- Email: [email protected]
Baseline Correction of a Correlation Model in Infrared Marker-Based Dynamic Tumor Tracking With A Gimbaled Linac
S826
International Journal of Radiation Oncology Biology Physics
Results: Table summarizes the values of M, S, and s in the left-right (LR), superior-inferior (SI), and anterior-posterior (AP) directions. Using MRA, the systematic errors were reduced. Compared with the CG method, MRA improved the target estimation accuracy by 76.7, 88.3, and 80.0% in the LR, SI, and AP directions, respectively. Conclusions: This study demonstrated that the target estimation accuracy was improved substantially by multiplying the coefficients calculated with MRA by each implanted marker position. Author Disclosure: M. Nakamura: None. M. Takamiya: None. M. Akimoto: None. N. Mukumoto: None. M. Yamada: None. H. Tanabe: None. Y. Matsuo: None. T. Mizowaki: G. Consultant; MHI. M. Kokubo: G. Consultant; MHI. M. Hiraoka: G. Consultant; MHI.
Mannheim. E. Research Grant; Elekta, IBA. F. Honoraria; Educational courses Elekta, IBA. I. Travel Expenses; Elekta, IBA. A. Zimmermann: A. Employee; University Medical Center Mannheim, University of Heidelberg. S. von Swietochowski: A. Employee; University Medical Center Mannheim, University of Heidelberg. F. Wenz: A. Employee; University Medical Center Mannheim, University of Heidelberg. E. Research Grant; Elekta, Zeiss. F. Honoraria; Elekta, Zeiss, Celgene, Roche, Lilly, Ipsen. G. Consultant; Eleka. I. Travel Expenses; Elekta, Zeiss, Celgene, Roche, Lilly, Ipsen. K. Advisory Board; Elekta, Celgene. Q. Patent/License Fee/ Copyright; Zeiss. F. Lohr: A. Employee; University Medical Center Mannheim, University of Heidelberg. E. Research Grant; Elekta, IBA. F. Honoraria; Teaching Honoraria Elekta, IBA, Board Honoraria C-Rad. I. Travel Expenses; Travel Grants Elekta, C-Rad, IBA. K. Advisory Board; Board Member of C-Rad. M. Stock; Stock Holdings IMUC, ACTI, ONCY, MRKd, SAZd. S. Leadership; Board Member C-Rad.
3570 Breath-Hold Cone Beam CT (CBCT): Improved Image Quality With “Stop-and-Go” Breath HoldeOnly Acquisition Versus Repetitive Breath Hold During Continuous Rotation J. Boda-Heggemann, A. Jahnke, L. Jahnke, A. Simeonova, S.K. Mai, H. Wertz, A. Zimmermann, S. von Swietochowski, F. Wenz, and F. Lohr; University Medical Centre Mannheim Medical Faculty Mannheim e University of Heidelberg, Mannheim, Germany Purpose/Objective(s): Breath-hold CBCT imaging for SABR (Stereotactic Ablative Body Radiation therapy) has been performed at our department with repeated breathhold with approximately 60% of the dataset being acquired in breathhold phases during one continuous CBCT rotation (2 minutes). This has established a complete computer-controlled deep inspiration breathhold (DIBH)-workflow from treatment planning to delivery. This method is clinically used and has been proven to be sufficient for high-precision image matching. It is now technically possible to stop the gantry rotation during free-breathing phases and acquire CBCT images during breathhold phases only (“stop-and-go” approach). Here we compare image quality of CBCT in DIBH-only “stop-and-go” acquisition vs repeat DIBH CBCT during continuous rotation. Materials/Methods: A patient with a lung metastasis of a soft tissue tumor has been treated with SABR (60Gy, 12 times 5Gy fractions). Before each daily fraction, CBCT images were acquired. CBCT in DIBH-only “stopand-go” acquisition and repeat DIBH CBCT during continuous rotation were compared quantitatively using grey value gradients that were calculated by taking the square root of the sum of the squares of the image gradients in x and y directions. Around the tumor and the diaphragm a region of interest (ROI) was defined. In this ROI the mean and standard deviation of the gradient image were calculated over the central 10 slices defining the sharpness of the image. Results: Acquisition time of a breath-hold only stop-and-go CBCT was 4 min vs 2 min for repeat-DIBH CBCT. Blurring was reduced in the stopand-go breath-hold-only CBCT vs repeat-DIBH