Magnetron maintenance

Abstract

Disclosed herein is a method of determining a model for predictive maintenance of a magnetron supplying radio frequency energy to a linear accelerator in a radiotherapy system (see Fig. 1). The method comprises collating lifetime data of each of a plurality of replaced magnetrons; analysing the data to determine a set of values indicative of the need for magnetron replacement; and outputting the set of determined values as a model for predictive maintenance of a magnetron. Also disclosed is a method of analysing the power set-up of a magnetron. The method comprises: measuring the magnetron power setup; comparing the measured magnetron power to a predetermined range ; and adjusting the power to within specification if it is outside said range.

Description

FIELD The present disclosure relates to radiotherapy device, and to a method of monitoring a radiotherapy device. BACKGROUND Radiotherapy is an important tool in modern cancer treatment. Radiotherapy devices are large, complex machines, with many moving parts and inter-operating mechanisms. Despite precision engineering and rigorous testing, some component parts of a radiotherapy device may start to degrade over its lifetime. This can sometimes lead to sub-optimal operation and even the occasional safety override. If at any point during treatment a radiotherapy device starts to function outside of its normal operating parameters, a safety override or “interrupt” occurs, whereby the machine stops delivering radiation to ensure patient safety. Existing methods therefore result in a significant amount of machine down time. Further, servicing of the radiotherapy machine is often scheduled at a time which is inconvenient, or inefficient in terms of both field service engineer resources and the resources of the hospital or other machine owner. It has been surmised that predictive maintenance and / or remote diagnostic techniques could be applied to radiotherapy machines. Identifying the link between particular data patterns and the particular fault or degrading component is often non-intuitive even for experienced service engineers. The present disclosure relates generally to developing a predictive maintenance model for a magnetron of a radiotherapy device, and to using the model to determine if a magnetron of a radiotherapy device is nearing the time at which it should be replaced or repaired. To date, no such predictive approach has been possible, and existing methods of servicing and repair of a radiotherapy device require sending a field service engineer to inspect the machine, take measurements from the magnetron, and diagnose and fix the problem. Regarding magnetron failure, there are a number of different failure modes. A review of 250 Magnetron replacements in the US resulted in 14 different failure modes not including ‘Dead on Arrival’. Categorising the failure modes is a manual process done by interpreting the comments and closure statements on the cases. Further, there is no process, training or guidance on determining if a magnetron has failed or can be recovered. It is all down to engineer experience. This means that not all magnetron replacements are essential. Replacements are made often without completing tests to verify the new magnetron performance. It would be useful to provide a consistent framework for determine when a magnetron should be replaced. The present invention seeks to address these and other disadvantages encountered in the prior art by providing a method of determining trends in magnetron failure data for predictive maintenance of a magnetron. SUMMARY Aspects and features of the present invention are described in the accompanying claims. BRIEF DESCRIPTION OF THE DRAWINGS Specific embodiments are described below by way of example only and with reference to the accompanying drawings in which: Figure 1 illustrates a radiotherapy device according to an aspect of the disclosure; Figure 2 illustrates a method of determining a predictive maintenance model for a magnetron; Figure 3 illustrates a method of predicting a fault in a magnetron in a radiotherapy device system according to an aspect of the disclosure; Figure 4 illustrates a block diagram of a radiotherapy device; and Figure 5 illustrates a method of reviewing a magnetron power setup. SPECIFIC DESCRIPTION OF CERTAIN EXAMPLE EMBODIMENTS Magnetrons and methods in this disclosure may be used in combination with the magnetrons and methods described in to GB patent application number GB1903820.7, entitled MAGENTRON FOR A RADIOTHERAPY DEVICE filed on 20 March 2019, and the corresponding priority-claiming PCT application number PCT / EP2020 / 057706, the contents of which is incorporated by reference in its entirety. Radiotherapy devices are an important tool in modern cancer treatment. Radiotherapy devices are large, complex machines, with many moving parts and inter-operating mechanisms. Despite precision engineering and rigorous testing, some component parts of a radiotherapy machines may start to degrade over the lifetime of the machine. This can sometimes lead to sub-optimal operation and even the occasional safety override. If at any point during treatment a radiotherapy device starts to function outside of its normal operating parameters, a safety override or “interrupt” occurs, whereby the machine stops delivering radiation to ensure patient safety. Such an event is inconvenient, as it adds time to the treatment, and in some cases means the treatment session must finish prematurely. Unplanned equipment downtime can disrupt planned treatment schedules, and may be expensive for the owner, be it due to loss of revenue, servicing and repair costs, or both. Magnetrons are complex components of a radiotherapy device, which are difficult and expensive to replace. There are a number of reasons a magnetron is replaced: • Unable to achieve specific dose rate • Age of Magnetron • Configuration of Power Conditions. • Tuner Position Above set limit • Noise found on Magnetron scope traces • Ringing at end of Mag I Pulse. A high-level overview radiotherapy decide according to the disclosure is illustrated in Figure 1. The linear accelerator 110 includes a source of electrons 112, a waveguide 114, and a target 116. Electrons are emitted from the electron gun and accelerated through the