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4D-MRI with Iterative Motion Correction and Averaging Improves Image SNR and Reduces Streaking Artifacts without Compromising Tumor Motion Trajectory
S62
International Journal of Radiation Oncology Biology Physics
designed RF doors. The separation between the Linac and MR was chosen to allow for a full magnetic field decoupling between all sub-components. With the MR at the imaging position the Linac/couch can be operated independently without posing safety risks or sub-optimal performance of the Linac/couch or MR scanner. The patient is moved between MR diagnostic table and Linac table via a patient transfer system operated as a hovercraft device. The MR/Linac configuration was assessed by considering the following aspects: a) magnetic field decoupling by experimental measurements e i.e. field mapping with Hall probes, magnetic pull forces on the Linac table, radiation beam specifications - output/flatness/symmetry, Linac on-board imaging, MR shimming and overall imaging performance e b) RF noise isolation (survey, MR testing), c) safety systems (interlocking system, redundancy measures), d) RT workflows (patient transfer, MR data flow, MR to kV matching), and QC procedures (MR image quality and B0 mapping, Linac/MR). Numerical simulations based on finite elements methods were also performed to model the entire MRLinac environment including the Linac table. The MR scanner field was generated using a LP optimization technique and the Linac/table was represented as full-scale CAD geometries. Results: The Linac beam performance and its kV imaging system were found to be unaffected by the MR presence in the treatment vault. The effects were measured and monitored over one year. The MR scanner was tested for optimal performance - the shimming and standard imaging procedures passed the clinical requirements. The numerical simulations and associated measurements for the magnetic field decoupling were in good agreement (forces, field mapping). RT workflows utilizing the kV and MR in-room imaging (independent and combined) are currently under development to facilitate the clinical integration of the system. Targeted applications are MR-based RT (adaptive) planning and guidance, fast imaging for the quantification of organ motion. Integrated quality control procedures were also developed for routine monitoring (MR shimming, image distortions e scanner-related and patient-induced, MR-to-kV matching). Conclusion: All aspects investigated support the feasibility of the MRguided RT system for clinical deployment. Author Disclosure: T. Stanescu: None. N. Schaer: None. S. Breen: None. D. Letourneau: None. K. Shet: None. C.I. Dickie: None. D.A. Jaffray: None.
were treated. Median lumpectomy volume was 5.52 mL (R: 2.48 e 23.85). Boost dosage was 10 Gy in 5 fractions in all but 1 patient, who had 4 fractions of 2.5 Gy. Dose coverage was satisfactory in all patients, with median lumpectomy planning target volume D95 Z 50.7 Gy (R: 49.2 e 61.3). Median motion during radiation treatment was 1.3mm anteriorposterior (R: 0.5 e 3) and 1.2mm cranio-caudal (R: 0.4 e 2.1). Treatment was generally well tolerated, with 5 acute G2 toxicities and 6 G1 toxicities seen. With a median follow-up time of 4 months, all patients had a good/ excellent cosmetic outcome. No local failures were observed. The number of patients, local control, and toxicity will be updated at the time of presentation. Conclusion: We have reported the results of the first use of real-time MRIguided radiotherapy allowing for imaging acquisition during radiation therapy delivery. In our prospective study, we have shown that such treatment is feasible and well tolerated with low rates of acute toxicity. Further follow-up will be needed to ascertain long-term toxicity. Finally, lumpectomy cavity motion during treatment with a free breathing technique was minimal and may support decreased planning target volume expansions in future MRI-guided breast cancer studies. These results may have implications for non-MRI guided therapy. Author Disclosure: A.P. Wojcieszynski: None. P.M. Hill: None. S.A. Rosenberg: None. C.R. Hullett: None. K.E. Mittauer: None. M.W. Geurts: None. Z.E. Labby: None. J. Bayouth: None. B.M. Anderson: None.
