Systems and methods for optimization of component lifetime in balance with image quality by measuring device wear

Abstract

An imaging system (1) includes a medical imaging device (2) configured to perform an imaging procedure. An electronic processor (18) is programmed to perform an imaging procedure setup assistance method (100) including receiving a minimum image quality for imaging an anatomical region of interest to be imaged by the medical imaging device performing the imaging procedure; determining one or more values for one or more parameters of the imaging procedure that reduce an amount of wear accrued on a component (10) of the medical imaging device while performing the imaging procedure while satisfying the minimum image quality; and outputting a recommendation (32) of the determined one or more values for the one or more parameters of the imaging procedure.

Description

SYSTEMS AND METHODS FOR OPTIMIZATION OF COMPONENT LIFETIME IN BALANCE WITH IMAGE QUALITY BY MEASURING DEVICE WEARFIELD

[0001] The following relates generally to the device and systems monitoring arts, the radiology arts, image quality arts, imaging device component wear arts, and related arts.BACKGROUND

[0002] Medical imaging systems are used in hospitals for diagnostic and interventional purposes. Their use causes such a device, or a part thereof, to wear over time, until it fails. The failed part is then replaced by a new or refurbished one. It would be useful to accurately estimate when the part will fail in the near future, and to obtain insight as to how the part is being used and / or how to prolong the lifetime of the part. This insight would be valuable to the user, as by providing the appropriate feedback, the user can adapt the usage of the device to prolong the lifetime of the part.

[0003] The medical images that are produced by medical imaging systems, such as magnetic resonance (MR), computed tomography (CT), direct X-ray (DXR), and image guided therapy (I GT) systems, can provide important information of an internal portion of the patient. Clinical images should have good image quality, but it is also desirable to have an extended lifetime of the imaging system and the components of the imaging system. These goals can be mutually exclusive. For example, a user might maximize image quality by selecting protocols for the scan - e.g., in CT or DXR with high voltage, high current and small focal spot - that lead to fast aging of the anode in the x-ray tube. Conversely, selecting such parameters maximize component lifetime can adversely impact image quality. In addition, the aging of components of the imaging system also depends on the scans that have been done before and upcoming scans, along with factors such as idle time for cooling down the system. The user normally does not know the best trade-off between desired image quality and long lifetime and so he might often select parameter settings that could lead to stress which would be not necessary for the selected image application. This applies also to the sequence of scans which could be done in an optimized way already during the scheduling.

[0004] Current static protocol optimizations of the imaging components ( especially X-ray tube, Generator etc.) has a significant effect on aging, lifetime, and can also result in abrupt failureand a sharp deterioration, if the scan protocols are not tuned and are not dynamically optimized in line with the current component health condition (as an example based on certain number of scans executed and its associated statistical data, geometric conditions, patient conditions and based on estimation of certain key dynamic characteristics in the form of temperatures, currents / voltages or changes in impedance with various usage paradigms of the system).

[0005] Static protocols do not modulate themselves based on the usage of clinical patterns and clinical conditions at hand, and can stress the components as if they are new and with initial conditions, but in this process, the components are stressed sub-optimally with reference to their life which leads to abrupt failures.

[0006] The following discloses certain improvements to overcome these problems and others.SUMMARY

[0007] In some embodiments disclosed herein, an imaging system includes a medical imaging device configured to perform an imaging procedure. An electronic processor is programmed to perform an imaging procedure setup assistance method including receiving a minimum image quality for imaging an anatomical region of interest to be imaged by the medical imaging device performing the imaging procedure; determining one or more values for one or more parameters of the imaging procedure that reduce an amount of wear accrued on a component of the medical imaging device while performing the imaging procedure while satisfying the minimum image quality; and outputting a recommendation of the determined one or more values for the one or more parameters of the imaging procedure.

[0008] In some embodiments disclosed herein, a non-transitory computer readable medium stores instructions readable and executable by an electronic processor to perform an imaging method including receiving a minimum image quality for imaging an anatomical region of interest to be imaged by a medical imaging device performing an imaging procedure; determining one or more values for one or more parameters of the imaging procedure that reduce an amount of wear accrued on a component of a medical imaging device to be used in performing the imaging procedure while satisfying the minimum image quality; and outputting a recommendation of the determined one or more values for the one or more parameters of the imaging procedure.