waveguide along an acceleration path 118 which is coincident with the centre axis of the waveguide. The electron beam is bent using magnets and strikes the target 116, to produce an x-ray beam 120. The x-ray beam 120 is used to treat a patient. The source of radiofrequency waves 122, such as a magnetron, produces radiofrequency (RF) waves. The source of radiofrequency waves is coupled to the waveguide, and is configured to pulse radiofrequency waves into the waveguide. The source of electrons 112 may be an electron gun. The source of electrons is configured to inject electrons into the waveguide 114. The waveguide 114 comprises a plurality of interconnected acceleration cavities (not shown) forming a channel through which the electron beam passes. The injection of electrons into the waveguide 114 is synchronised with the pumping of the radiofrequency waves into the waveguide 114. The design and operation of the radiofrequency wave source 122, electron source 112 and the waveguide 114 is such that the radiofrequency waves accelerate the electrons to very high energies as they propagate through the waveguide 114 down the acceleration path 118. The waveguide is designed in order that a suitable electric field pattern is produced which accelerates electrons propagating through the waveguide 114. A magnetron comprises a cathode heated by a filament. A magnetic field causes electrons emitted by the cathode to spiral outward through a cavity. As the electrons pass the cavity they induce a resonant, RF field in the cavity through the oscillation of charges around the cavity. The RF field can then be extracted with a short antenna attached to one of the spokes. The device further comprises an oscilloscope 124. The oscilloscope is configured to measure the output of the magnetron. The oscilloscope 124 may be a micro-oscilloscope. The oscilloscope 124 is configured to measure current and anode of the magnetron anode. Predictive maintenance Methods of the present disclosure involve analysing data from magnetrons to determine data patterns indicative of failure of a magnetron. These patterns are used to create a predictive maintenance model for determining whether data from a magnetron indicates whether it should be replaced or repaired. The disclosed methods help to reduce machine downtime and thereby minimise disruption to the machine’s normal operation. The disclosed techniques can also be used to more effectively plan machine downtime for times which are more convenient or cost-effective for the owner of the equipment and / or the patients. Data collected from the magnetron can also be used in the future for trending and to learn how a magnetron behaves during its lifespan. Data can also be used in further research and development of magnetrons for radiotherapy devices. To create a predictive maintenance model requires sufficient data to determine data patterns and trends prior to and at the point of failure. Currently, in radiation therapy there is no data routinely recorded from the magnetron. If the magnetron malfunctions, or another component of the radiotherapy machine malfunctions, an engineer may manually test components of the machine. This may involve testing the parameters of the magnetron through manually, and temporarily, attaching an oscilloscope to the magnetron. This is done during machine “downtime”, i.e. when the machine is not operational. The output parameters and behaviours of the magnetron therefore are only tested or recorded once the radiotherapy machine malfunctions. Unplanned equipment downtime can disrupt planned treatment schedules. The behaviour of a magnetron over its lifetime is not tracked or recorded. Particularly, conditions a magnetron has been exposed to, or the behaviours of the magnetron, towards the end of its life are not tracked or analysed. There is no known way of identifying magnetrons which are approaching the end of their operational life. Further, measurements of magnetron output are not taken during routine operation of a radiotherapy device, but are only taken by an onsite service engineer. This will be either during unplanned downtime or planned downtime. Since under normal operation the output of a magnetron is not measured, a magnetron may be operating within a performance range which, whilst acceptable to meeting the performance requirements, is not optimal. Performing outside optimal conditions may lead to a shortening of the life span of the magnetron, or to less efficient or suboptimal performance of the radiotherapy device. Finally, since measurements of the output of the magnetron can currently only be taken with a service engineer on site, and are not taken during routine operation of the radiotherapy device, there is a lack of data relating to performance of a magnetron in a radiotherapy device over its lifetime. It has to date not been possible to establish patterns relating to the performance of a magnetron. Under the present set up, there is not enough data to determine particular data patterns which may be indicative of a particular fault. To date there has been no predictive maintenance of a magnetron of a radiotherapy device. For the above reasons the data has not been collected to create a model for predictive maintenance of a magnetron. This disclosure relates to a method of creating a model for predictive maintenance of a magnetron. Figure 2 Figure 2 shows a method of determining a model for predictive maintenance of a magnetron. The method is performed by a processor of a computer or at a central server. The model is based on data from a plurality of magnetrons and can be used to determine whether a specific magnetron has an increased likelihood of failure. The method includes collating data 210 from multiple magnetrons, the data relating to the magnetron prior to or at the point of failure of that magnetron. In some examples the data is lifetime data from the magnetron. Collating data is obtaining or retrieving data. In some examples the data is collected through accessing records stored on a system. The records include data relating to a number of measurements from each of a plurality of magnetrons at or prior to failure. In some examples the data is collected through accessing multiple different records stored in multiple different systems. In other examples the data is collected