G Bednarz

WE-G-BRA-08: Failure Modes and Effects Analysis (FMEA) for Gamma Knife Radiosurgery

Purpose: To perform a failure modes and effects analysis (FMEA) study for Gamma Knife (GK) radiosurgery processes at our institution based on our experience with the treatment of more than 13,000 patients. Methods: A team consisting of medical physicists, nurses, radiation oncologists, neurosurgeons at the University of Pittsburgh Medical Center and an external physicist expert was formed for the FMEA study. A process tree and a failure mode table were created for the GK procedures using the Leksell GK Perfexion and 4C units. Three scores for the probability of occurrence (O), the severity (S), and the probability of no detection (D) for failure modes were assigned to each failure mode by each professional on a scale from 1 to 10. The risk priority number (RPN) for each failure mode was then calculated (RPN = OxSxD) as the average scores from all data sets collected. Results: The established process tree for GK radiosurgery consists of 10 sub-processes and 53 steps, including a sub-process for frame placement and 11 steps that are directly related to the frame-based nature of the GK radiosurgery. Out of the 86 failure modes identified, 40 failure modes are GK specific, caused by the potential for inappropriate use of the radiosurgery head frame, the imaging fiducial boxes, the GK helmets and plugs, and the GammaPlan treatment planning system. The other 46 failure modes are associated with the registration, imaging, image transfer, contouring processes that are common for all radiation therapy techniques. The failure modes with the highest hazard scores are related to imperfect frame adaptor attachment, bad fiducial box assembly, overlooked target areas, inaccurate previous treatment information and excessive patient movement during MRI scan. Conclusion: The implementation of the FMEA approach for Gamma Knife radiosurgery enabled deeper understanding of the overall process among all professionals involved in the care of the patient and helped identify potential weaknesses in the overall process.

TH‐C‐ValB‐03: Quality Assurance Procedure for a KV Cone‐Beam Device

Purpose: To establish a quality assurance procedure for kV cone‐beam (CB) devices, and use the procedure to assess the accuracy and precision of a particular CB unit. Introduction: The introduction of the Elekta CB system has created a need for a quality assurance (QA) procedure to test the systems reliability and accuracy and its alignment with the MV treatment beam isocenter. Materials and Methods: An acrylic phantom with dimensions of 18×19×18 cm3 was used for this work. The phantom included a central slab made of polystyrene that contained two drill holes. One was drilled to the center of the cube, and the other was off to one side and established phantom orientation during scanning. The holes were left open during CT and CB imaging to minimize artifacts. The phantom was first positioned with the center hole at the accelerator mechanical isocenter. Repeated cone‐beam datasets were acquired to determine the systems ability to detect and correct for known table shifts. Finally, the phantom was positioned with CB guidance and the beebee was placed in the center hole. The Winston‐Lutz (WL) test in the phantom was preformed using the electronic portal imager(EPID). The differences between known and CB determined shifts and the WL test analysis were tabulated. Results: and Conclusion: The results showed that the cone‐beam system was capable of determining the shifts applied to the QA phantom to within 0.5 ± 0.4 mm with the largest difference between the known and calculated shift of 1.9 mm. The mean agreement between the kV and MV isocenters was 0.5 mm ± 0.4 mm with the largest deviation of 1.3 mm. This QA study supports a conclusion that the Elekta CB system can be reliably used for positioning of patients with the accuracy in a 1 to 2 mm range.

SU-E-J-261: Statistical Analysis and Chaotic Dynamics of Respiratory Signal of Patients in BodyFix