138 Prospective Results of Real-Time Magnetic Resonance Imaging Guided Lumpectomy Cavity Boost Treatment A.P. Wojcieszynski,1 P.M. Hill,2 S.A. Rosenberg,1 C.R. Hullett,1 K.E. Mittauer,1 M.W. Geurts,1 Z.E. Labby,2 J. Bayouth,1 and B.M. Anderson1; 1Department of Human Oncology, University of Wisconsin Hospital and Clinics, Madison, WI, 2Department of Human Oncology, University of Wisconsin, Madison, WI Purpose/Objective(s): To report on the early results of the first prospective trial of real-time MRI-guided radiotherapy for lumpectomy cavity boost and motion assessment in patients undergoing treatment for breast cancer. Materials/Methods: Inclusion criteria for this prospective IRB-approved study included patients with DCIS or breast cancer treated with breast conservation therapy. Patients underwent both CT as well as MRI-based simulation, which was performed with a breast-specific MRI coil and included axial as well as cine sagittal images. Patients received whole breast irradiation via linear accelerator, with dose and technique at the discretion of the treating radiation oncologist. A boost was then delivered to the lumpectomy cavity plus 10 mm margin, utilizing MRI guidance with imaging of the lumpectomy cavity performed prior to and during treatment. All boost fractions were delivered with free breathing technique. A detailed motion of lumpectomy cavity variability during daily setup as well as treatment was performed. Results: At the time of this writing, 11 patients have been enrolled in this prospective study. Median patient age was 57 (R: 45 e 66). Median tumor size was 1.1 cm (R: 0.7 e 3.5). Six right-sided and 5 left-sided tumors
139 4D-MRI with Iterative Motion Correction and Averaging Improves Image SNR and Reduces Streaking Artifacts without Compromising Tumor Motion Trajectory J. Pang,1 W. Yang,1 X. Bi,2 M. Fenchel,2 Z. Deng,1 Y. Chen,3 R. Tuli,4 L. Gerhard,2 D. Li,1 and Z. Fan4; 1Cedars Sinai Medical Center, Los Angeles, CA, 2Siemens Healthcare, Los Angeles, CA, 3University of Pennsylvania, Philadelphia, PA, 4Cedars-Sinai Medical Center, Los Angeles, CA Purpose/Objective(s): Four dimensional magnetic resonance (4D-MR) imaging has been increasingly used in radiation therapy. Novel 4D-MR with self-gating methods overcome spatiotemporal limitations of the conventional methods. Using motion information directly derived from self-gating signal, k-space data are retrospectively grouped into multiple bins representing different breathing phases. Because each bin only contains a subset of the acquired data, reconstructions from individual bin could suffer from low signal-to-noise ratio (SNR) and severe streaking artifacts. Reducing the number of bins alleviates the under-sampling effect within each bin, yet will introduce blurring. In this work, we applied a motion-corrected averaging (MoCoAve) method to improve the quality of 4D-MR images without compromising tumor motion information. Materials/Methods: Five patients (3 pancreatic, 1 liver, 1 lung) were scanned using two prototype 4D-MR sequences with Stack-of-Stars (SOS) (3 scans) and Koosh-Ball (KB) (4 scans) radial trajectories, respectively. The MoCoAve process was performed on the undersampled 10-phase image series. First, an arbitrary phase is chosen as the reference, and the forward and inverse transforms between all other phases and the reference are calculated using a symmetric diffeomorphic model. Then, the MoCoAve-enhanced image of each phase is produced by aligning all other phases to it using the respective combinations of the spatial transforms, and averaging all warped images. SNR (defined as signal intensity in the liver divided by the standard deviation of background air signal) and tumor motion trajectories were assessed using images reconstructed from individual bins, as well as corresponding images after MoCoAve. Tumor contours were drawn on the end of expiration phase, and then mapped to the other phases using a B-spline based deformable registration. The coordinates of the tumor centers were then extracted for motion trajectory evaluation. SNR and tumor motion trajectories before and after MoCoAve were compared using paired t test.