CBCT acquired during continuous gantry rotation. Upon objective grey-value analysis, sharpness for the breath-hold-only images was increased by (13.8 2.5)% for the tumor ROI and (19.9 1.9)% for the diaphragm ROI compared to the repetitive breath-hold images reconstructed from 60% breathhold phases. Conclusions: Breath-hold only CBCT with “stop-and-go” acquisition further improved image quality and reduced blurring over acquisition in repetitive DIBH during continuous rotation at the cost of a longer time to acquire a full image dataset. Imaging time can be reduced further by combining kV and MV imaging for a full CBCT acquisition in a single breath-hold. Author Disclosure: J. Boda-Heggemann: A. Employee; University Medical Center Mannheim, University of Heidelberg. F. Honoraria; Lectures for Elekta on conferences, Training courses Elekta. A. Jahnke: A. Employee; University Medical Center Mannheim, University of Heidelberg. L. Jahnke: A. Employee; University Medical Center Mannheim, University of Heidelberg. F. Honoraria; Training courses for Elekta, Speaker at conferences for Elekta. G. Consultant; Elekta 2013. A. Simeonova: A. Employee; University Medical Center Mannheim, University of Heidelberg. F. Honoraria; Educational lectures for Elekta. S.K. Mai: A. Employee; University Medical Center Mannheim, University of Heidelberg. H. Wertz: A. Employee; University Medical Center
3571 Baseline Correction of a Correlation Model in Infrared MarkerBased Dynamic Tumor Tracking With A Gimbaled Linac M. Akimoto,1 M. Nakamura,1 N. Mukumoto,1 M. Yamada,1 K. Yokota,1 H. Tanabe,2 N. Ueki,1 S. Kaneko,1 Y. Matsuo,1 H. Monzen,1 T. Mizowaki,1 M. Kokubo,3 and M. Hiraoka1; 1Kyoto University Graduate School of Medicine, Kyoto, Japan, 2Institute of Biomedical Reseach and Innovation, Kobe, Japan, 3Kobe City Medical Center General Hospital, Kobe, Japan Purpose/Objective(s): Our previous study demonstrated that the baseline drift of external and internal respiratory motion reduced the prediction accuracy of infrared (IR) marker-based dynamic tumor tracking irradiation (IR Tracking) with a gimbaled Linac. In this study, we proposed a baseline correction method, applied immediately before beam delivery, to improve the prediction accuracy of IR Tracking. Materials/Methods: To perform IR Tracking, a correlation model [in this study, four-dimensional (4D) model] should be constructed at the beginning of treatment to correlate the internal and external respiratory signals, and the 4D model is expressed using a quadratic function involving the IR marker position (x) and its velocity (v), namely function F(x,v). First, the first 4D model, F1st(x,v), was adjusted by the baseline drift IR markers (BDIR) along the x-axis, as function F’(x,v). Next, BDdetect, that was defined as the difference between the target positions indicated by the implanted fiducial markers (Pdetect) and the predicted target positions with F’(x,v) (Ppredict) was determined using orthogonal kV-X-ray images at the peaks of the Pdetect of the end-inhale and end-exhale phases for 10 s just before irradiation. F’(x,v) was corrected with BDdetect to compensate for the residual error. The final corrected 4D model was expressed as Fcor(x,v) Z F1st{(x-BDIR),v}-BDdetect. We retrospectively applied this function to 53 paired logfiles of the 4D model for 12 lung cancer patients who underwent IR Tracking. The 95th percentile of the absolute differences between Pdetect and Ppredict (jEpj) was compared between F1st(x,v) and Fcor(x,v). In addition, the overall mean (M), systematic (S), and random (s) errors were calculated using mean values and standard deviations of Ep for F1st(x,v) and Fcor(x,v) of each treatment session. Results: The median 95th percentile of jEpj (units: mm) was 1.0, 1.7, and 3.5 for F1st(x,v), and 0.6, 1.1, and 2.1 for Fcor(x,v) in the left-right (LR), anterior-posterior (AP), and superior-inferior (SI) directions, respectively. The 95th percentile of jEpj peaked at 3.2 mm using Fcor(x,v), compared with 8.4 mm using F1st(x,v). Table shows the M, S, and s of Ep for F1st(x,v) and Fcor(x,v) in the LR, AP, and SI directions. Conclusions: Our proposed method improved the prediction accuracy of IR Tracking by correcting the baseline drift immediately before irradiation. Scientific Abstract 3571; Table 4D model