PURPOSE To quantify respiratory signal of patients in BodyFix undergoing 4DCT scan with and without immobilization cover. METHODS 20 pairs of respiratory tracks recorded with RPM system during 4DCT scan were analyzed. Descriptive statistic was applied to selected parameters of exhale-inhale decomposition. Standardized signals were used with the delay method to build orbits in embedded space. Nonlinear behavior was tested with surrogate data. Sample entropy SE, Lempel-Ziv complexity LZC and the largest Lyapunov exponents LLE were compared. RESULTS Statistical tests show difference between scans for inspiration time and its variability, which is bigger for scans without cover. The same is for variability of the end of exhalation and inhalation. Other parameters fail to show the difference. For both scans respiratory signals show determinism and nonlinear stationarity. Statistical test on surrogate data reveals their nonlinearity. LLEs show signals chaotic nature and its correlation with breathing period and its embedding delay time. SE, LZC and LLE measure respiratory signal complexity. Nonlinear characteristics do not differ between scans. CONCLUSION Contrary to expectation cover applied to patients in BodyFix appears to have limited effect on signal parameters. Analysis based on trajectories of delay vectors shows respiratory system nonlinear character and its sensitive dependence on initial conditions. Reproducibility of respiratory signal can be evaluated with measures of signal complexity and its predictability window. Longer respiratory period is conducive for signal reproducibility as shown by these gauges. Statistical independence of the exhale and inhale times is also supported by the magnitude of LLE. The nonlinear parameters seem more appropriate to gauge respiratory signal complexity since its deterministic chaotic nature. It contrasts with measures based on harmonic analysis that are blind for nonlinear features. Dynamics of breathing, so crucial for 4D-based clinical technologies, can be better controlled if nonlinear-based methodology, which reflects respiration characteristic, is applied. Funding provided by Varian Medical Systems via Investigator Initiated Research Project.

SU‐E‐T‐578: Dose Differences Between the Three Dose Calculation Algorithms in Leksell GammaPlan

PURPOSE To evaluate the dose differences introduced by the TMR10 and the convolution dose calculation algorithms in Gamma Plan version 10. METHODS A target with a prescription dose of 20Gy was defined on a human head CT image set and the treatment times for single collimator, single shot placement were calculated using the 3 dose calculation algorithms in GammaPlan. Three comparative studies were conducted: first, the matrix position is varied every 20mm in the X and the Y directions on the central slice (Z = 100mm) and the shot times were compared on each matrix for all collimators of a Perfexion unit. A total of 55 matrix positions were identified; second, the study was repeated for all the 4 collimators of a 4C unit; third, the comparison was made for the 8mm collimator of the Perfexion unit on the transverse slices with Z = 20,40,60,80,100,120,140,160. A total of 312 matrix positions were included. RESULTS The treatment times from TMR10 and TMR classic agree within ±2.5% for all the treatment shots using all the 7 collimators from both machines. The time differences between the convolution and the TMR classic are similar on each matrix for all the 7 collimators but depend on the location of the treatment shots. We identified a maximum decrease in delivered dose of 11.5% for treatment in the superior frontal/parietal vertex region for older calculations lacking inhomogeneity correction to account for the greater percentage of the average beam path occupied by bone. The differences in the inferior temporal lobe and the cerebellum/neck regions are significantly less, owing to the counter-balancing effects of both bone and the air-cavity inhomogeneities. CONCLUSION Dose prescriptions based on the experiences with the TMR classic may need to be adjusted to accommodate the up to 11.5% difference between the convolution and the TMR classic.

SU‐E‐J‐161: Biomechanical Framework for Thoracic Tumors Characteristics

PURPOSE Respiration-induced kinematics of thoracic tumors suggests a simple analogy with elasticity, where a strain is used to characterize the volume of interests. The application of the biomechanical framework allows for the objective determination of tumor characteristics. METHODS The deformation of a given tissue element can be determined if its displacement is known. The latter can be obtained from 4DCT scans using image registration of the end of inhalation and exhalation CT volumes. The averaged right Cauchy-Green strain tensor was determined for each of the 15 retrospectively analyzed thoracic GTVs. The departure of the strain tensor from the identity matrix gauges the departure of the medium from rigidity. The averaging was carried out in Log-Euclidean framework. The fractional and geodesic anisotropy factors were determined for the tensor. RESULTS The amplitude of GTV motion varied from 0.64 to 4.21 with the average of 1.2cm. The GTV size ranged from 5.16 to 149.99cc with the average of 43.19cc. The tumor deformation is inconsiderable and the tensorial anisotropy is small. The Log-Euclidean distance of averaged strain tensors from the identity matrix ranged from 0.06 to 0.31 with the average of 0.19. The Frobenius distance from the identity matrix is similar and ranged from 0.06 to 0.35 with the average of 0.21. Their fractional anisotropy ranged from 0.02 to 0.12 with the average of 0.07. Their geodesic anisotropy ranged from 0.03 to 0.16 with the average of 0.09. These values also indicate insignificant deformation. CONCLUSION The biomechanical framework allows for the quantitative description of the tissue or anatomical regions of interest. Such regional characterization of volume of interests can be used in the objective evaluation of changes of the anatomy during the treatment or after the treatment. It might be used for correlation of outcome studies with objective characterization of changes within biomechanical framework. These objective characteristics do not rely on human interpretation. The measured changes might have predictive characteristics for the therapeutic success of the treatment.

