Accuracy evaluation of a six-degree-of-freedom couch using cone beam CT and IsoCal phantom with an in-house algorithm

PURPOSE

The accuracy of a six degree of freedom (6DoF) couch was evaluated using a novel method.

METHODS

Cone beam CT (CBCT) images of a 3D phantom (IsoCal) were acquired with different, known combinations of couch pitch and roll angles. Pitch and roll angles between the maximum allowable values of 357 and 3 degrees were tested in one degree increments. A total of 49 combinations were tested at 0 degrees of yaw (couch rotation angle). The 3D positions of 16 tungsten carbide ball bearings (BBs), each 4 mm in diameter and arranged in a known geometry within the IsoCal phantom, were determined in the 49 image sets with in-house software. The BB positions at different rotation angles were determined using a rotation matrix from the original BB positions at zero pitch and roll angles. A linear least squares fit method estimated the rotation angles and differences between detected and nominal rotation angles were calculated. This study was conducted for the case with and without extra weight on the couch. Couch walk shifts for the system were investigated using eight combinations of rotation, roll and pitch.

RESULTS

A total of 49 CBCT images with voxel sizes 0.5 × 0.5 × 1.0 mm³ were taken for the case without extra weight on the couch. The 16 BBs were determined to evaluate the isocenter translation and rotation differences between the calculated and nominal couch values. Among all 49 calculations, the maximum rotation angle differences were 0.10 degrees for pitch, 0.15 degrees for roll and 0.09 degrees for yaw. The corresponding mean and standard deviation values were 0.028 ± 0.032, -0.043 ± 0.058, and -0.009 ± 0.033 degrees. The maximum translation differences were 0.3 mm in the left-right direction, 0.5 mm in the anterior-posterior direction and 0.4 mm in the superior-inferior direction. The mean values and corresponding standard deviations were 0.07 ± 0.12, -0.05 ± 0.25, and -0.12±0.14 mm for the planes described above. With an 80 kg phantom on the couch, the maximum translation shift was 0.69 mm. The couch walk translation shifts were less than 0.1 mm and rotation shifts were less than 0.1 degree.

CONCLUSIONS

Errors of a new 6DoF couch were tested using CBCT images of a 3D phantom. The rotation errors were less than 0.3 degree and the translation errors were less than or equal to 0.8 mm in each direction. This level of accuracy is warranted for clinical radiotherapy utilization including stereotactic radiosurgery.

Are pitch and roll compensations required in all pathologies? A data analysis of 2945 fractions

OBJECTIVE

New linear accelerators can be equipped with a 6D robotic couch, providing two additional rotational motion axes: pitch and roll. These shifts in kilo voltage-cone beam CT (kV-CBCT) image-guided radiotherapy (IGRT) were evaluated over the first 6 months of usage of a 6D robotic couch-top, ranking the treatment sites for which the two compensations are larger for patient set-up.

METHODS

The couch compensations of 2945 fractions for 376 consecutive patients treated on the PerfectPitch™ 6D couch (Varian(®) Medical Systems, Palo Alto, CA) were analysed. Among these patients, 169 were treated for brain, 111 for lung, 54 for liver, 26 for pancreas and 16 for prostate tumours. During the set-up, patient anatomy from planning CT was aligned to kV-CBCT, and 6D movements were executed. Information related to pitch and roll were extracted by proper querying of the Microsoft(®) SQL server (Microsoft Corporation, Redmond, WA) ARIA database (Varian Medical Systems). Mean values and standard deviations were calculated for all sites. Kolmogorov-Smirnov (KS) test was performed.

RESULTS

Considering all the data, mean pitch and roll adjustments were -0.10° ± 0.92° and 0.12° ± 0.96°, respectively; mean absolute values for both adjustments were 0.58° ± 0.69° and 0.69° ± 0.72°, respectively. Brain treatments showed the highest mean absolute values for pitch and roll rotations (0.73° ± 0.69° and 0.80° ± 0.78°, respectively); the lowest values of 0.36° ± 0.47° and 0.49° ± 0.58° were found for pancreas. KS test was significant for brain vs liver, pancreas and prostate. Collective corrections (pitch + roll) >0.5°, >1.0° and >2.0° were observed in, respectively, 79.8%, 61.0% and 29.1% for brain and 56.7%, 39.4% and 6.7% for pancreas.

