- Email: [email protected]
A dynamic non-invasive technique for predicting organ motion in respiratory-gated radiotherapy of the chest
Proceedings of the 43rd Annual ASTRO Meeting
25
complete a feasibility protocol and one patient on a compassionate use basis. A twelfth patient has been treated as part of an ongoing phase I clinical trial. The CyberKnife consists of a 6 MV linear accelerator mounted to an industrial robot, a pair of orthogonal x-ray imaging systems, and a computer to perform treatment planning and control the treatment process. The AccuTraCK system consists of an infrared LED tracking system and additional computers that are used to indirectly track the target movement. The AccuTraCK system is used to modify the robot aiming to account for tumor movement. The target is implanted with gold seed fiducial markers under CT guided fluoroscopy using either a trans-thoracic or trans-bronchial approach. A few days post implant, a treatment planning CT is performed, the CTs are imported into the planning system, the fiducial seeds are identified, and a conformal treatment plan is created. At treatment time, infrared LEDs are attached to the patient and their positions are continuously monitored. After the patient is aligned, several pairs of orthogonal x-ray images of the fiducials are acquired to determine the fiducial locations throughout the respiratory cycle. This information, coincident LED and fiducial locations, is used to create a correspondence model relating the locations of the LEDs with the locations of the fiducials, and hence the target. During treatment the LED locations and the occasionally updated model relating the LED locations to the fiducial locations are used to correct the robot aiming for target motion. Due to the finite response time of the robot system, an LMS adaptive filtering algorithm is used to predict in advance where the robot should aim. We examine the accuracy of both the modeling and prediction of the AccuTraCK system. Results: Model errors: The model assumes a linear relationship between the locations of the external LEDs and the internal fiducials. Whenever a new image set is taken, the current model fiducial locations are compared with radiographically determined fiducial locations, providing a measure of the error of the current model, and the model is updated. Prediction algorithm errors: Due to the finite response time of the robot system, AccuTraCK uses an LMS adaptive filtering algorithm to predict the tumor location 0.8 seconds into the future. The difference between where the algorithm predicted the fiducials would be, and where they actually are, as indicated by the model applied to the LED data, is the error of the prediction algorithm. The complete data required to “exactly” determine the prediction errors was stored for patients 9-12. It should be noted that the robot would not necessarily achieve the location requested by the prediction algorithm. The effects of this source of inaccuracy will be presented as well. The following table summarizes the preliminary results of the correspondence model and prediction errors. Conclusion: The means of the modeling errors ranged from 0.6 to 3.4 mm. The means of the prediction errors ranged from 0.6 to 3.0 mm.
patient number
1
2
3
4
5
6
7
8
9
10
11
12
model error (mm) prediction error (mm)
0.6
0.7
1.9
1.2
1.2
0.7
2.3
1.2
2.1 1.8
0.8 0.6
1.5 0.6
3.4 3.0
39
A Dynamic Non-Invasive Technique for Predicting Organ Motion in Respiratory-Gated Radiotherapy of the Chest
V.R. Kini, S.S. Vedam, P.J. Keall, D.W. Arthur, R. Mohan Medical College of Virginia, Richmond, VA Purpose: Many radiotherapy techniques account for respiratory organ motion using invasive methods like implanted markers to determine the position of the target. Non-invasive respiratory gated radiotherapy techniques like those based on external anatomic motion are better tolerated. A critical assumption in such systems is the correlation between the motion of the external and internal anatomy. Day to day reproducibility of target and organ motion is also implicitly assumed. We evaluated the ability of a non-invasive respiratory gating system to accurately predict the dynamic position of internal structures such as the diaphragm, chest wall and tumor. Methods: The gating apparatus consists of reflective markers placed on the patient’s chest. Software interfaced to a video camera tracks the marker motion. The marker