The M, S, and s of Ep for 1st and corrected
F1st(x,v)[mm] M S
s
Fcor(x,v)[mm]
LR
AP
SI
LR
AP
SI
0.0 0.4 0.3
0.3 0.7 0.6
1.2 1.4 1.8
0.0 0.4 0.1
-0.1 0.7 0.3
0.2 1.0 0.5
Volume 90 Number 1S Supplement 2014 Author Disclosure: M. Akimoto: None. M. Nakamura: None. N. Mukumoto: None. M. Yamada: None. K. Yokota: None. H. Tanabe: None. N. Ueki: None. S. Kaneko: None. Y. Matsuo: None. H. Monzen: None. T. Mizowaki: G. Consultant; Mitsubishi Heavy Industries, Ltd. M. Kokubo: G. Consultant; Mitsubishi Heavy Industries, Ltd. M. Hiraoka: G. Consultant; Mitsubishi Heavy Industries, Ltd.
3572 Patient-Specific Collision Detection Package Using Surface Imaging L. Padilla, E.A. Pearson, and C. Pelizzari; University of Chicago, Chicago, IL Purpose/Objective(s): With the increased prevalence of complex radiation treatments utilizing noncoplanar beams, and CBCT in IGRT, collisions between patient, couch and gantry are of rising concern in radiation therapy. The actual patient anatomy is necessary for reliable collision predictions and the limited region scanned for treatment planning is often insufficient. Detection of potential collisions during treatment simulation and planning using a surface image of the patient is a novel technique. It can help ensure patient safety, reduce potential machine damage and streamline treatment delivery in the clinic. We created a fast and efficient collision detection package that incorporates existing CT planning data with captures from a surface imaging system to help resolve these issues. Materials/Methods: This package consists of two different components to assess collisions with the collimator and imaging arms using the actual patient space. A computer model of the gantry and imaging arms was used. The patient space encompasses patient anatomy, immobilization devices and the treatment couch. The patient anatomy was generated from a surface image captured with a commercial surface imaging system and registered to the skin surface from the planning CT. The 3D model of the immobilization device was created using a Kinect camera. The couch was modeled as a slab. All three components of the patient space were combined and placed at the appropriate treatment position. The first program evaluates a full gantry rotation with the couch at 0, consistent with imaging and some VMAT procedures. It allows for the addition of a safety margin to account for possible patient position inconsistencies. This program outputs the gantry angles of collision, if any, and couch shifts necessary for clearance. This can be used to detect collisions during CBCT and determine the potential “treatment collision-free space” at the CT simulator for treatment planning. The second program takes a specific gantry-couch angle combination and produces a colormap of the patient geometry to indicate minimum clearance distance and highlight potential collisions. This is useful to assess valid treatment geometries for planning noncoplanar beams. Results: We successfully calculated the collision-free space and gantrycouch angle combinations, as well as the necessary couch shifts to avoid collision using the software package presented. The results were validated in the treatment room with measurements on a phantom. Conclusions: This software allows for reliable and efficient collision detection using an accurate model of the full patient space. The implementation of this package in the clinic can lead to improved patient safety, elimination of machine collisions during treatment and reduction of treatment planning and delivery times. Author Disclosure: L. Padilla: None. E.A. Pearson: None. C. Pelizzari: E. Research Grant; Varian Medical Systems. K. Advisory Board; Member of scientific advisory board of Reflexion Medical. N. Stock Options; Reflexion Medial.
