4D cone beam CT (4D-CBCT), the motion model was obtained by enforcing a global smoothness regularization term on the motion fields. The objective of this work was to enhance the performance of the SMEIR for 4D-CBCT by using a multi-organ meshing model to explicitly consider the discontinuity in the motion fields between different organs during the respiration. Materials/Methods: In the SMEIR algorithm, the deformable motion between a reference phase 4D-CBCT and other phases was obtained by matching the 4D-CBCT projections at each phase with the corresponding forward projections of the deformed reference phase directly. In this work, the sliding motion between lung and thoracic cage was considered by a multi-organ meshing model. Specifically, lung and thoracic cage were first segmented from a reference phase 4D-CBCT. Multi-organ tetrahedral meshes were then created from the segmented images to control different motions for different organs during the respiration. In the multi-organ meshes, continuous motion was enforced within each organ and along the normal direction of the organ interface, while discontinuous motion was allowed along the tangential direction of the organ interface. The updated motion model was then used in the motion compensated image reconstruction step in the SMEIR algorithm. The performance of the proposed method was evaluated through both digital phantom and patient studies. Results: Our multi-organ meshing method could capture well the organ surfaces and the sliding motion between different organs. The proposed multi-organ, mesh-based algorithm outperformed the homogeneous meshbased method in image reconstruction accuracy and organ motion accuracy in both the digital phantom and lung cancer patient studies. In the 4D NCAT digital phantom, normalized cross correlations (NCCs) between reconstructed image and the ground truth image for homogeneous and multi-organ, mesh-based methods were 0.9893 and 0.9920, respectively; the maximum motion errors along the interface between the lung and thoracic cage for homogeneous and multi-organ, mesh-based methods were 6.12 mm, and 0.95 mm, respectively. Conclusion: The multi-organ, mesh-based 4D-CBCT reconstruction method can estimate the motion fields accurately by considering the sliding motion between different organs. Both image quality and motion estimating accuracy are improved by the proposed method as compared to traditional homogeneous mesh-based approach. Author Disclosure: Z. Zhong: None. X. Gu: None. P. Iyengar: None. W. Mao: None. X. Guo: None. J. Wang: None.