position serves as the signal that may be used to trigger imaging or treatment. Six patients underwent simultaneous monitoring of the chest wall and chest fluoroscopy. Patients were audio or video-coached to improve the reproducibility of breathing patterns. Each patient had 5 daily sessions. In each session, five 30-second fluoroscopic movies were recorded with and without coaching. A frame-by-frame analysis of each fluoroscopic movie was performed. Edge detection and pattern-recognition software was used to analyze movies for motion of different parts of the diaphragm, chest wall and tumor where possible. This was quantitatively correlated to the external marker motion during the first session using an analytical expression. Using this correlation coefficient, motion of the internal structures in later sessions was predicted based on the external marker motion. These predictions were then compared to the actual recorded motion of the same structures. Results: We analyzed 150 fluoroscopic movies. Predictability was analyzed for 120 motion traces. The correlation between the chest wall motion and the internal organ motion was represented by a simple mathematical function: R(t)-R ⴝ a[F(t- ␦ )-F] Where R(t) ⫽ external marker trace, a ⫽ ratio of amplitudes, F(t) ⫽ fluoro motion and ␦⫽ phase shift. Table 1 shows the range of standard deviations between the predicted positions and the observed positions of some sample structures in each patient. The difference between the predicted and observed positions of the chest wall, diaphragm or tumor was small (maximum 3.5 mms). Different internal structures moved in phase during a single session. Amplitude of motion was the same for different parts of the diaphragm. Coaching did not significantly alter predictability (maximum 3.8 mm with audio and 1.6 mm with video). However, without coaching, significant variation in respiratory patterns was seen between sessions. Audio and video coaching improved the reproducibilty of frequency and amplitude of motion respectively. Conclusions: 1. This non-invasive technique accurately predicts the respiratory motion of internal structures within 3.5 mms. 2. Different parts of the diaphragm and chest wall move in phase with each other. 3. Breathing motion is more reproducible with audio and visual coaching. This may improve the accuracy of gated or conventional 3D conformal radiotherapy.
26
I. J. Radiation Oncology
Patient Number
1
Medial Diaphragm Central Diaphragm Lateral Diaphragm Upper Chest Wall Lower Chest Wall
1.2 to 3.2 mms 1.6 to 3.5 mms 1.2 to 2.2 mms
40
● Biology ● Physics
2 0.5 0.9 0.8 0.3
to to to to
1.6 1.2 1.1 0.7
Volume 51, Number 3, Supplement 1, 2001
3 mms mms mms mms
0.6 to 2.8 mms 1.1 to 2.6 mms 0.4 to 0.8 mms
0.6 to 2 mms
4 0.5 0.9 0.8 0.4
to to to to
0.9 1.2 0.9 0.6
mms mms mms mms
5
6
1.0 to 1.4 mms 1.8 to 2.2 mms
1.0 to 1.9 mms 1.5 to 2.4 mms 1.1 to 2.3 mms
0.5 to 1.8 mms
Breath Holding Versus Real-Time Target Tracking for Respiratory Motion Compensation during Radiosurgery for Lung Tumors
T.M. Guerrero1, R.L. Crownover4, R.F. Rodebaugh4, T. Pawlicki1, D.P. Martin1,2, G.D. Glosser5, R.I. Whyte3, Q.T. Le1, M.J. Murphy1,2, H. Shiomi6, M.S. Weinhaus4, C.M. Ma1 1 Radiation Oncology, Stanford University, Stanford, CA, 2Neurosurgery, Stanford University, Stanford, CA, 3 Cardiothoracic Surgery, Stanford University, Stanford, CA, 4Radiation Oncology, Cleveland Clinic, Cleveland, OH, 5 Accuray, Inc, Oncology Systems, Sunnyvale, CA, 6Radiation Oncology, Osaka University Medical School, Osaka, Japan Purpose: A multi-institutional phase I dose escalation trial is underway to treat lung tumors using frameless stereotactic radiosurgery. Two techniques are utilized to account for target motion due to respiration, breath-holding (BH) and robotic real-time target tracking (RTT). In this study we investigate and compare the dosimetric consequences of breath-holding versus real-time target tracking for respiratory motion compensation utilizing Monte Carlo dose calculations. Materials and Methods: The Monte Carlo code package EGS4/MCDOSE was modified to include intra-fraction motion with an adjustable time sampling based on measured intra-fraction patient positions. Patient treatment plans and measured intra-fraction motion characteristics of lung tumor radiosurgery cases treated with BH or RTT were utilized. Treatment planning was performed utilizing the Accuray TPS and prescribed to 15 Gy to an