3573 Is Surface Registration Accurate Enough for Patient Setup in Head and Neck Radiation Therapy? Y. Kim,1,2 C. Jenkins,1 Y. Na,1 R. Li,1 and L. Xing1; 1Radiation Oncology, Stanford University School of Medicine, Stanford, CA, 2Center for Bionics, Korea Institute of Science and Technology, Seoul, Korea, Republic of Korea Purpose/Objective(s): Recently, 3D optical surface imaging (OSI) has been applied to patient setup in radiation therapy (RT). The OSI system for patient setup has advantages compared with the conventional method using
Poster Viewing Abstracts S827 cone-beam CT (CBCT) because it is radiation free and frame-less. While the conventional CBCT method uses volumetric registration, the OSI system uses surface registration for patient positioning. Although some research has reported on the outcomes and accuracy of OSI for RT, the accuracy of the surface registration for OSI has not yet been sufficiently investigated. This study compares the relative accuracy of surface and volumetric registration in head-and-neck RT. Materials/Methods: 26 sets of planning CT and CBCT patient data were used in the experiment. For each set of data, the CBCT acquired in the course of RT was registered to the planning CT using both volumetric and surface registration algorithms. By examining the level of agreement between the two registration approaches we are able to assess the accuracy of surface registration. As input data for surface registration, patients’ skin surfaces were created by contouring patient skin from planning CT and treatment CBCT, respectively. Surface registration was performed using the iterative closest points (ICP) based on point-plane closest method. The accuracy of each method was also evaluated by digital simulation tests. Results: The digital simulation tests showed that both of the surface and volumetric registration methods are precise enough to compare the results of patient data. Based on the results of 26 patients, we found that considerable deviation between the surface registration and the volumetric registration could occur. The average translation deviation between the surface and volumetric registrations was 2.7 mm, while the maximum rootmean-square deviation was 5.2 mm. The residual deviation of the surface registration was computed to have an average of 0.9 mm and a maximum of 1.7 mm. Conclusions: This research provides an objective assessment of the accuracy of patient positioning based on surface information and sheds useful insight into the validity of surface imaging in radiation therapy. Caution should thus be taken in using an OSI system as the sole modality for patient setup because surface registration may lead to results different from those of the conventional volumetric registration. Additional measures are required to ensure the accuracy of OSI in RT patient setup. A deformable registration can be used to improve the accuracy of RT. Author Disclosure: Y. Kim: None. C. Jenkins: None. Y. Na: None. R. Li: None. L. Xing: None.
3574 Couch-Height Based Patient Setup for Abdominal Radiation Therapy S. Oohira,1 Y. Ueda,1 K. Nishiyama,1 M. Miyazaki,1 M. Isono,1 T. Katsutomo,1 M. Takashina,2 M. Koizumi,2 K. Kawanabe,1 and T. Teruki1; 1Department of Radiation Oncology, Osaka Medical Center for Cancer and Cardiovascular Diseases, Osaka, Japan, 2 Department of Medical Physics and Engineering, Osaka University Graduate School of Medicine, Osaka, Japan Purpose/Objective(s): To compare the accuracy of couch-height based patient set-up (CHPS) with conventional skin mark patient set-up (SMPS) for abdominal radiation therapy. Materials/Methods: 18 patients with pancreatic cancer were set-up using a body immobilization device and underwent CT simulation. Anterior and bilateral skin marks corresponding to the isocenter were placed and a distance between the isocenter and couch top was measured. At treatment, the couch was set at the level that was indicated at CT simulation (CHPS). The difference in couch level between CHPS and SMPS was measured (SMPS-CHPS shift). Under CHPS, a time-integrated electronic portal image (TI-EPI) was acquired using right or left treatment beam. Off-line manual bone matching was performed using the TI-EPI and the corresponding digitally reconstructed
Scientific Abstract 3574; Table S, s and M in A-P direction for SMPS, SMPS/NAL, SMPS/eNAL and CHPS S (mm) s (mm) M (mm)
SMPS
SMPS/NAL
SMPS/eNAL
CHPS
4.0 2.6 9.8
1.8 3.2 5.9
0.8 3.2 3.8
1.0 0.8 2.5
International Journal of Radiation Oncology Biology Physics
Results: Table summarizes the values of M, S, and s in the left-right (LR), superior-inferior (SI), and anterior-posterior (AP) directions. Using MRA, the systematic errors were reduced. Compared with the CG method, MRA improved the target estimation accuracy by 76.7, 88.3, and 80.0% in the LR, SI, and AP directions, respectively. Conclusions: This study demonstrated that the target estimation accuracy was improved substantially by multiplying the coefficients calculated with MRA by each implanted marker position. Author Disclosure: M. Nakamura: None. M. Takamiya: None. M. Akimoto: None. N. Mukumoto: None. M. Yamada: None. H. Tanabe: None. Y. Matsuo: None. T. Mizowaki: G. Consultant; MHI. M. Kokubo: G. Consultant; MHI. M. Hiraoka: G. Consultant; MHI.