CONCLUSION

Adjustments in all six dimensions, including unconventional pitch and roll rotations, improve the patient set-up in all treatment sites. The greatest improvement was observed for patients with brain tumours.

ADVANCES IN KNOWLEDGE

To our knowledge, this is the first systematic evaluation of the clinical efficacy of a 6D Robotic couch-top in CBCT IGRT over different tumour regions.

Long-term evaluation and cross-checking of two geometric calibrations of kV and MV imaging systems for Linacs

Geometric or mechanical accuracy of kV and MV imaging systems of two Varian TrueBeam linacs have been monitored by two geomertirc calibration systems, Varian IsoCal geometric calibration system and home‐developed gQA system. Results of both systems are cross‐checked and the long‐term geometric stabilities of linacs are evaluated. Two geometric calibration methodologies have been used to assess kV and MV imaging systems and their coincidence periodically on two TrueBeam linacs for about one year. Both systems analyze kV or MV projection images of special designed phantoms to retrieve geometric parameters of the imaging systems. The isocenters — laser isocenter and centers of rotations of kV imager and EPID — are then calculated, based on results of multiple projections from different angles. Long‐term calibration results from both systems are compared for cross‐checking. There are 24 sessions of side‐by‐side calibrations performed by both systems on two TrueBeam linacs. All the disagreements of isocenters between two calibrations systems are less than 1 mm with ± 0.1 mm SD. Most of the large disagreements occurred in vertical direction (AP direction), with an averaged disagreement of 0.45 mm. The average disagreements of isocenters are 0.09 mm in other directions. Additional to long‐term calibration monitoring, for the accuracy test, special tests were performed by misaligning QA phantoms on purpose (5 mm away from setup isocenter in AP, SI, and lateral directions) to test the liability performance of both systems with the known deviations. The errors are within 0.5 mm. Both geometric calibration systems, IsoCal and gQA, are capable of detecting geometric deviations of kV and MV imaging systems of linacs. The long‐term evaluation also shows that the deviations of geometric parameters and the geometric accuracies of both linacs are small and very consistent during the one‐year study period.

PACS number: 87.56.Fc

Use of TrueBeam developer mode for imaging QA

The purpose of this study was to automate regular Imaging QA procedures to become more efficient and accurate. Daily and monthly imaging QA for SRS and SBRT protocols were fully automated on a Varian linac. A three-step paradigm where the data are automatically acquired, processed, and analyzed was defined. XML scripts were written and used in developer mode in a TrueBeam linac to automatically acquire data. MATLAB R013B was used to develop an interface that could allow the data to be processed and analyzed. Hardware was developed that allowed the localization of several phantoms simultaneously on the couch. 14 KV CBCTs from the Emma phantom were obtained using a TrueBeam onboard imager as example of data acquisition and analysis. The images were acquired during two months. Artifacts were artificially introduced in the images during the reconstruction process using iTool reconstructor. Support vector machine algorithms to automatically identify each artifact were written using the Machine Learning MATLAB R2011 Toolbox. A daily imaging QA test could be performed by an experienced medical physicist in 14.3 ± 2.4 min. The same test, if automated using our paradigm, could be performed in 4.2 ± 0.7 min. In the same manner, a monthly imaging QA could be performed by a physicist in 70.7 ± 8.0 min and, if fully automated, in 21.8 ± 0.6 min. Additionally, quantitative data analysis could be automatically performed by Machine Learning Algorithms that could remove the subjectivity of data interpretation in the QA process. For instance, support vector machine algorithms could correctly identify beam hardening, rings and scatter artifacts. Traditional metrics, as well as metrics that describe texture, are needed for the classification. Modern linear accelerators are equipped with advanced 2D and 3D imaging capabilities that are used for patient alignment, substantially improving IGRT treatment accuracy. However, this extra complexity exponentially increases the number of QA tests needed. Using the new paradigm described above, not only the bare minimum — but also best practice — QA programs could be implemented with the same manpower.