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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 0.5mm gantry locus 2mm image reg 2mm couch locus 3% dose delivery 0.5mm kV-MV 1mm laser loc 10mm target respiratory motion 2mm immob movement 2mm contouring variation 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 1. Map your processes (flowchart, tree, etc) 2. Give any foreseeable fault a weighted score • likelihood of Occurrence • Severity of fault • likelihood of being Detected 3. Then add QA processes to address the potential faults, with most effort focused on highest scores 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 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) 17 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 2. Image, register, check offset is right 3. Correct couch, re-image, check residual error 4. Visually inspect the new phantom position 1. 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 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 33 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% 99.5% 96.0% 88.9% Dose Diff 1% 97.9% 95.5% 77.5% More QA Equipment Tomas Kron, Peter MacCallum Cancer Centre 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 45 In vivo Dosimetry 46 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 Little Sigma Big Sigma For a population of patients: • Systematic Error = standard deviation of individual mean errors • Random Error = Root-Mean-Sum of individual random errors 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 54