isodose line covering the tumor. Each patient had an internal gold fiducial placed in or near their lung tumor. The fiducial location was determined 30 to 150 times during treatment by the Cyberknife’s (Accuray, Inc, Sunnyvale, CA) x-ray tracking system. For BH cases, the tumor position was determined with one breath-hold and partial treatment delivered with the next breath-hold. The robot position was adjusted to the tumor positions. For patients treated with RTT the AccuTrak (Accuray, Inc, Sunnyvale, CA) external optical tracking data was utilized to determine the expected tumor positions at a millisecond sampling rate. Dose distribution were calculated with EGS4/ MCDOSE for the treatment plan, the plan if motion had not been corrected for, and the plan with the residual position error using EGS4/MCDOSE. The dose distributions and resulting DVHs were analyzed to obtain the RTOG defined coverage and the volume of normal lung treated above 6.5 Gy. The motion characteristics of six patients treated with BH and dosimetry results of three of those cases are reported. The dosimetry of two patients treated with RTT are compared. Results: The six BH cases had a tumor position rms displacement from the initial set-up position of 0.196 to 0.231 cm, the estimated error for robot pointing obtained after correcting the robot pointing to the prior breath measured position was 0.128 to 0.296 cm. For BH patients the coverage values obtained using EGS4/MCDOSE ranged from 9.9 to 11.6 Gy. Calculation of the dose distribution using the measured motion resulted in coverage values of 4.9 to 6.9 Gy when no motion correction by the treatment delivery system was included. There was no significant improvement in coverage when the treatment targeting position was adjusted to the prior breath position value. For RTT patients the EGS4/MCDOSE determined coverage values were 12.0 and 13.5 Gy. When the measured tumor position motion was incorporated in the calculation the coverage values became 6.97 and 8.3 Gy. When only the residual error of the tumor targeting was incorporated the coverage values became 12.2 and 10.9 Gy. The volume of normal lung treated above 6.5 Gy remained below 10% of the total lung volume in all the cases studied. Conclusion: Monte Carlo determined coverage dosages were significantly lower than the TPS prescribed dose of 15 Gy. Further loss of lung tumor coverage is the predominant effect of respiratory motion during radiosurgery observed in this study. Adjusting for the residual differences in breath hold positions did not improve the dose coverage, though the rms deviation was improved in most cases. Real-time target tracking significantly restored the coverage, to within 1.67% of the planned value in one of the cases. Presently we are extending this Monte Carlo study to include the entire data set of eighteen patients.
41
A New Irradiation System For Lung Cancer: Patient’s Self-Breath-Hold and Self-Switching RadiationBeam On and Off Without Any Respiratory Monitoring Devices
H. Onishi, K. Kuriyama, T. Komiyama, J. Ueki, T. Araki Radiation Oncology, Yamanashi Medical University, Nakakoma, Japan Purpose: It is helpful to irradiate a lung tumor under a breath-hold for a reduction of planning target volume(PTV). To develop a simple and efficient breath-hold radiation technique for lung cancer, we made three purposes. 1) To compare the reproducibility of tumor-position under a breath-hold by a patient’s self-estimation with that under a breath-hold by a radiation technologist’s instruction. 2) To develop a switch which enable a patient to turn the radiation-beam on and off. 3) To evaluate the reproducibility of a tumor-position in the radiation field with a patient’s self-breath-hold and self-switching radiation-beam. Materials and Methods: Twenty patients with lung cancer were enrolled in this study. All patients were taught sufficiently how to hold the breath at a same inspiration phase with showing a fluoroscopy of respiratory motion before evaluation. CT images were obtained with 2mm-thickness in the vicinity of the tumor under a series of the breath-hold by a radiation technologist’s instruction and another series of the breath-hold by a patient’s self-estimation. CT was repeated three times with each breath-hold method. Differences of tumor position on CT images were measured in three dimensions with a CT analyzing menu. We have newly developed a switch which was connected directly to the console box of linac ( EXL-15DP; Mitsubishi Electric, Tokyo, Japan). It