Volume 96 Number 2S Supplement 2016 Results: The 4D-MR images were successfully acquired from all subjects. Imaging time was 9.0 and 6.5 minutes using SOS and KB trajectories, respectively. MoCoAve method visually reduced streaking artifacts and preserved sharpness. Quantitative analysis showed significantly improved SNR (mean SD without and with MoCoAve: SOS: 7.5 2.1 vs. 21.3 5.9, P < 0.01; KB: 28.9 13.1 vs. 43.2 19.1, P < 0.01). The motion trajectories of tumors measured from 70 volumetric image pairs showed excellent agreement between images reconstructed without and with MoCoAve. Correlation coefficients were: 0.94 0.10, 0.88 0.12, and 0.74 0.16 in the SI, AP, and LR directions. Conclusion: MoCoAve, making use of the entire k-space data to reconstruct each respiratory bin, improves 4D-MR SNR and reduces streaking artifacts while preserving tumor motion trajectory. Author Disclosure: J. Pang: None. W. Yang: None. X. Bi: None. M. Fenchel: None. Z. Deng: None. Y. Chen: None. R. Tuli: None. L. Gerhard: None. D. Li: None. Z. Fan: None.
140 A Particle FiltereBased Motion Prediction Algorithm for Lung Tumors Using Dynamic Magnetic Resonance Imaging A. Bourque, S. Bedwani, E.J. Filion, and J.F. Carrier; Centre Hospitalier de l’Universite´ de Montre´al, Montreal, QC, Canada Purpose/Objective(s): This study proposes a novel motion prediction algorithm implemented in the context of radiation therapy treatments with MR-Linac machines. MRI acquisitions during treatment enable a constant visualization of the tumor with superior soft-tissue contrast. However, due to latency problems caused by communication delays and repositioning of the beam, a prediction of the subsequent position of the tumor is needed. In the case of lung tumors, the challenge to procure knowledge of the shape and position of the tumor is increased, where intrafractional movements and deformations might be significant. Materials/Methods: A 1.5 T MRI is used to acquire images for three free breathing NSCLC patients with a balanced steady-state free precession (bSSFP) sequence. The acquisition lasts one minute at a rate of four images per second, thus providing a total of 240 images per patient. A particle filter method based on histograms is used to predict the centroid location of the tumor, and is independent of any preliminary training on the data sets. The Bhattacharyya distance is calculated to compare the similarity between the model histogram and the histogram related to each particle. A contour is also automatically performed on each acquired image and is transposed at the predicted location of the tumor’s centroid. The prediction model is finally compared a posteriori with the contours of an expert. The comparison includes the root mean square errors between centroid positions and the mean errors on distances along anterior-posterior and inferior-superior axis. Results: The root mean square errors are 2.2 mm, 2.9 mm, and 0.9 mm for patient 1, 2, and 3, respectively. The mean errors in the anterior-posterior axis are (-0.6 1.2) mm, (-0.1 1.5) mm and (-0.3 0.4) mm while they are of (-0.2 1.7) mm, (0.1 2.5) mm, and (-0.3 0.6) mm in inferiorsuperior for patients 1, 2, and 3, respectively. In all cases, the addition of a 4 mm uniform margin on the predicted contour of the tumor provides coverage of the GTV at all time. Conclusion: The accuracy of this algorithm is dependent on the amplitude of movement of the tumor and its shape. The prediction error increases when the tumor changes direction abruptly and it is reflected in larger standard deviations in the inferior-superior direction. The linear dynamic model used in the implementation of the particle filter implies that optimal results would be obtained for patients breathing slowly and regularly or by using a higher imaging rate. Moreover, retrospective analyses of the prediction errors can provide patient specific margins considering the complexity of the tumor’s motion. To conclude, this particle filter based motion prediction algorithm offers a promising and adaptable method for lung tumor online tracking treatments.