Mannheim. E. Research Grant; Elekta, IBA. F. Honoraria; Educational courses Elekta, IBA. I. Travel Expenses; Elekta, IBA. A. Zimmermann: A. Employee; University Medical Center Mannheim, University of Heidelberg. S. von Swietochowski: A. Employee; University Medical Center Mannheim, University of Heidelberg. F. Wenz: A. Employee; University Medical Center Mannheim, University of Heidelberg. E. Research Grant; Elekta, Zeiss. F. Honoraria; Elekta, Zeiss, Celgene, Roche, Lilly, Ipsen. G. Consultant; Eleka. I. Travel Expenses; Elekta, Zeiss, Celgene, Roche, Lilly, Ipsen. K. Advisory Board; Elekta, Celgene. Q. Patent/License Fee/ Copyright; Zeiss. F. Lohr: A. Employee; University Medical Center Mannheim, University of Heidelberg. E. Research Grant; Elekta, IBA. F. Honoraria; Teaching Honoraria Elekta, IBA, Board Honoraria C-Rad. I. Travel Expenses; Travel Grants Elekta, C-Rad, IBA. K. Advisory Board; Board Member of C-Rad. M. Stock; Stock Holdings IMUC, ACTI, ONCY, MRKd, SAZd. S. Leadership; Board Member C-Rad.
3570 Breath-Hold Cone Beam CT (CBCT): Improved Image Quality With “Stop-and-Go” Breath HoldeOnly Acquisition Versus Repetitive Breath Hold During Continuous Rotation J. Boda-Heggemann, A. Jahnke, L. Jahnke, A. Simeonova, S.K. Mai, H. Wertz, A. Zimmermann, S. von Swietochowski, F. Wenz, and F. Lohr; University Medical Centre Mannheim Medical Faculty Mannheim e University of Heidelberg, Mannheim, Germany Purpose/Objective(s): Breath-hold CBCT imaging for SABR (Stereotactic Ablative Body Radiation therapy) has been performed at our department with repeated breathhold with approximately 60% of the dataset being acquired in breathhold phases during one continuous CBCT rotation (2 minutes). This has established a complete computer-controlled deep inspiration breathhold (DIBH)-workflow from treatment planning to delivery. This method is clinically used and has been proven to be sufficient for high-precision image matching. It is now technically possible to stop the gantry rotation during free-breathing phases and acquire CBCT images during breathhold phases only (“stop-and-go” approach). Here we compare image quality of CBCT in DIBH-only “stop-and-go” acquisition vs repeat DIBH CBCT during continuous rotation. Materials/Methods: A patient with a lung metastasis of a soft tissue tumor has been treated with SABR (60Gy, 12 times 5Gy fractions). Before each daily fraction, CBCT images were acquired. CBCT in DIBH-only “stopand-go” acquisition and repeat DIBH CBCT during continuous rotation were compared quantitatively using grey value gradients that were calculated by taking the square root of the sum of the squares of the image gradients in x and y directions. Around the tumor and the diaphragm a region of interest (ROI) was defined. In this ROI the mean and standard deviation of the gradient image were calculated over the central 10 slices defining the sharpness of the image. Results: Acquisition time of a breath-hold only stop-and-go CBCT was 4 min vs 2 min for repeat-DIBH CBCT. Blurring was reduced in the stopand-go breath-hold-only CBCT vs repeat-DIBH CBCT acquired during continuous gantry rotation. Upon objective grey-value analysis, sharpness for the breath-hold-only images was increased by (13.8 2.5)% for the tumor ROI and (19.9 1.9)% for the diaphragm ROI compared to the repetitive breath-hold images reconstructed from 60% breathhold phases. Conclusions: Breath-hold only CBCT with “stop-and-go” acquisition further improved image quality and reduced blurring over acquisition in repetitive DIBH during continuous rotation at the cost of a longer time to acquire a full image dataset. Imaging time can be reduced further by combining kV and MV imaging for a full CBCT acquisition in a