SU-E-J-34: Tensor-Based Measure of Tumor Deformation

Purpose: To present a tensorial based method for evaluation of tumor deformation. Method and Materials:Image registration provides a displacement field for each voxel and as such provides also a deformation field. The Jacobian of the deformation field reveals the volume changes. However even with Jacobian =1, a voxel might deform. We use the Green‐St Venan strain tensor to quantify the tumor deformation. The tensor is obtained with the in‐house implemented elastic image registration. The Gaussian Pyramid of registered CT volumes allows for speedup of the registration as well as for addressing larger displacement. We use retrospectively ten cases of lungcancer patients for whom a pre‐tretamnt 4 DCT was obtained. The use of the tensor allows for factoring out rotation and translation. This in turns allows for the measurement of the pure deformation. Results: Only three GTVs were observed to deform between phase 50 and phase 0. The relative maximal shape change as quantified by the average ratio of the tensorial elipsoid radii was 30%. The average tumor motion for the cases was 1.6 cm. Conclusion: The method allows for improved measurement of intra fractional tumor deformation as contrasted with methods based on contour or volume comparison. It can be adopted to the measurement of the inter fractional changes in tumor shape and size if relevant CT scans are available.

SU‐E‐J‐27: 4DCT‐Derived Treatment Planning Scan with Improved Quality

Purpose: To improve the quality of the treatment planningCT volume for cancer occurring in upper abdomen. Method and Materials:Delineation of tumors in liver or pancreas critically depends on the image quality due to rather weak radio‐opacity difference between the tumor and surrounding tissue. Since for cases with tumor motion > 0.5cm, phase 50% scan is used for treatment planning the quality of this image is worse than its helical equivalent. Thus in order to improve Signal to Noise ratio (SNR) and Contrast to Noise ratio (CNR) of the phase 50 scan a synchronized averaging of the entire 4DCT data set is applied to create a composite CT volume equivalent to phase 50 scan. Four dimensional CT scans of ten patients with liver and pancreas cancer were used retrospectively in this study. In‐house implementation of Demons algorithm allowed for adding the deformed CT phases to phase 50% scan. Results: Improved SNR for all cases was observed. Average improvement of SNR for all cases in the region of interest was by a factor of 2.8. The scan also look better for a visual inspection. Conclusion: Synchronized averaging of the 4 DCT scan can be used to obtain better quality treatment planning scans. However possible artefacts in 4DCT phases might preclude effective use of the entire set of CT phases.

SU‐GG‐T‐279: Current Practice in Small Radiosurgery Field Dosimetry — Preliminary Results from 21 Centers Participating in the International Leksell Gamma Knife Calibration Survey

Purpose: To investigate current practice in calibration of small Leksell Gamma Knife (LGK) radiosurgery fields and measure output of the surveyed LGK units using alanine dosimeter. Methods and Materials: Each participant of the project received a LGK calibration questionnaire addressing following information: LGK model, calibration protocol used, phantom used, ion chamber used, LGK calibration personnel, independent verification of calibration and collimator relative output factor values. Alanine dosimeters evaluated with a Bruker ECS106 Electron Paramagnetic Resonancespectrometer using the protocol described in the NIST Ionizing Radiation Division Quality System Manual were used to measure the dose rate of the surveyed LGK units. Results: To date, 21 LGK units have participated in this project (North America 9, Europe 5 and Asia 7).The calibration protocols used for surveyed sites were: AAPM TG21 9 sites, IAEA TRS277 1 site and IAEA TRS398 11 sites. ELEKTA ABS spherical phantom was used in 18 cases and ELEKTA solid water phantom in 3 cases. Following ion chambers were used for LGK calibration: PTW 31010 (0.125 cm3) 9 times, Exradin A16 (0.070 cm3) 2 times, PTW 31006 (0.125 cm3) 1 times, Capintec A1SL1 (0.070 cm3) 2 times, Wellhoffer IC‐10 (0.125 cm3) 1 time, Exradin A1SL 1 time and Exradin A14SL 1 time. Calibration of LGK units was performed by an on‐site physicist in 15 cases and by ELEKTA physicist in 6 cases. Independent verification was done in 8 cases out of total 21 surveyed units. All LGK units surveyed are currently using the ELEKTA default values for collimator relative output factors. Conclusions: The range of dose planned to measured ratio was 0.972 – 1.030 demonstrating reasonably good LGK calibration consistency. Mean absolute value percentage deviation between planned and measured dose was 1.34 – 0.84 %. This project is ongoing and more information will be provided at its conclusion.