Oral Scientific Sessions
S63
Author Disclosure: A. Bourque: None. S. Bedwani: None. E.J. Filion: None. J. Carrier: None.
141 Evaluation of a 4D Ultrasound (US) Real-Time Tracking System for SBRT of Upper Abdominal Lesions in Healthy Volunteers Under Computer-Controlled Breath Hold: A Correlation of Ultrasound and Surface Motion Data D.S.K. Sihono,1 C. Weiss,2 L. Vogel,1 S. Kegel,1 J. Tho¨lking,1 F. Wenz,1 H. Wertz,1 F. Lohr,3 and J. Boda-Heggemann1; 1Department of Radiation Oncology, Universita¨tsmedizin Mannheim, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany, 2Abteilung fuer Medizinische Statistik, Biomathematik und Informationsverarbeitung, Universita¨tsmedizin Mannheim, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany, 3Department of Radiation Oncology, Modena, Modena, Italy Purpose/Objective(s): Hypofractionated SBRT (Stereotactic Body Radiotherapy) is an effective therapy option for liver metastases with low toxicity. Due to respiratory motion, motion management methods are under development to allow precise irradiation of the target and optimal OAR sparing. In our department, liver SBRT is performed in computercontrolled DIBH (Deep Inspiratory Breath Hold) and image-guidance with breath-hold Cone-beam CT. However, residual errors, intrafractional reproducibility of the breath-hold and organ position stability still has to be considered. For additional intrafractional monitoring of target and/or surrogate structures an ultrasound (US)-based tracking system has been developed. We evaluated the applicability of this system in a clinical setup on healthy volunteers (HV) and correlated motion of the US-tracked structures with the motion of an infrared-detected surface marker. Materials/Methods: Five healthy volunteers (HV, after permission of the local IRB) were set up simulating the clinical situation to obtain the performance of US tracking. US datasets were acquired in computercontrolled breath hold (BH, time 20 sec, free breathing phases of 5-6 breath cycles). Tracked structures were renal pelvis (centroid structure) and a portal vein branches (non-centroid structure). US scanning range was varied (40, 25, and 10 degrees). US motion component in superior-inferior direction was compared with the motion of an external stray marker on the body surface. A statistical correlation of stray and US target motion during breath holds was estimated by the Pearson correlation coefficient. Results: The setup with the arm-based fixation of the US probe and combination with computer-controlled DIBH could be easily performed in all HVs without inconvenience and was stable during test sessions of 30 mins. Image quality was in all cases sufficient for definition of US-tracking target and tracking during DIBH. Success rates to track the target were 94.56% and 92.75% for renal pelvis and portal vein branches, respectively. US tracking in breath-hold phases correlated well with the external marker movement. Over all HV and scan ranges, Pearson correlation coefficient (PCC) was strong for most of the 61 breath holds (PCC Z 0.71-0.99). Only 5 breath-holds showed weak correlation (PCC Z 0.53-0.65) and 2 breath-holds showed no statistical correlation (PCC Z 0.33-0.48). Scan range and HV did not have a statistically significant effect on the correlation (P values 0.74 and 0.129). Conclusion: The ultrasound tracking system is applicable to a clinical setup with the chosen hardware solution. Ultrasound motion data show a strong correlation with surface motion data for most of individual breath holds and across all breath-holds, if phases of unsuccessful tracking were excluded from the analysis. Further tests on patients in real treatment situation with a possibility to compare with CBCT data are under way. Author Disclosure: D.S. Sihono: None. C. Weiss: None. L. Vogel: None. S. Kegel: None. J. Tho¨lking: None. F. Wenz: Research Grant; Elekta. Honoraria; Elekta. Chairman; Department of Radiation Oncology. H. Wertz: Research Grant; Elekta. Honoraria; Elekta. Travel Expenses; Elekta. F. Lohr: None. J. Boda-Heggemann: Research Grant; Elekta. Honoraria; Elekta. Travel Expenses; Elekta.