directly from a magnetron. That is, the magnetron includes a component configured to send data relating to the magnetron to a central server via a network. The data collected can include values for the following measurements. The measurements may be comprised in, or derived from, the data: LT & HT Hour History •     Mean X-Ray Dose Rate for all XRAY Energies (Not FFF). •     Min, Mean & Max Tuner Position forXLOW Energy. •      Min, Mean & Max Magnetron Filament Current & Voltage with no energy selected. •     Min, Mean & Max Magnetron Filament Voltage for XLOW Energy. LT Hours - Low Tension Hours is the amount of time the linac has been switched on and in a closed state or higher. HT Hours - High Tension Hours is the number of hours where the beam has been on for. After collecting the data, the method includes analysing the data to determine a set of values indicative of the need for magnetron replacement. In the method of Figure 2 this is done is steps 220 and 230. At step 220 the method includes processing the data to determine average values of each measurement of the plurality of magnetrons prior to or at the time the magnetron is replaced. This can include determining the average lifetime of the parts of the magnetron; the average start value and the average end value of the magnetron tuner the average lifetime of the tuner the average total Mils delivered by the magnetron the average HT hours at replacement the average days since installation the average clinical day From the data it can be determined when the magnetron has been replaced as the tuner position changes, and on some occasions a change in dose rate is observed. At step 230 the method includes analyzing trends in the data to determine a predictive maintenance model. The model can include a plurality of sets of values corresponding to or derived from the average values determined in step 220. A predictive maintenance model in some examples includes threshold values. If data relating to a magnetron falls outside the threshold values, this could be an indication that the magnetron is close to failure and needs replacing. There are difficulties presented with analyzing the data and using the data for predictions, especially with manually analyzing the data. The tuner position is often random, on the same machine the optimal tuner position will be different for each magnetron fitted, when a magnetron is changed the tuner position can increase or decrease from the previous value. There also appears to be no real limit to the tuner position, it can run at very high values previously thought to be end of life for many months or years. Many of the difficulties with manual analytic of data are overcome using the application of Al. In some embodiments Al is used to analyze the data to determine values for a predictive maintenance model for a magnetron. The determined values are output at step 240 as a model for predictive maintenance of a magnetron. The model may be output to a user interface or to be stored in a system. There is also provided computer readable medium configured to perform the method of Figure 2. Figure 3 Figure 3 illustrates a method of predicting a fault in a magnetron in a radiotherapy device system using the predictive model. The method is performed by a processor of a computer or at a central server. To use the model to determine whether replacement of a magnetron is necessary, data from a first magnetron is obtained 310. The data may be retrieved from records stored in a system. Alternatively the data may be received from the magnetron at a central server over a network. The data is then used to determine whether repair or replacement of the magnetron should be scheduled 320. For example, if the data indicates that the magnetron is operating outside of its optimal parameters defined in the model, the process determines that replacement of the magnetron should be scheduled. The model may include threshold values against which data from the first magnetron is compared. The data from the first magnetron includes a plurality of measurements, each of those measurements is compared against a respective value in the model. For example, the measurement of LT & HT Hour History of X-Ray Dose Rate for the first magnetron is compared to a corresponding threshold value for this measurement in the model. The data may be used to derive the measurements of the first magnetron. The determination can be made dependent on the whether the measurement is above or below the corresponding threshold value in the model. The model may also include predetermined ranges. If the first magnetron measurement is in the predetermined range, the determination is made that replacement of the magnetron does not need to be scheduled. If the measurement of the first magnetron falls outside the predetermined range it is determined that replacement of the magnetron should be scheduled. Finally, at step 330 the determination is output. This may be in the form of an automated message being sent to a service engineer. When this information is analysed it can trigger automatic messages for service engineers and managers to perform a service intervention on the affected machine / s. If the magnetron is showing signs of deterioration, the engineer receives an automated message which prompts him to replace the magnetron. This can be scheduled outside clinical hours to minimise clinical downtime. Scheduling activities outside of clinical hours can also reduce travel time, as well as the mean time to repair. The output may be in the form of an automated message. There is also provided computer readable medium configured to perform the method of Figure 3. Advantages Being able to predict a magnetron failure allows the service teams to plan the activity with the customer rather than resulting in unplanned system downtime. The predictive model also validates whether a magnetron replacement is required. This avoids magnetrons being replaced without it being necessary. Finally, it can improve visibility of stock. In some instances, data is received from the magnetrons at a server over a network. Figure 4 illustrates a block diagram of a linear accelerator network which optionally can be used in the above implementations. The network of Figure 4 can be used in the method of the present disclosure when data