25
complete a feasibility protocol and one patient on a compassionate use basis. A twelfth patient has been treated as part of an ongoing phase I clinical trial. The CyberKnife consists of a 6 MV linear accelerator mounted to an industrial robot, a pair of orthogonal x-ray imaging systems, and a computer to perform treatment planning and control the treatment process. The AccuTraCK system consists of an infrared LED tracking system and additional computers that are used to indirectly track the target movement. The AccuTraCK system is used to modify the robot aiming to account for tumor movement. The target is implanted with gold seed fiducial markers under CT guided fluoroscopy using either a trans-thoracic or trans-bronchial approach. A few days post implant, a treatment planning CT is performed, the CTs are imported into the planning system, the fiducial seeds are identified, and a conformal treatment plan is created. At treatment time, infrared LEDs are attached to the patient and their positions are continuously monitored. After the patient is aligned, several pairs of orthogonal x-ray images of the fiducials are acquired to determine the fiducial locations throughout the respiratory cycle. This information, coincident LED and fiducial locations, is used to create a correspondence model relating the locations of the LEDs with the locations of the fiducials, and hence the target. During treatment the LED locations and the occasionally updated model relating the LED locations to the fiducial locations are used to correct the robot aiming for target motion. Due to the finite response time of the robot system, an LMS adaptive filtering algorithm is used to predict in advance where the robot should aim. We examine the accuracy of both the modeling and prediction of the AccuTraCK system. Results: Model errors: The model assumes a linear relationship between the locations of the external LEDs and the internal fiducials. Whenever a new image set is taken, the current model fiducial locations are compared with radiographically determined fiducial locations, providing a measure of the error of the current model, and the model is updated. Prediction algorithm errors: Due to the finite response time of the robot system, AccuTraCK uses an LMS adaptive filtering algorithm to predict the tumor location 0.8 seconds into the future. The difference between where the algorithm predicted the fiducials would be, and where they actually are, as indicated by the model applied to the LED data, is the error of the prediction algorithm. The complete data required to “exactly” determine the prediction errors was stored for patients 9-12. It should be noted that the robot would not necessarily achieve the location requested by the prediction algorithm. The effects of this source of inaccuracy will be presented as well. The following table summarizes the preliminary results of the correspondence model and prediction errors. Conclusion: The means of the modeling errors ranged from 0.6 to 3.4 mm. The means of the prediction errors ranged from 0.6 to 3.0 mm.
patient number
1
2
3
4
5
6
7
8
9
10
11
12
model error (mm) prediction error (mm)
0.6
0.7
1.9
1.2
1.2
0.7
2.3
1.2
2.1 1.8
0.8 0.6
1.5 0.6
3.4 3.0
39
A Dynamic Non-Invasive Technique for Predicting Organ Motion in Respiratory-Gated Radiotherapy of the Chest
V.R. Kini, S.S. Vedam, P.J. Keall, D.W. Arthur, R. Mohan Medical College of Virginia, Richmond, VA Purpose: Many radiotherapy techniques account for respiratory organ motion using invasive methods like implanted markers to determine the position of the target. Non-invasive respiratory gated radiotherapy techniques like those based on external anatomic motion are better tolerated. A critical assumption in such systems is the correlation between the motion of the external and internal anatomy. Day to day reproducibility of target and organ motion is also implicitly assumed. We evaluated the ability of a non-invasive respiratory gating system to accurately predict the dynamic position of internal structures such as the diaphragm, chest wall and tumor. Methods: The gating apparatus consists of reflective markers placed on the patient’s chest. Software interfaced to a video camera tracks the marker motion. The marker position serves as the signal that may be used to trigger imaging or treatment. Six patients