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

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

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

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

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

QA Approach

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

Equipment QA

Equipment QA TG-142 Daily

Equipment QA TG-142 Monthly

Equipment QA TG-142 Annual (1)

Equipment QA TG-142 Annual (2)

Equipment QA TG-142 MLC

Equipment QA TG-142 Imaging (1)

Equipment QA TG-142 Imaging (2)

Equipment QA ASTRO

Equipment QA TG-101

Equipment QA TG-101

Equipment QA TG-101

Equipment QA TG-101

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

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

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

Equipment QA – kV/MV coincidence Sharpe et al, Med. Phys. 33, 136-144, 2006

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

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

Equipment QA – Image Quality Rings Streaks Capping Motion

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

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

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

Machine QA – MLC Accuracy

Patient-specific qa

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

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

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.

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

Delta4 Results

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

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%

More QA Equipment Tomas Kron, Peter MacCallum Cancer Centre 41

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

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

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

In vivo Dosimetry

Process review

Process Evaluation

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

Process Evaluation Van Herk 2012

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

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

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

Thank you Tomas Kron Simon Downes Sean White