single breath-hold. Author Disclosure: J. Boda-Heggemann: A. Employee; University Medical Center Mannheim, University of Heidelberg. F. Honoraria; Lectures for Elekta on conferences, Training courses Elekta. A. Jahnke: A. Employee; University Medical Center Mannheim, University of Heidelberg. L. Jahnke: A. Employee; University Medical Center Mannheim, University of Heidelberg. F. Honoraria; Training courses for Elekta, Speaker at conferences for Elekta. G. Consultant; Elekta 2013. A. Simeonova: A. Employee; University Medical Center Mannheim, University of Heidelberg. F. Honoraria; Educational lectures for Elekta. S.K. Mai: A. Employee; University Medical Center Mannheim, University of Heidelberg. H. Wertz: A. Employee; University Medical Center
3571 Baseline Correction of a Correlation Model in Infrared MarkerBased Dynamic Tumor Tracking With A Gimbaled Linac M. Akimoto,1 M. Nakamura,1 N. Mukumoto,1 M. Yamada,1 K. Yokota,1 H. Tanabe,2 N. Ueki,1 S. Kaneko,1 Y. Matsuo,1 H. Monzen,1 T. Mizowaki,1 M. Kokubo,3 and M. Hiraoka1; 1Kyoto University Graduate School of Medicine, Kyoto, Japan, 2Institute of Biomedical Reseach and Innovation, Kobe, Japan, 3Kobe City Medical Center General Hospital, Kobe, Japan Purpose/Objective(s): Our previous study demonstrated that the baseline drift of external and internal respiratory motion reduced the prediction accuracy of infrared (IR) marker-based dynamic tumor tracking irradiation (IR Tracking) with a gimbaled Linac. In this study, we proposed a baseline correction method, applied immediately before beam delivery, to improve the prediction accuracy of IR Tracking. Materials/Methods: To perform IR Tracking, a correlation model [in this study, four-dimensional (4D) model] should be constructed at the beginning of treatment to correlate the internal and external respiratory signals, and the 4D model is expressed using a quadratic function involving the IR marker position (x) and its velocity (v), namely function F(x,v). First, the first 4D model, F1st(x,v), was adjusted by the baseline drift IR markers (BDIR) along the x-axis, as function F’(x,v). Next, BDdetect, that was defined as the difference between the target positions indicated by the implanted fiducial markers (Pdetect) and the predicted target positions with F’(x,v) (Ppredict) was determined using orthogonal kV-X-ray images at the peaks of the Pdetect of the end-inhale and end-exhale phases for 10 s just before irradiation. F’(x,v) was corrected with BDdetect to compensate for the residual error. The final corrected 4D model was expressed as Fcor(x,v) Z F1st{(x-BDIR),v}-BDdetect. We retrospectively applied this function to 53 paired logfiles of the 4D model for 12 lung cancer patients who underwent IR Tracking. The 95th percentile of the absolute differences between Pdetect and Ppredict (jEpj) was compared between F1st(x,v) and Fcor(x,v). In addition, the overall mean (M), systematic (S), and random (s) errors were calculated using mean values and standard deviations of Ep for F1st(x,v) and Fcor(x,v) of each treatment session. Results: The median 95th percentile of jEpj (units: mm) was 1.0, 1.7, and 3.5 for F1st(x,v), and 0.6, 1.1, and 2.1 for Fcor(x,v) in the left-right (LR), anterior-posterior (AP), and superior-inferior (SI) directions, respectively. The 95th percentile of jEpj peaked at 3.2 mm using Fcor(x,v), compared with 8.4 mm using F1st(x,v). Table shows the M, S, and s of Ep for F1st(x,v) and Fcor(x,v) in the LR, AP, and SI directions. Conclusions: Our proposed method improved the prediction accuracy of IR Tracking by correcting the baseline drift immediately before irradiation. Scientific Abstract 3571; Table 4D model
The M, S, and s of Ep for 1st and corrected
F1st(x,v)[mm] M S