underwent simultaneous monitoring of the chest wall and chest fluoroscopy. Patients were audio or video-coached to improve the reproducibility of breathing patterns. Each patient had 5 daily sessions. In each session, five 30-second fluoroscopic movies were recorded with and without coaching. A frame-by-frame analysis of each fluoroscopic movie was performed. Edge detection and pattern-recognition software was used to analyze movies for motion of different parts of the diaphragm, chest wall and tumor where possible. This was quantitatively correlated to the external marker motion during the first session using an analytical expression. Using this correlation coefficient, motion of the internal structures in later sessions was predicted based on the external marker motion. These predictions were then compared to the actual recorded motion of the same structures. Results: We analyzed 150 fluoroscopic movies. Predictability was analyzed for 120 motion traces. The correlation between the chest wall motion and the internal organ motion was represented by a simple mathematical function: R(t)-R ⴝ a[F(t- ␦ )-F] Where R(t) ⫽ external marker trace, a ⫽ ratio of amplitudes, F(t) ⫽ fluoro motion and ␦⫽ phase shift. Table 1 shows the range of standard deviations between the predicted positions and the observed positions of some sample structures in each patient. The difference between the predicted and observed positions of the chest wall, diaphragm or tumor was small (maximum 3.5 mms). Different internal structures moved in phase during a single session. Amplitude of motion was the same for different parts of the diaphragm. Coaching did not significantly alter predictability (maximum 3.8 mm with audio and 1.6 mm with video). However, without coaching, significant variation in respiratory patterns was seen between sessions. Audio and video coaching improved the reproducibilty of frequency and amplitude of motion respectively. Conclusions: 1. This non-invasive technique accurately predicts the respiratory motion of internal structures within 3.5 mms. 2. Different parts of the diaphragm and chest wall move in phase with each other. 3. Breathing motion is more reproducible with audio and visual coaching. This may improve the accuracy of gated or conventional 3D conformal radiotherapy.
26
I. J. Radiation Oncology
Patient Number
1
Medial Diaphragm Central Diaphragm Lateral Diaphragm Upper Chest Wall Lower Chest Wall
1.2 to 3.2 mms 1.6 to 3.5 mms 1.2 to 2.2 mms
40
● Biology ● Physics
2 0.5 0.9 0.8 0.3
to to to to
1.6 1.2 1.1 0.7
Volume 51, Number 3, Supplement 1, 2001
3 mms mms mms mms
0.6 to 2.8 mms 1.1 to 2.6 mms 0.4 to 0.8 mms
0.6 to 2 mms
4 0.5 0.9 0.8 0.4
to to to to
0.9 1.2 0.9 0.6
mms mms mms mms
5
6
1.0 to 1.4 mms 1.8 to 2.2 mms
1.0 to 1.9 mms 1.5 to 2.4 mms 1.1 to 2.3 mms
0.5 to 1.8 mms
Breath Holding Versus Real-Time Target Tracking for Respiratory Motion Compensation during Radiosurgery for Lung Tumors
T.M. Guerrero1, R.L. Crownover4, R.F. Rodebaugh4, T. Pawlicki1, D.P. Martin1,2, G.D. Glosser5, R.I. Whyte3, Q.T. Le1, M.J. Murphy1,2, H. Shiomi6, M.S. Weinhaus4, C.M. Ma1 1 Radiation Oncology, Stanford University, Stanford, CA, 2Neurosurgery, Stanford University, Stanford, CA, 3 Cardiothoracic Surgery, Stanford University, Stanford, CA, 4Radiation Oncology, Cleveland Clinic, Cleveland, OH, 5 Accuray, Inc, Oncology Systems, Sunnyvale, CA, 6Radiation Oncology, Osaka University Medical School, Osaka, Japan Purpose: A multi-institutional phase I dose escalation trial is underway to treat lung tumors using frameless stereotactic radiosurgery. Two techniques are utilized to account for target motion due to respiration, breath-holding (BH) and robotic real-time target tracking (RTT). In this study we investigate and compare the dosimetric consequences of breath-holding versus real-time target tracking for respiratory motion compensation utilizing Monte Carlo dose calculations. Materials and Methods: The Monte Carlo code package EGS4/MCDOSE was modified to include intra-fraction motion with an adjustable time sampling based on measured intra-fraction patient positions. Patient treatment plans and measured intra-fraction motion characteristics of lung tumor radiosurgery cases treated with BH or RTT were utilized. Treatment planning was performed utilizing the Accuray TPS and prescribed to 15 Gy to an isodose line covering the tumor. Each patient had an internal gold fiducial placed in or