International Journal of Radiation Oncology Biology Physics
designed RF doors. The separation between the Linac and MR was chosen to allow for a full magnetic field decoupling between all sub-components. With the MR at the imaging position the Linac/couch can be operated independently without posing safety risks or sub-optimal performance of the Linac/couch or MR scanner. The patient is moved between MR diagnostic table and Linac table via a patient transfer system operated as a hovercraft device. The MR/Linac configuration was assessed by considering the following aspects: a) magnetic field decoupling by experimental measurements e i.e. field mapping with Hall probes, magnetic pull forces on the Linac table, radiation beam specifications - output/flatness/symmetry, Linac on-board imaging, MR shimming and overall imaging performance e b) RF noise isolation (survey, MR testing), c) safety systems (interlocking system, redundancy measures), d) RT workflows (patient transfer, MR data flow, MR to kV matching), and QC procedures (MR image quality and B0 mapping, Linac/MR). Numerical simulations based on finite elements methods were also performed to model the entire MRLinac environment including the Linac table. The MR scanner field was generated using a LP optimization technique and the Linac/table was represented as full-scale CAD geometries. Results: The Linac beam performance and its kV imaging system were found to be unaffected by the MR presence in the treatment vault. The effects were measured and monitored over one year. The MR scanner was tested for optimal performance - the shimming and standard imaging procedures passed the clinical requirements. The numerical simulations and associated measurements for the magnetic field decoupling were in good agreement (forces, field mapping). RT workflows utilizing the kV and MR in-room imaging (independent and combined) are currently under development to facilitate the clinical integration of the system. Targeted applications are MR-based RT (adaptive) planning and guidance, fast imaging for the quantification of organ motion. Integrated quality control procedures were also developed for routine monitoring (MR shimming, image distortions e scanner-related and patient-induced, MR-to-kV matching). Conclusion: All aspects investigated support the feasibility of the MRguided RT system for clinical deployment. Author Disclosure: T. Stanescu: None. N. Schaer: None. S. Breen: None. D. Letourneau: None. K. Shet: None. C.I. Dickie: None. D.A. Jaffray: None.
were treated. Median lumpectomy volume was 5.52 mL (R: 2.48 e 23.85). Boost dosage was 10 Gy in 5 fractions in all but 1 patient, who had 4 fractions of 2.5 Gy. Dose coverage was satisfactory in all patients, with median lumpectomy planning target volume D95 Z 50.7 Gy (R: 49.2 e 61.3). Median motion during radiation treatment was 1.3mm anteriorposterior (R: 0.5 e 3) and 1.2mm cranio-caudal (R: 0.4 e 2.1). Treatment was generally well tolerated, with 5 acute G2 toxicities and 6 G1 toxicities seen. With a median follow-up time of 4 months, all patients had a good/ excellent cosmetic outcome. No local failures were observed. The number of patients, local control, and toxicity will be updated at the time of presentation. Conclusion: We have reported the results of the first use of real-time MRIguided radiotherapy allowing for imaging acquisition during radiation therapy delivery. In our prospective study, we have shown that such treatment is feasible and well tolerated with low rates of acute toxicity. Further follow-up will be needed to ascertain long-term toxicity. Finally, lumpectomy cavity motion during treatment with a free breathing technique was minimal and may support decreased planning target volume expansions in future MRI-guided breast cancer studies. These results may have implications for non-MRI guided therapy. Author Disclosure: A.P. Wojcieszynski: None. P.M. Hill: None. S.A. Rosenberg: None. C.R. Hullett: None. K.E. Mittauer: None. M.W. Geurts: None. Z.E. Labby: None. J. Bayouth: None. B.M. Anderson: None.
