1. 6. Clinical implementation and SBRT quality assurance
Patient Specific QA
Equipment specific QA
In vivo Dosimetry
TG-142 and TG-101 guidelines
Process assessment
Clinical challenges
Jeffrey Barber, Medical Physicist
IAEA RAS6065, Singapore Dec 2012

2. Useful References AAPM TG-101 Report: SBRT
AAPM TG-142 Report: Medical Linac QA
AAPM TG-179 Report: CT-based IGRT QA

4. 0.5mm gantry locus 2mm couch locus 2mm image reg 1mm laser loc
10mm target respiratory motion
2mm couch locus
2mm image reg
2mm immob movement
3% dose delivery
2mm contouring variation
1mm laser loc
0.5mm kV-MV

5. Quality Assurance
Physicists should check individual parameters and combined processes
If you check everything in isolation, how do you know what you are doing at the end
TG-142 and TG-101 are guidelines. Lots of advice on how to do things, how to investigate and how to develop local protocol
The future TG-100 proposes a different approach

6. QA Approach Perks et al (2012) IJROBP 83 p1324
Fault Mode Effects Analysis (FMEA)
Process Engineering concept used to focus QA efforts on most practical problems
Map your processes (flowchart, tree, etc)
Give any foreseeable fault a weighted score
likelihood of Occurrence
Severity of fault
likelihood of being Detected
Then add QA processes to address the potential faults, with most effort focused on highest scores

7. QA Approach

8. QA Approach
FMEA promises to increase the efficiency and effectiveness of the testing required
But FMEA takes a lot of resources and time to set up
Current guidelines are effective, if intensive
Quality Assurance can be categorised as:
Equipment QA
Patient-specific QA

9. Equipment QA

10. Equipment QA
TG-142 Daily

11. Equipment QA
TG-142 Monthly

12. Equipment QA
TG-142 Annual (1)

13. Equipment QA
TG-142 Annual (2)

14. Equipment QA
TG-142 MLC

15. Equipment QA
TG-142 Imaging (1)

16. Equipment QA
TG-142 Imaging (2)

17. Equipment QA
ASTRO

18. Equipment QA
TG-101

19. Equipment QA
TG-101

20. Equipment QA
TG-101

21. Equipment QA
TG-101

22. Equipment QA – kV/MV coincidence
Room Lasers
Imaging Isocentre
Radiation Isocentre

23. Equipment QA – kV/MV coincidence
Room Lasers
Imaging Isocentre
Radiation Isocentre

24. Equipment QA – kV/MV coincidence
Winston-Lutz type tests check centre points

25. Equipment QA – kV/MV coincidence
Sharpe et al, Med. Phys. 33, , 2006

26. Equipment QA – kV/MV coincidence
Elekta: Planar images are uncorrected. Flexmap offset saved in DICOM header. 3D reconstructions include the correction.
Varian: Flex is included in robotic arm so each image is corrected.
If flex needs calibrating, it will be visible in the reconstructed images
Bissonnette

27. Equipment QA – Daily Checks
Daily IGRT QA
Set up phantom with known offset
Image, register, check offset is right
Correct couch, re-image, check residual error
Visually inspect the new phantom position

28. Equipment QA – Image Quality
Rings
Streaks
Capping
Motion

29. Equipment QA – Image Quality
Most important Image Quality parameter is spatial accuracy and scaling

30. Equipment QA – Image Quality
Most important Image Quality parameter is spatial accuracy and scaling

31. Machine QA – MLC Accuracy
Using Picket Fence and Garden Fence beams
Film
EPID
Array Device
Analysis is the hard part
How good is your eye?
How good is your image processing?
Lots of commercial solutions available

32. Machine QA – MLC Accuracy

33. Patient-specific qa

34. Patient Specific QA high doses + small volumes
+ complex beam arrangements
+ moving structures
= need for patient-specific QA
Verify Dose
Verify 3D Distribution

35. Patient Specific QA Verify Dose
Copy plan to phantom, recalculate, deliver to chamber
Chamber measurements ≤ 3% from planned dose
Array devices and film can be calibrated to dose

36. Patient Specific QA Verify Distribution
Array devices (MapCheck, ArcCheck, Matrixx, Octavius, Delta4, etc.)
Film
Gel?
Use Record/Verify “QA Mode” deliver at true gantry angles.
Analyse beams individually and as whole fraction.

37. Patient-Specific QA (Pre-Tx)
Using the Delta4 phantom we get psuedo-3D distribution of points across the plan volume
Two 2D planes of diodes form a cross
Real plan > copy to phantom CT, recalc > measure > analyse
Results are highly reproducible

38. Delta4 Results

39. Delta4 Results Halo distribution
TPS pumping dose in the non-lateral-equilibrium regions
Absolute dose max ~200% patient prescription
Difference of dose absorption between high and low density mediums

40. Delta4 Results
Very similar results when measurements are repeated on same day and different day  reproducible delivery by MLC
Very similar results when measurements are repeated on different linacs  well matched and stable linacs
Where to set tolerance for pass/fail?
Avg γ Pass
3mm DTA
2mm DTA
1mm DTA
Dose Diff 3%
100.0%
99.5%
93.1%
Dose Diff 2%
96.0%
88.9%
Dose Diff 1%
97.9%
95.5%
77.5%

41. More QA Equipment
Tomas Kron, Peter MacCallum Cancer Centre 41

42. Patient-Specific QA (Post-Tx)
Phantom measurements check one delivery, one time.
Linac log files can be used to check actual treatment delivery mechanical parameters
Combine this with IGRT and dose reconstruction/accumulation is possible

43. Patient-Specific QA (Post-Tx)
Elekta does not have dynalogs
But a record of mechanical parameters is sent to Mosaiq after delivery
A report can be generated and compared to the DICOM-RTPlan

44. In vivo Dosimetry TLD OSLD Diodes MOSFETS Radiochromic film squares
“Ex vivo” Dosimetry
Transit Dosimetry via EPID
Per fraction beam fluence measurements
Recommend checking in field and out of field

45. In vivo Dosimetry

46. Process review

47. Process Evaluation

48. Process Evaluation MARGINPTV = 2.5Σ + 0.7σ Σ – st dev of sys errors
σ – st dev of random errors
2.5 and 0.7 come from 90% and 95% confidence intervals for Gaussian distributions, respectively.
This margin has the 95% isodose line cover the CTV in 90% of patients
Systematic errors contribute more than random errors to uncertainty
4DCT and IGRT should remove systematic error and reduce random error

49. Process Evaluation
Van Herk 2012

50. Process Evaluation For a single patient:
Systematic Error = mean offset
Random Error = standard deviation
Chris Fox, Peter MacCallum

51. Process Evaluation For a population of patients:
Systematic Error = standard deviation of individual mean errors
Random Error = Root-Mean-Sum of individual random errors
Big Sigma
Little Sigma
Chris Fox, Peter MacCallum

52. Process Evaluation You can only collect statistics on what you image.
If you want to know how accurate your IGRT is, you need another image after any couch shift

53. Thank you
Tomas Kron
Simon Downes
Sean White