near their lung tumor. The fiducial location was determined 30 to 150 times during treatment by the Cyberknife’s (Accuray, Inc, Sunnyvale, CA) x-ray tracking system. For BH cases, the tumor position was determined with one breath-hold and partial treatment delivered with the next breath-hold. The robot position was adjusted to the tumor positions. For patients treated with RTT the AccuTrak (Accuray, Inc, Sunnyvale, CA) external optical tracking data was utilized to determine the expected tumor positions at a millisecond sampling rate. Dose distribution were calculated with EGS4/ MCDOSE for the treatment plan, the plan if motion had not been corrected for, and the plan with the residual position error using EGS4/MCDOSE. The dose distributions and resulting DVHs were analyzed to obtain the RTOG defined coverage and the volume of normal lung treated above 6.5 Gy. The motion characteristics of six patients treated with BH and dosimetry results of three of those cases are reported. The dosimetry of two patients treated with RTT are compared. Results: The six BH cases had a tumor position rms displacement from the initial set-up position of 0.196 to 0.231 cm, the estimated error for robot pointing obtained after correcting the robot pointing to the prior breath measured position was 0.128 to 0.296 cm. For BH patients the coverage values obtained using EGS4/MCDOSE ranged from 9.9 to 11.6 Gy. Calculation of the dose distribution using the measured motion resulted in coverage values of 4.9 to 6.9 Gy when no motion correction by the treatment delivery system was included. There was no significant improvement in coverage when the treatment targeting position was adjusted to the prior breath position value. For RTT patients the EGS4/MCDOSE determined coverage values were 12.0 and 13.5 Gy. When the measured tumor position motion was incorporated in the calculation the coverage values became 6.97 and 8.3 Gy. When only the residual error of the tumor targeting was incorporated the coverage values became 12.2 and 10.9 Gy. The volume of normal lung treated above 6.5 Gy remained below 10% of the total lung volume in all the cases studied. Conclusion: Monte Carlo determined coverage dosages were significantly lower than the TPS prescribed dose of 15 Gy. Further loss of lung tumor coverage is the predominant effect of respiratory motion during radiosurgery observed in this study. Adjusting for the residual differences in breath hold positions did not improve the dose coverage, though the rms deviation was improved in most cases. Real-time target tracking significantly restored the coverage, to within 1.67% of the planned value in one of the cases. Presently we are extending this Monte Carlo study to include the entire data set of eighteen patients.
41
A New Irradiation System For Lung Cancer: Patient’s Self-Breath-Hold and Self-Switching RadiationBeam On and Off Without Any Respiratory Monitoring Devices
H. Onishi, K. Kuriyama, T. Komiyama, J. Ueki, T. Araki Radiation Oncology, Yamanashi Medical University, Nakakoma, Japan Purpose: It is helpful to irradiate a lung tumor under a breath-hold for a reduction of planning target volume(PTV). To develop a simple and efficient breath-hold radiation technique for lung cancer, we made three purposes. 1) To compare the reproducibility of tumor-position under a breath-hold by a patient’s self-estimation with that under a breath-hold by a radiation technologist’s instruction. 2) To develop a switch which enable a patient to turn the radiation-beam on and off. 3) To evaluate the reproducibility of a tumor-position in the radiation field with a patient’s self-breath-hold and self-switching radiation-beam. Materials and Methods: Twenty patients with lung cancer were enrolled in this study. All patients were taught sufficiently how to hold the breath at a same inspiration phase with showing a fluoroscopy of respiratory motion before evaluation. CT images were obtained with 2mm-thickness in the vicinity of the tumor under a series of the breath-hold by a radiation technologist’s instruction and another series of the breath-hold by a patient’s self-estimation. CT was repeated three times with each breath-hold method. Differences of tumor position on CT images were measured in three dimensions with a CT analyzing menu. We have newly developed a switch which was connected directly to the console box of linac ( EXL-15DP; Mitsubishi Electric, Tokyo, Japan). It