138 Prospective Results of Real-Time Magnetic Resonance Imaging Guided Lumpectomy Cavity Boost Treatment A.P. Wojcieszynski,1 P.M. Hill,2 S.A. Rosenberg,1 C.R. Hullett,1 K.E. Mittauer,1 M.W. Geurts,1 Z.E. Labby,2 J. Bayouth,1 and B.M. Anderson1; 1Department of Human Oncology, University of Wisconsin Hospital and Clinics, Madison, WI, 2Department of Human Oncology, University of Wisconsin, Madison, WI Purpose/Objective(s): To report on the early results of the first prospective trial of real-time MRI-guided radiotherapy for lumpectomy cavity boost and motion assessment in patients undergoing treatment for breast cancer. Materials/Methods: Inclusion criteria for this prospective IRB-approved study included patients with DCIS or breast cancer treated with breast conservation therapy. Patients underwent both CT as well as MRI-based simulation, which was performed with a breast-specific MRI coil and included axial as well as cine sagittal images. Patients received whole breast irradiation via linear accelerator, with dose and technique at the discretion of the treating radiation oncologist. A boost was then delivered to the lumpectomy cavity plus 10 mm margin, utilizing MRI guidance with imaging of the lumpectomy cavity performed prior to and during treatment. All boost fractions were delivered with free breathing technique. A detailed motion of lumpectomy cavity variability during daily setup as well as treatment was performed. Results: At the time of this writing, 11 patients have been enrolled in this prospective study. Median patient age was 57 (R: 45 e 66). Median tumor size was 1.1 cm (R: 0.7 e 3.5). Six right-sided and 5 left-sided tumors
139 4D-MRI with Iterative Motion Correction and Averaging Improves Image SNR and Reduces Streaking Artifacts without Compromising Tumor Motion Trajectory J. Pang,1 W. Yang,1 X. Bi,2 M. Fenchel,2 Z. Deng,1 Y. Chen,3 R. Tuli,4 L. Gerhard,2 D. Li,1 and Z. Fan4; 1Cedars Sinai Medical Center, Los Angeles, CA, 2Siemens Healthcare, Los Angeles, CA, 3University of Pennsylvania, Philadelphia, PA, 4Cedars-Sinai Medical Center, Los Angeles, CA Purpose/Objective(s): Four dimensional magnetic resonance (4D-MR) imaging has been increasingly used in radiation therapy. Novel 4D-MR with self-gating methods overcome spatiotemporal limitations of the conventional methods. Using motion information directly derived from self-gating signal, k-space data are retrospectively grouped into multiple bins representing different breathing phases. Because each bin only contains a subset of the acquired data, reconstructions from individual bin could suffer from low signal-to-noise ratio (SNR) and severe streaking artifacts. Reducing the number of bins alleviates the under-sampling effect within each bin, yet will introduce blurring. In this work, we applied a motion-corrected averaging (MoCoAve) method to improve the quality of 4D-MR images without compromising tumor motion information. Materials/Methods: Five patients (3 pancreatic, 1 liver, 1 lung) were scanned using two prototype 4D-MR sequences with Stack-of-Stars (SOS) (3 scans) and Koosh-Ball (KB) (4 scans) radial trajectories, respectively. The MoCoAve process was performed on the undersampled 10-phase image series. First, an arbitrary phase is chosen as the reference, and the forward and inverse transforms between all other phases and the reference are calculated using a symmetric diffeomorphic model. Then, the MoCoAve-enhanced image of each phase is produced by aligning all other phases to it using the respective combinations of the spatial transforms, and averaging all warped images. SNR (defined as signal intensity in the liver divided by the standard deviation of background air signal) and tumor motion trajectories were assessed using images reconstructed from individual bins, as well as corresponding images after MoCoAve. Tumor contours were drawn on the end of expiration phase, and then mapped to the other phases using a B-spline based deformable registration. The coordinates of the tumor centers were then extracted for motion trajectory evaluation. SNR and tumor motion trajectories before and after MoCoAve were compared using paired t test.
