Design and standard utilization of safety devices for the radiation treatment management of heavy patients.

PURPOSE: Increasing number of heavy cancer patients has created challenges in diagnostic imaging and radiation oncology. Practical weight limits of the equipment can become an obstacle both for imaging and treatment of these patients. Most magnetic resonance imaging and computed tomography (CT) tables' static load capacities are between 450 and 500 pounds, and linear accelerator tables can support similar weights depending on the type of the table and manufacturer. One recurring issue we encountered was failure of the treatment couch's longitudinal drive belt due to heavy patients' sudden movement. In several cases, snapping of the longitudinal drive belt occurred when the patient's weight was under 300 lbs (below the rated weight limit). Additionally, we observed vertical deflection of the couch when extended/cantilevered with heavy patients. The purpose of this work was to implement immobilization methods and safety devices for radiation treatment management of heavy patients in order to increase patient/provider safety, prevent treatment couch damage, and reduce treatment disruptions. MATERIALS AND METHODS: We created three safety devices for treatment management of heavy patients. Wooden brace and Scissor jack were used to lock the couch longitudinal axis (while the couch longitudinal drive was floated) during the setup of a heavy patient and absorb the mechanical impulse applied to the couch longitudinal drive belt. Wooden brace was built in house and positioned in between the wall and treatment couch to lock the longitudinal axis. Commercially available 10 in × 10 in scissor jack lift with adjustable height 3 ½ in - 13 in was modified to increase effectiveness and safety. An additional stand was created with adjustable height and rolling rubber wheels to support the couch when extended/cantilevered with heavy patients. RESULTS: Using these devices prevented the longitudinal belt from breaking and improved the patient/therapist safety at eight treatment sites within our network. No farther couch belt failures were observed since devices were introduced for clinical use. All three devices can be used and removed without any modifications done to the treatment couch.

Utilizing historical MLC performance data from trajectory logs and service reports to establish a proactive maintenance model for minimizing treatment disruptions.

PURPOSE: This study proposes a proactive maintenance model utilizing historical Multileaf collimator (MLC) performance data to predict potential MLC dysfunctions, promote preemptive maintenance and thereby reduce treatment disruptions. METHODS: MLC failures were assumed to correlate with MLC performance quantitation from trajectory logs. A cohort of data from service reports and trajectory logs was used to establish a model for predicting MLC dysfunctions. Specifically, the service reports logged by our in-house engineers recorded failure status, including service date, service reason and actions taken, while trajectory logs recorded the ordered/actual leaf positions in 20-ms intervals. Leaf performance from trajectory logs was quantified, where an event was defined as detecting a leaf's position deviation ≥a mm. Three a values, 0.05, 0.1, and 0.5 mm, were used as candidates to determine the appropriate threshold for deviation event quantitation. Logged MLC failures from service reports were retrieved and classified into two categories based on the patterns of their deviation events calculated from trajectory logs: (a) failures with continuous deviations: deviation events lasted several days before failure, and (b) failures with a burst of deviations: deviation events only lasted 1 or 2 days and then MLC failed suddenly. The proposed proactive model focused on the failures with continuous deviations since abnormal trends in their deviation events lasted couple of days, allowing preventive maintenance. The model was predefined with three parameters (x, y, z): if a leaf scored ≥x deviation events per day in any y days within up to a z-day window, the leaf was marked as a "potential failure." The distributions of the deviation events as functions of time (days or weeks) and leaves using the found a-value were then associated with logged failures to find model parameters. In a retrospective demonstration, a total of 28 logged failures with continuous deviations and 66 397 trajectory logs from two TBs' 3-yr records were used to determine the model parameters (x, y, z). The established model was then applied to a third TB for validation. RESULTS: Deviation event threshold, a, was determined to be 0.1 mm, and the resulting model parameters were (x = 20, y = 6, z = 10). When validating the third TB's 3-yr record with 12 logged continuous deviation failures, the model predicted 16 failures: seven were confirmed from the records with a hit rate of 58.3%, while nine were not; further investigation of each unconfirmed failure convinced that some could be actual failures, but somehow not recorded. CONCLUSION: The model offers an addition to preemptive maintenance for reducing treatment disruptions.