relating to multiple magnetrons (step 210) and data relating to the first magnetron (step 310) is received from the magnetrons themselves over a network. The connections between components are wired electrical connections. In some embodiments the connections may be wireless. The linear accelerator has a control until 420 which is configured to control the linear accelerator to deliver a treatment plan to a patient. The control until 420 controls the magnetron 430 to output a required amount of rf energy to the waveguide. The control unit 420 also controls the other components of the linear accelerator 440. Controlling the other components 440 can include: controlling the electron gun to feed electrons to the waveguide; controlling the gantry to rotate according to the treatment plan to provide the angle which radiation is delivered to the patient; and 9 controlling a collimator, such as a multi leaf collimator, MLC, to collimate the beam according to the treatment plan. The control unit 420 sends information on the linac network 410. The linac network 410 may send the treatment plan to the control unit 420 ahead of the treatment delivery. The magnetron 440 is controlled by the control unit to 420 to provide rf energy to the waveguide of the linac. The output of the magnetron 430 is measured by oscilloscope 450. The oscilloscope may send the measurements to the linac network. The measurements may be sent through wired or wireless connections to the linac network. The oscilloscope may be a headless oscilloscope which sends the data to an ARM based micro-computer. This micro-computer is connected to the linac network. The control 420 also controls other components 440 of the linac, for example the electron gun, the gantry and / or the multi-leaf collimator. Outputs of each of these components are sent to the linac network 410. The outpours are measured by different measurement devices. The output of the beam itself may also be measured, and the measurement sent to the linac network 440. The measurements of the beam and of the outputs of the components of the linac are sent to the linac network as electrical signals through an electrical connection, or are sent wirelessly. The linac network 410 has a wireless connection to the internet. The linac network can send information to a central server such as Axeda or Thingworx. The data can then be automatically analyzed and stored. The data may be sent - that is transmitted - over a wireless network such as an internet or intranet, WLAN connection or a peer-to-peer connection. Data from the linac network can be sent to a central server storage. The central server, such as a cloud solution, can be accessed remotely, for example from a computer at a central location which is also connected to the internet. That is, the linac network is configured to send data to the central server, and data on the central server can be accessed from a device which is located at a location remote from the linear accelerator. The data which is transmitted to the cloud in the system of Figure 2 includes: • The treatment plan (including the dose of radiation, the shape of the radiation beam, the angles at which the radiation is delivered, the timing of pulses of radiation and any other information relating to the delivery of radiation) • The history of the magnetron - including LT and HT hours for a plurality of measurements • The output of other components of the linear accelerator Magnetron power set up There is also provided a method of analysing magnetron power set-up. Magnetron power should be set up appropriately to ensure optimum performance of the magnetron. When a magnetron is set up, Mag I Pulse is measured on site to determine if the power is set up appropriately. Each magnetron has an optimum range or specification for power set-up. This power setup is controlled by the M.Mag and charge rate, which are known values to be set for each energy. Magnetrons are setup within a specific range of efficiency depending on the value of M.Mag. The present inventors have determined that if a machine runs with abnormal power, more frequent replacements are required. The magnetron power can be analysed using the ratio M.Mag / Chargerate, and also a limit to the highest charge rate per energy. The inventors have determined that machines with two or more energies (XRays or High energy electrons - 15MeV+) running with abnormal power conditional would have double the rate of magnetron replacements compared to machines running within expected ranges. A method of determining the power set-up of a magnetron is described below. At step 410 the magnetron power setup is measured. Magnetron setup can be reviewed in the QlikSense application RCC Monitoring. This can include reviewing the setup of all energies and sorting by the values of: M.Mag Chargerate Low Dose Interlock Cal Block PRF M. Mag is the magnetron magnet current. Changing the M Mag parameter changes the strength of the magnetic field around the magnetron. Chargerate is the charge rate of the HT PSU. The value of M Mag should be “matched” to the value of chargerate. If these parameters are not matched the magnetron life will be shortened. Low dose interlock sets the threshold for dose rate. If the beam fails to achieve this value or degrades below it, the beam will terminate abnormally. Cal block PRF can be used to reduce the number of magnetron pulses. At step 420 the measured magnetron power is compared to the specification / designated ranges. The specification is a predetermined range. If the magnetron power is determined to be outside the designated efficiency ranges or specification, the power is adjusted to within the specification 430, for example to factory settings. This puts the magnetron under less strain meaning it is less likely to fail. If the magnetron power is determined to be within the specification 440, no action is taken. Therefore there is provided a method of reviewing magnetron power set-up to increase the life span of the magnetron. Features of the above aspects can be combined in any suitable manner. It will be understood that the above description is of specific embodiments by way of aspect only and that many modifications and alterations will be within the skilled person’s reach and are intended to be covered by the scope of the appendant claims.