s
Fcor(x,v)[mm]
LR
AP
SI
LR
AP
SI
0.0 0.4 0.3
0.3 0.7 0.6
1.2 1.4 1.8
0.0 0.4 0.1
-0.1 0.7 0.3
0.2 1.0 0.5
Volume 90 Number 1S Supplement 2014 Author Disclosure: M. Akimoto: None. M. Nakamura: None. N. Mukumoto: None. M. Yamada: None. K. Yokota: None. H. Tanabe: None. N. Ueki: None. S. Kaneko: None. Y. Matsuo: None. H. Monzen: None. T. Mizowaki: G. Consultant; Mitsubishi Heavy Industries, Ltd. M. Kokubo: G. Consultant; Mitsubishi Heavy Industries, Ltd. M. Hiraoka: G. Consultant; Mitsubishi Heavy Industries, Ltd.
3572 Patient-Specific Collision Detection Package Using Surface Imaging L. Padilla, E.A. Pearson, and C. Pelizzari; University of Chicago, Chicago, IL Purpose/Objective(s): With the increased prevalence of complex radiation treatments utilizing noncoplanar beams, and CBCT in IGRT, collisions between patient, couch and gantry are of rising concern in radiation therapy. The actual patient anatomy is necessary for reliable collision predictions and the limited region scanned for treatment planning is often insufficient. Detection of potential collisions during treatment simulation and planning using a surface image of the patient is a novel technique. It can help ensure patient safety, reduce potential machine damage and streamline treatment delivery in the clinic. We created a fast and efficient collision detection package that incorporates existing CT planning data with captures from a surface imaging system to help resolve these issues. Materials/Methods: This package consists of two different components to assess collisions with the collimator and imaging arms using the actual patient space. A computer model of the gantry and imaging arms was used. The patient space encompasses patient anatomy, immobilization devices and the treatment couch. The patient anatomy was generated from a surface image captured with a commercial surface imaging system and registered to the skin surface from the planning CT. The 3D model of the immobilization device was created using a Kinect camera. The couch was modeled as a slab. All three components of the patient space were combined and placed at the appropriate treatment position. The first program evaluates a full gantry rotation with the couch at 0, consistent with imaging and some VMAT procedures. It allows for the addition of a safety margin to account for possible patient position inconsistencies. This program outputs the gantry angles of collision, if any, and couch shifts necessary for clearance. This can be used to detect collisions during CBCT and determine the potential “treatment collision-free space” at the CT simulator for treatment planning. The second program takes a specific gantry-couch angle combination and produces a colormap of the patient geometry to indicate minimum clearance distance and highlight potential collisions. This is useful to assess valid treatment geometries for planning noncoplanar beams. Results: We successfully calculated the collision-free space and gantrycouch angle combinations, as well as the necessary couch shifts to avoid collision using the software package presented. The results were validated in the treatment room with measurements on a phantom. Conclusions: This software allows for reliable and efficient collision detection using an accurate model of the full patient space. The implementation of this package in the clinic can lead to improved patient safety, elimination of machine collisions during treatment and reduction of treatment planning and delivery times. Author Disclosure: L. Padilla: None. E.A. Pearson: None. C. Pelizzari: E. Research Grant; Varian Medical Systems. K. Advisory Board; Member of scientific advisory board of Reflexion Medical. N. Stock Options; Reflexion Medial.