[0009] In some embodiments disclosed herein, a non-transitory computer readable medium stores instructions readable and executable by an electronic processor to perform an imaging method including receiving historical data related to wear rates of a component of medical imaging devices performing imaging procedures; grouping imaging procedures having similar wear rates; monitoring an operational history of medical imaging devices used in the grouped imaging procedures to determine a total time Ti kto date by each medical imaging device i performing each imaging procedure type ; determining a wear rate / ?fe.for each procedure type ; outputting an indication of the determined wear rate f>k.

[0010] One advantage resides in improving medical image quality while reducing wear of components of a medical imaging device that acquires the images.

[0011] Another advantage resides in dynamically optimizing an imaging protocol to reduce imaging component wear while producing quality images.

[0012] Another advantage resides in estimating a wear of a component of a medical device by comparing records of usage of the component with records of already-failed components.

[0013] Another advantage resides in adapting usage of a component of a medical device to delay failure of the component.

[0014] A given embodiment may provide none, one, two, more, or all of the foregoing advantages, and / or may provide other advantages as will become apparent to one of ordinary skill in the art upon reading and understanding the present disclosure.BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The disclosure may take form in various components and arrangements of components, and in various steps and arrangements of steps. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the disclosure.

[0016] FIGURE 1 diagrammatically illustrates an imaging apparatus in accordance with the present disclosure.

[0017] FIGURES 2, 3, and 4 diagrammatically illustrate imaging methods using the apparatus of FIGURE 1.DETAILED DESCRIPTION

[0018] The following discloses approaches for dynamically recommending imaging procedure settings that balance image quality versus wear. To do so, a minimum image quality inthe anatomical region of interest is provided. This could be manually entered by the imaging technologist (or specified in the radiology examination order form or in upcoming examination notes entered by a reviewing radiologist) or could be determined automatically based on information about the radiology examination. As an example of the latter, if the reason for exam is of a type that benefits from high resolution, such as detecting potentially small carcinogenic nodules, then the minimum image quality could be set to a higher value than would be the case if the reason for exam is to assess a coarser feature such as fluid in lungs. The minimum image quality could be quantified in various ways, such as in terms of signal to noise ratio (SNR) or contrast-to-noise ratio (CNR) normalized by dose.

[0019] Next, parameters of the imaging procedure that affect the amount of wear on the X-ray tube (or, more generally, the amount of wear on a component whose wear is to be controlled) are determined which minimize the amount of wear while satisfying the minimum image quality. In the case of an X-ray tube, key factors that impact the X-ray tube wear typically include focal spot temperature, focal track temperature, and bulk anode temperature. However, these temperatures are not directly measurable (at least in many X-ray tubes). To determine these temperatures, a digital simulation of the X-ray tube operation is performed based on available information such as measured X-ray tube housing temperature and X-ray tube operating parameters such as the tube bias in kV, the filament current in mA, and the pulse width (PW). The model is developed based on historical data, thermodynamic simulations, and so forth. By applying the model for the known operating parameters (e.g., kV, mA, and PW) and available information, the relevant anode temperatures are determined for a given imaging procedure / settings. Different imaging procedures and settings therefore can thus be modeled to determine a recommended imaging procedure with recommended settings that minimizes X-ray tube wear while providing the desired minimum image quality. These settings may be recommended to the operator, and optionally may be automatically set up as the default procedure / settings .

[0020] In some embodiments, a slider or other user dialog can be provided via which the user manually adjusts the balance between image quality and wear. By moving the slider this balance is adjusted, and the system recomputes the optimal imaging procedure and imaging parameters after each such adjustment.

[0021] In other disclosed aspects, component wear is estimated based on historical data. To facilitate implementation of this approach, the available imaging procedures are grouped into imaging procedure types that are expected to produce similar wear rates. The operational history of a set of imaging devices is monitored to keep track of the total timek^ to date by each imaging device i performing each imaging procedure type k. The wear rate produced by the procedure type k is denoted f>k. Then the total wear is given by f>k• Tksummed over all procedure types k. As the Tkvalues are known as well as the total wear, this linear equation can be solved to determine the wear rates (3k.

[0022] In some embodiments, a table of the available procedure types k can be presented along with the corresponding parameter values and aggregated timefor the current imaging device i and the resulting wear / 3k- Tk. The operator can then decide which procedure to use based on the tabulated amount of wear and the corresponding parameter value (whose effect on image quality the operator is assumed to understand). Optionally, this information could be automatically analyzed in conjunction with a procedure equivalence table to provide the operator with a suggested substitute procedure that may provide reduced wear by employing a different value of the parameter setting.

