RSNA Meeting, Chicago, IL Predictive Maintenance of Medical Accelerators: A Customized Dashboard Interface CM Able, MS, AH Baydush, Ph.D., C Nguyen, BS, J Gersh, Ph.D., A Ndlovu, Ph.D., I Rebo, Ph.D., J Booth. Ph.D., M. Perez, Ph.D., B Sintay, Ph.D., and MT Munley, Ph.D. December 3, 2014 RSNA Meeting, Chicago, IL
Disclosures: This work was supported by a grant from Varian Medical Systems, Inc.
Accelerator Predictive Maintenance: Statistical Process Control (SPC) Machine operational and performance data can be represented by a continuous probability density function – typically a Gaussian distribution. Statistical Process Control (SPC) techniques detect non-random changes in a process or system Process Control Charts are the primary tool of SPC Individual – individual parameter versus time Moving Range – difference between adjacent individual data points versus time Standard SPC methods utilize the mean and standard deviation of the process or system being monitored to detect non-random changes, i.e. potential failures The use of statistical process control techniques provides us the platform to determine when an accelerator subsystem’s operating characteristics have changed. The key characteristic that makes SPC effective is that given a homogeneous set of data, regardless of whether the data has a normal distribution, >98% of the data will fall within 3 standard deviations. When action limits are developed they can provide a guidepost to determine when service intervention may be required.
Automated and performed daily VMAT QA Daily Delivery VMAT delivery of a robust, technically challenging QA treatment (Snooker Cue*) Maximizes gantry and MLC speed and speed changes, both with and against gravity Automated and performed daily 5 sites consisting of 7 Truebeam linacs currently under investigation Trajectory and text log files are decoded and analyzed * VanEsch et al, Med Phys 38(9), 5146-5166 (2011).
525 total parameters analyzed 35 from text log file 490 from trajectory log file 482 of which are MLC/carriage related 40 are raw measured values 485 are software processed values Speed - distance change over time Cross correlation With small, challenging subsection of baseline run Analyze location and peak cross correlation value
PdM Software: Initial Launch
PdM Software: Machine Selection
PdM Software: 9 Major Sub-systems
PdM Software: RF Generation 5 Parameters
PdM Software: Gun Control 5 Parameters
PdM Software: Energy Control 4 Parameters
PdM Software: Beam Uniformity 11 Parameters
PdM Software: DC Voltage Generation 7 Parameters
PdM Software: System Regulation 3 Parameters
PdM Software: Jaws and Gantry 10 Parameters: 6 measured, 4 calculated
PdM Software: MLC Bank (A or B) 4 Parameters per leaf: all calculated
PdM Software: Analysis Pane
Individual and Moving Range Charts Upper – Individual Lower – MR White line – Baseline Mean Red Lines – Upper and lower control limits Orange data – warning condition Red data – Alarm condition
Horizontal Histogram and Parameter Summary Statistics Upper – Horizontal frequency histogram White line – Baseline Grand Mean Red Lines – Upper and lower control limits Middle – Parameter summary statistics Lower – Saved user comments
Hybrid Upper and Lower Control Limits Blue Line – Standard SPC limits (+/-3 SD) using measured data Green Line – Limits using system specifications (MLC +/-2mm/s) Red Line – Hybrid method combining standard SPC method and an empirical factor based on clinical requirements or global variation determined from a large sample of accelerators
PdM Software: Example Detection – Beam Steering Radial Angle Steering Coil Current values deviated to an alarm state Deviation was identified by morning flatness/symmetry checks Change in symmetry was confirmed by scanning water phantom Adjustments were made and values were back at baseline
PdM Software: MLC Motor Failure Prediction? Cross correlation (CC) of baseline MLC position (location versus time during delivery) and its position during each daily delivery This chart shows the CC maximum value is decreasing Decreasing CC max value indicates the MLC is lagging compared to its baseline performance MLC performance is evaluated by speed, CC maximum value and CC maximum value location
Predictive Maintenance: Next Steps We have deployed an alpha version of the TrueBeam PdM software to our data collection partners. We are now correlating our findings with field service reports in an effort to validate the utility and effectiveness of our technique for predictive ability. Thank You!