3573 Is Surface Registration Accurate Enough for Patient Setup in Head and Neck Radiation Therapy? Y. Kim,1,2 C. Jenkins,1 Y. Na,1 R. Li,1 and L. Xing1; 1Radiation Oncology, Stanford University School of Medicine, Stanford, CA, 2Center for Bionics, Korea Institute of Science and Technology, Seoul, Korea, Republic of Korea Purpose/Objective(s): Recently, 3D optical surface imaging (OSI) has been applied to patient setup in radiation therapy (RT). The OSI system for patient setup has advantages compared with the conventional method using
Poster Viewing Abstracts S827 cone-beam CT (CBCT) because it is radiation free and frame-less. While the conventional CBCT method uses volumetric registration, the OSI system uses surface registration for patient positioning. Although some research has reported on the outcomes and accuracy of OSI for RT, the accuracy of the surface registration for OSI has not yet been sufficiently investigated. This study compares the relative accuracy of surface and volumetric registration in head-and-neck RT. Materials/Methods: 26 sets of planning CT and CBCT patient data were used in the experiment. For each set of data, the CBCT acquired in the course of RT was registered to the planning CT using both volumetric and surface registration algorithms. By examining the level of agreement between the two registration approaches we are able to assess the accuracy of surface registration. As input data for surface registration, patients’ skin surfaces were created by contouring patient skin from planning CT and treatment CBCT, respectively. Surface registration was performed using the iterative closest points (ICP) based on point-plane closest method. The accuracy of each method was also evaluated by digital simulation tests. Results: The digital simulation tests showed that both of the surface and volumetric registration methods are precise enough to compare the results of patient data. Based on the results of 26 patients, we found that considerable deviation between the surface registration and the volumetric registration could occur. The average translation deviation between the surface and volumetric registrations was 2.7 mm, while the maximum rootmean-square deviation was 5.2 mm. The residual deviation of the surface registration was computed to have an average of 0.9 mm and a maximum of 1.7 mm. Conclusions: This research provides an objective assessment of the accuracy of patient positioning based on surface information and sheds useful insight into the validity of surface imaging in radiation therapy. Caution should thus be taken in using an OSI system as the sole modality for patient setup because surface registration may lead to results different from those of the conventional volumetric registration. Additional measures are required to ensure the accuracy of OSI in RT patient setup. A deformable registration can be used to improve the accuracy of RT. Author Disclosure: Y. Kim: None. C. Jenkins: None. Y. Na: None. R. Li: None. L. Xing: None.
3574 Couch-Height Based Patient Setup for Abdominal Radiation Therapy S. Oohira,1 Y. Ueda,1 K. Nishiyama,1 M. Miyazaki,1 M. Isono,1 T. Katsutomo,1 M. Takashina,2 M. Koizumi,2 K. Kawanabe,1 and T. Teruki1; 1Department of Radiation Oncology, Osaka Medical Center for Cancer and Cardiovascular Diseases, Osaka, Japan, 2 Department of Medical Physics and Engineering, Osaka University Graduate School of Medicine, Osaka, Japan Purpose/Objective(s): To compare the accuracy of couch-height based patient set-up (CHPS) with conventional skin mark patient set-up (SMPS) for abdominal radiation therapy. Materials/Methods: 18 patients with pancreatic cancer were set-up using a body immobilization device and underwent CT simulation. Anterior and bilateral skin marks corresponding to the isocenter were placed and a distance between the isocenter and couch top was measured. At treatment, the couch was set at the level that was indicated at CT simulation (CHPS). The difference in couch level between CHPS and SMPS was measured (SMPS-CHPS shift). Under CHPS, a time-integrated electronic portal image (TI-EPI) was acquired using right or left treatment beam. Off-line manual bone matching was performed using the TI-EPI and the corresponding digitally reconstructed
Scientific Abstract 3574; Table S, s and M in A-P direction for SMPS, SMPS/NAL, SMPS/eNAL and CHPS S (mm) s (mm) M (mm)
SMPS
SMPS/NAL
SMPS/eNAL
CHPS
4.0 2.6 9.8
1.8 3.2 5.9
0.8 3.2 3.8
1.0 0.8 2.5