Volume 96 Number 2S Supplement 2016 Results: The 4D-MR images were successfully acquired from all subjects. Imaging time was 9.0 and 6.5 minutes using SOS and KB trajectories, respectively. MoCoAve method visually reduced streaking artifacts and preserved sharpness. Quantitative analysis showed significantly improved SNR (mean SD without and with MoCoAve: SOS: 7.5 2.1 vs. 21.3 5.9, P < 0.01; KB: 28.9 13.1 vs. 43.2 19.1, P < 0.01). The motion trajectories of tumors measured from 70 volumetric image pairs showed excellent agreement between images reconstructed without and with MoCoAve. Correlation coefficients were: 0.94 0.10, 0.88 0.12, and 0.74 0.16 in the SI, AP, and LR directions. Conclusion: MoCoAve, making use of the entire k-space data to reconstruct each respiratory bin, improves 4D-MR SNR and reduces streaking artifacts while preserving tumor motion trajectory. Author Disclosure: J. Pang: None. W. Yang: None. X. Bi: None. M. Fenchel: None. Z. Deng: None. Y. Chen: None. R. Tuli: None. L. Gerhard: None. D. Li: None. Z. Fan: None.
140 A Particle FiltereBased Motion Prediction Algorithm for Lung Tumors Using Dynamic Magnetic Resonance Imaging A. Bourque, S. Bedwani, E.J. Filion, and J.F. Carrier; Centre Hospitalier de l’Universite´ de Montre´al, Montreal, QC, Canada Purpose/Objective(s): This study proposes a novel motion prediction algorithm implemented in the context of radiation therapy treatments with MR-Linac machines. MRI acquisitions during treatment enable a constant visualization of the tumor with superior soft-tissue contrast. However, due to latency problems caused by communication delays and repositioning of the beam, a prediction of the subsequent position of the tumor is needed. In the case of lung tumors, the challenge to procure knowledge of the shape and position of the tumor is increased, where intrafractional movements and deformations might be significant. Materials/Methods: A 1.5 T MRI is used to acquire images for three free breathing NSCLC patients with a balanced steady-state free precession (bSSFP) sequence. The acquisition lasts one minute at a rate of four images per second, thus providing a total of 240 images per patient. A particle filter method based on histograms is used to predict the centroid location of the tumor, and is independent of any preliminary training on the data sets. The Bhattacharyya distance is calculated to compare the similarity between the model histogram and the histogram related to each particle. A contour is also automatically performed on each acquired image and is transposed at the predicted location of the tumor’s centroid. The prediction model is finally compared a posteriori with the contours of an expert. The comparison includes the root mean square errors between centroid positions and the mean errors on distances along anterior-posterior and inferior-superior axis. Results: The root mean square errors are 2.2 mm, 2.9 mm, and 0.9 mm for patient 1, 2, and 3, respectively. The mean errors in the anterior-posterior axis are (-0.6 1.2) mm, (-0.1 1.5) mm and (-0.3 0.4) mm while they are of (-0.2 1.7) mm, (0.1 2.5) mm, and (-0.3 0.6) mm in inferiorsuperior for patients 1, 2, and 3, respectively. In all cases, the addition of a 4 mm uniform margin on the predicted contour of the tumor provides coverage of the GTV at all time. Conclusion: The accuracy of this algorithm is dependent on the amplitude of movement of the tumor and its shape. The prediction error increases when the tumor changes direction abruptly and it is reflected in larger standard deviations in the inferior-superior direction. The linear dynamic model used in the implementation of the particle filter implies that optimal results would be obtained for patients breathing slowly and regularly or by using a higher imaging rate. Moreover, retrospective analyses of the prediction errors can provide patient specific margins considering the complexity of the tumor’s motion. To conclude, this particle filter based motion prediction algorithm offers a promising and adaptable method for lung tumor online tracking treatments.
