Testing the portal imager GLAaS algorithm for machine quality assurance

G Nicolini¹ , E Vanetti¹ , A Clivio¹^,3 , A Fogliata¹ , G Boka¹^,4 and L Cozzi¹^,2

¹Oncology Institute of Southern Switzerland, Medical Physics Unit, Bellinzona, Switzerland

²University of Lausanne, Faculty of Medicine, Lausanne, Switzerland

³University of Milan, Medical Physics Specialisation School, Milan, Italy

⁴Latvian Oncology Center of Riga Eastern University Clinical Hospital. Dept. of Dosimetry., Riga, Latvia

author email corresponding author email

Radiation Oncology 2008, 3:14doi:10.1186/1748-717X-3-14


Published:	21 May 2008

Abstract

Background

To report about enhancements introduced in the GLAaS calibration method to convert raw portal imaging images into absolute dose matrices and to report about application of GLAaS to routine radiation tests for linac quality assurance procedures programmes.

Methods

Two characteristic effects limiting the general applicability of portal imaging based dosimetry are the over-flattening of images (eliminating the "horns" and "holes" in the beam profiles induced by the presence of flattening filters) and the excess of backscattered radiation originated by the detector robotic arm supports. These two effects were corrected for in the new version of GLAaS formalism and results are presented to prove the improvements for different beams, detectors and support arms. GLAaS was also tested for independence from dose rate (fundamental to measure dynamic wedges).

With the new corrections, it is possible to use GLAaS to perform standard tasks of linac quality assurance. Data were acquired to analyse open and wedged fields (mechanical and dynamic) in terms of output factors, MU/Gy, wedge factors, profile penumbrae, symmetry and homogeneity. In addition also 2D Gamma Evaluation was applied to measurement to expand the standard QA methods. GLAaS based data were compared against calculations on the treatment planning system (the Varian Eclipse) and against ion chamber measurements as consolidated benchmark. Measurements were performed mostly on 6 MV beams from Varian linacs. Detectors were the PV-as500/IAS2 and the PV-as1000/IAS3 equipped with either the robotic R- or Exact- arms.

Results

Corrections for flattening filter and arm backscattering were successfully tested. Percentage difference between PV-GLAaS measurements and Eclipse calculations relative doses at the 80% of the field size, for square and rectangular fields larger than 5 × 5 cm²showed a maximum range variation of -1.4%, + 1.7% with a mean variation of <0.5%. For output factors, average percentage difference between GLAaS and Eclipse (or ion chamber) data was -0.4 ± 0.7 (-0.2 ± 0.4) respectively on square fields. Minimum, maximum and average percentage difference between GLAaS and Eclipse (or ion chamber) data in the flattened field region were: 0.1 ± 1.0, 0.7 ± 0.8, 0.1 ± 0.4 (1.0 ± 1.4, -0.3 ± 0.2, -0.1 ± 0.2) respectively. Similar minimal deviations were observed for flatness and symmetry.

For Dynamic wedges, percentage difference of MU/Gy between GLAaS and Eclipse (or ion chamber) was: -1.1 ± 1.6 (0.4 ± 0.7). Minimum, maximum and average percentage difference between GLAaS and Eclipse (or ion chamber) data in the flattened field region were: 0.4 ± 1.6, -1.5 ± 1.8, -0.1 ± 0.3 (-2.2 ± 2.3, 2.3 ± 1.2, 0.8 ± 0.3) respectively.

For mechanical wedges differences of transmission factors were <1.6% (Eclipse) and <1.1% (ion chamber) for all wedges. Minimum, maximum and average percentage difference between GLAaS and Eclipse (or ion chamber) data in the flattened field region were: -1.3 ± 0.7, -0.7 ± 0.7, -0.2 ± 0.2 (-0.8 ± 0.8, 0.7 ± 1.1, 0.2 ± 0.3) respectively.

Conclusion

GLAaS includes now efficient methods to correct for missing "horns" and "holes" induced by flattening filter in the beam and to compensate for excessive backscattering from the support arm. These enhancements allowed to use GLAaS based dosimetric measurement to perform standard tasks of Linac quality assurance with reliable and consistent results. This fast method could be applied to routine practice being also fast in usage and because it allows the introduction of new analysis tools in routine QA by means, e.g., of the Gamma Index analysis.