SU‐GG‐T‐540: Intensity‐Modulated Arc Therapy for Stereotactic Radiotherapy of Spinal & Paraspinal Tumors

Purpose: Stereotactic radiosurgery of metastatic spinal tumor with intensity‐modulated radiotherapy(IMRT) technique requires a long treatment time due to an extensive monitor units (MU) resulting from multiple highly intensity‐modulated beams in order to sparing adjacent spinal cord and other critical structures. This study investigates the feasibility of using intensitymodulated arc therapy (IMAT) as an alternative modality with a shorter treatment time while maintaining a compatible dosimetric performance as IMRT technique. Methods/Materials: 8 patients with spinal or paraspinal tumor were recruited in this study. All those patients were previously treated with IMRT technique, in which 18Gy or 24Gy doses were delivered in a single fraction with 11 to 13 coplanar radiation beams. Single arc and 2‐arc IMAT plans were retrospectively generated for each patients using RapidArcTM treatment planning system (Varian Medical System, Sunnyvale, CA). The previous delivered IMRT plans were chosen as a reference. The differences of following parameters between IMAT and IMRT plans were used to evaluate the plan performance: the volumes of PTV receiving 95% and 100% of prescribed dose(V95, V100), the maximum spinal cord dose (MSPDOSE) and the total monitor units (TMU). Results: For all 8 patients, the differences of V95 and V100 between single arc IMAT and IMRT plans are −5.3%±4.8% and −9.3%±7.8% , while the difference of MSPDOSE is 0.23Gy±0.87Gy. In contrary, the differences of V95 and V100 between 2‐arc IMAT and IMRT plans are −0.67%±2.01%, −1.1%±2.23% , while the difference of MSPDOSE is 0.38Gy±0.47GY. The ratios of TMU of single arc and 2‐arc IMAT plans over IMRT plan are 55%±19% and 65%±17%. Conclusion: For stereotactic radiosurgery of spinal tumor,IMRT plan provide better dose coverage than single arc IMAT plan, but 2‐arc IMAT plan is capable of providing a compatible dosimetric performance as IMRT plan while significantly reducing the treatment time.

SU‐GG‐I‐126: An Adaptivie Regularization for the Demons Algorithm

Purpose: An examination of the regularization methods of the deformation vector field obtained with the “Demons” algorithm. The application of the adaptive smoothing for incremental variability of the similarity between the source and target image and its effect on the general characteristics of the transformation map. Method and Materials: The synthetic images are used for the numerical experiments. An adaptive iterative smoothing of the deformation field computed with the “Demons” algorithm is examined. The transformation map is examined with respect to the degree of the dissimilarity between the matched images and adaptive filtering . The standard Gaussian filtering with varying standard deviations (σ) is used for the adaptive smoothing. The magnitudes if the deformation vectors and the smoothness characteristics of the deformation maps are examined. Results: The deformation map reflecting the degree of the dissimilarity between the source and the target image gains truthfulness after application of the adaptive regularization. The real magnitude of the deformation between registered objects affects the effectiveness of the filtering. Since this value is not known an arbitrary a priori selection of the Gaussian filter is never optimal. The larger the filters σ the smaller the magnitude of the deformation vectors is obtained. An inverse trend is observed for the magnitude of the maps standard deviation. The convergence rate of the algorithm is affected by the selection of the given σ. The mean squared sum of intensity differences measures the images similarity. Conclusion: The application of the adaptive regularization of the deformation field reflects the varying scales of the real deformation and how the algorithm is parametrically allowed to accommodate these differences during the iteration. This idea might be extended to anisotropic adaptive filtering to accommodate inhomogeneity of the real deformation at a given resolution scale.