Oral Scientific Sessions
S63
Author Disclosure: A. Bourque: None. S. Bedwani: None. E.J. Filion: None. J. Carrier: None.
141 Evaluation of a 4D Ultrasound (US) Real-Time Tracking System for SBRT of Upper Abdominal Lesions in Healthy Volunteers Under Computer-Controlled Breath Hold: A Correlation of Ultrasound and Surface Motion Data D.S.K. Sihono,1 C. Weiss,2 L. Vogel,1 S. Kegel,1 J. Tho¨lking,1 F. Wenz,1 H. Wertz,1 F. Lohr,3 and J. Boda-Heggemann1; 1Department of Radiation Oncology, Universita¨tsmedizin Mannheim, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany, 2Abteilung fuer Medizinische Statistik, Biomathematik und Informationsverarbeitung, Universita¨tsmedizin Mannheim, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany, 3Department of Radiation Oncology, Modena, Modena, Italy Purpose/Objective(s): Hypofractionated SBRT (Stereotactic Body Radiotherapy) is an effective therapy option for liver metastases with low toxicity. Due to respiratory motion, motion management methods are under development to allow precise irradiation of the target and optimal OAR sparing. In our department, liver SBRT is performed in computercontrolled DIBH (Deep Inspiratory Breath Hold) and image-guidance with breath-hold Cone-beam CT. However, residual errors, intrafractional reproducibility of the breath-hold and organ position stability still has to be considered. For additional intrafractional monitoring of target and/or surrogate structures an ultrasound (US)-based tracking system has been developed. We evaluated the applicability of this system in a clinical setup on healthy volunteers (HV) and correlated motion of the US-tracked structures with the motion of an infrared-detected surface marker. Materials/Methods: Five healthy volunteers (HV, after permission of the local IRB) were set up simulating the clinical situation to obtain the performance of US tracking. US datasets were acquired in computercontrolled breath hold (BH, time 20 sec, free breathing phases of 5-6 breath cycles). Tracked structures were renal pelvis (centroid structure) and a portal vein branches (non-centroid structure). US scanning range was varied (40, 25, and 10 degrees). US motion component in superior-inferior direction was compared with the motion of an external stray marker on the body surface. A statistical correlation of stray and US target motion during breath holds was estimated by the Pearson correlation coefficient. Results: The setup with the arm-based fixation of the US probe and combination with computer-controlled DIBH could be easily performed in all HVs without inconvenience and was stable during test sessions of 30 mins. Image quality was in all cases sufficient for definition of US-tracking target and tracking during DIBH. Success rates to track the target were 94.56% and 92.75% for renal pelvis and portal vein branches, respectively. US tracking in breath-hold phases correlated well with the external marker movement. Over all HV and scan ranges, Pearson correlation coefficient (PCC) was strong for most of the 61 breath holds (PCC Z 0.71-0.99). Only 5 breath-holds showed weak correlation (PCC Z 0.53-0.65) and 2 breath-holds showed no statistical correlation (PCC Z 0.33-0.48). Scan range and HV did not have a statistically significant effect on the correlation (P values 0.74 and 0.129). Conclusion: The ultrasound tracking system is applicable to a clinical setup with the chosen hardware solution. Ultrasound motion data show a strong correlation with surface motion data for most of individual breath holds and across all breath-holds, if phases of unsuccessful tracking were excluded from the analysis. Further tests on patients in real treatment situation with a possibility to compare with CBCT data are under way. Author Disclosure: D.S. Sihono: None. C. Weiss: None. L. Vogel: None. S. Kegel: None. J. Tho¨lking: None. F. Wenz: Research Grant; Elekta. Honoraria; Elekta. Chairman; Department of Radiation Oncology. H. Wertz: Research Grant; Elekta. Honoraria; Elekta. Travel Expenses; Elekta. F. Lohr: None. J. Boda-Heggemann: Research Grant; Elekta. Honoraria; Elekta. Travel Expenses; Elekta.