Claims

1. A method of determining a model for predictive maintenance of a magnetron of a radiotherapy device, the method performed by a processor of a computer and / or at a central server and the method comprising:collating lifetime data of each of a plurality of magnetrons;analysing the data to determine a set of values indicative of the need for magnetron replacement; andoutputting the set of determined values as a model for predictive maintenance of a magnetron.

2. The method according to claim 1, wherein the data comprises a plurality of measurements associated with each magnetron.

3. The method according to claim 2, wherein;the magnetron comprises a magnetron tuner, and the magnetron is suitable for use in a radiotherapy device configured to generate X-ray beams;the plurality of measurements comprises Low Tension, LT, & High Tension, HT, hour history, the LT being hours over the lifetime of the magnetron it has been switched on in a closed state or higher, HT being the number of hours the beam has been switched on; the LT & HT hour history comprising at least one of: mean X-ray dose rate for all X-ray energies; min, mean & max magnetron tuner position for XLOW energy; min, mean & max magnetron filament current & voltage with no magnetron output energy selected; and min, mean & max magnetron filament voltage for XLOW energy.

4. The method according to any of claims 2 to 3, wherein analysing the data comprises determining average values of each of the plurality of measurements.

5. The method according to claim 4, wherein the average values comprise at least one of: the average lifetime of parts of the magnetron; the average start value and the average end value of the magnetron tuner; the average lifetime of the tuner; the average total MUs delivered by the magnetron; the average HT hours at replacement of the magnetron; the average days since instillation of the magnetron; and the average clinical day.

6. The method of any preceding claim, wherein analysing the data comprises determining trends in the data.

7. The method of any preceding claim wherein analysing the data comprises using Al to determine trends in the data.

8. The method of any preceding claim wherein collating data comprises retrieving data from records stored in a system .

9. The method of any preceding claim wherein collating data comprises receiving data from the magnetron over a network.

10. The method of any preceding claim further comprising: receiving data relating to a first magnetron, comparing the data from the first magnetron to the predictive maintenancemodel;based on the comparison, determining whether replacement should be scheduled.

11. The method of claim 10, wherein the data relating to the first magnetron is received from the first magnetron over a network.

12. The method of claim 10 or 11, wherein data relating to the first magnetron comprises a plurality of measurements of the first magnetron; andwherein comparing the data from the first magnetron to the predictive maintenance model comprises comparing each measurement of the first magnetron to a respective value in the model.

13. The method of any of claims 10 to 12, wherein comparing the data from the first magnetron to the predictive maintenance model comprises comparing a value derived from or comprised in the data from the first magnetron to a threshold value in the predictive model, andif the value is greater than the threshold value, determining that replacement of the magnetron should be scheduled.

14. The method of any of claims 10 to 13, further comprising outputting the determination.

15. The method of any preceding claim wherein outputting the model for predictive maintenance of a magnetron comprises outputting the model to a user interface or to a computer storage medium.

16. The method of any preceding claim wherein the plurality of magnetrons are each a magnetron of a radiotherapy device wherein the radiotherapy device comprises a linear accelerator.

17. A computer readable medium comprising instructions which, when executed by a processor, cause the processor to perform the method of any preceding claim.

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