[0023] With reference to FIGURE 1, an illustrative imaging system 1 is shown. The imaging system 1 can include a medical imaging device 2 configured to perform an imaging procedure. The medical imaging device 2 can comprise, for example, a computed tomography (CT) imaging device (as shown), or a C-arm imaging device such as are sometimes used for cardiac imaging, or an image guided therapy (IGT) system employing X-ray imaging, a fluoroscope, a digital radiography (DR) imaging device, or other an imaging device that utilizes X-ray imaging (hereinafter referred to as an “X-ray device” or variants thereof).

[0024] As shown in FIGURE 1, the medical imaging device 2 can include an X-ray tube 10 shown by partial removal of the housing of the CT scanner 2 in FIGURE 1, and diagrammatically shown as INSET A in the upper left of FIGURE 1 as having a cathode 12 and an anode 14. The cathode 12 includes a filament 15 that when heated by a filament current If emits electrons which can be attracted to the anode 14 by a tube voltage Vt that is generated between the cathode 12 and the anode 14, and the anode 14 (in some examples, a rotating anode that rotates about a shaft 13 to distribute heat) comprises a target area on which the electrons e“ impinge, thereby generating X-rays that form an X-ray beam used during image acquisition. The flow ofelectrons e“ forms a tube current It of the X-ray tube 10. The intensity of the X-ray beam produced by the X-ray tube 10 depends, often in a highly nonlinear fashion, on various operational parameters such as the mentioned tube voltage Vt, tube current, and filament current If. The diagrammatically shown X-ray tube 10 is a simplified representation - modern commercial X-ray tubes used in X-ray imaging devices often include additional components such as a grid whose geometry and electrical bias can be used to control the shape, focus, intensity, or other characteristics of the X-ray beam, and such components may introduce additional X-ray tube performance variables such as the grid voltage. The wear of the X-ray tube 10 depends on the operating temperatures of the X-ray tube at key locations, such as a temperature of the focal spot A of the electron beam on the rotating anode 14, focal track temperature (where the focal track B is the path the focal spot A traces on the rotating anode 14 as it rotates, and is diagrammatically indicated by a dashed line in INSET A), and bulk anode temperature of the rotating anode 14. These temperatures for a given scan depend on various operating parameters such as tube voltage Vt, tube current, and filament current If.

[0025] An X-ray detector 16 is configured to detect the X-ray radiation. As shown in FIGURE 1, the detector 16 typically comprises a detector array. The detector 16 is also in electronic communication with an electronic processing device 18, such as a workstation computer, or more generally a computer. Images produced by the medical imaging device 2 via the X-ray radiation generated by the cathode 12 and the anode 14 are processed by the electronic processing device 18. The illustrative medical imaging device 2 employs tomographic imaging in which the X-ray tube 10 and detector 16 rotate together around an imaging subject to acquire a three- dimensional (3D) image of the subject. In other types of X-ray imaging devices these components may be fixed in position rather than revolving around the imaging subject, and hence provide a two-dimensional (2D) image. In a C-arm configuration, the X-ray tube 10 and the detector 16 can be moved to different vantage points (called “views”) around the patient to (for example) provide clinically significant views of the heart, or in an IGT to provide a chosen view of an interventional procedure. In some embodiments, the electronic processing device 18 can also serve as a device controller of medical imaging device 2.

[0026] The electronic processing device 18 may also include a server computer or a plurality of server computers, e.g., interconnected to form a server cluster, cloud computing resource, or so forth, to perform more complex computational tasks. For example, in a commonconfiguration a local electronic processing device serves as a controller for controlling the imaging device 1 to perform image acquisition, and also to record machine log data; while a server is connected via a hospital network and / or the Internet to occasionally receive updates of the machine log data. The server performs analyses on the uploaded machine log data such as applying a predictive failure model to predict when the X-ray tube 10 will fail. Additionally, in embodiments disclosed herein, the server performs predictive analysis to determine when the operator should be alerted to cease performing manual calibration of the X-ray tube 10. The workstation 18 includes typical components, such as an electronic processor 20 (e.g., a microprocessor), at least one user input device (e.g., a mouse, a keyboard, a trackball, and / or the like) 22, and a display device 24 (e.g., an LCD display, plasma display, cathode ray tube display, and / or so forth). In some embodiments, the display device 24 can be a separate component from the workstation 18, or may include two or more display devices. In some embodiments, when the electronic processing device 18 can also serve as a device controller of the medical imaging device 2, the display device 24 comprises a controller display configured to present a representation of the imaging procedure and enabling editing of the imaging procedure.

[0027] The electronic processor 20 is operatively connected with one or more non- transitory storage media 26. The non-transitory storage media 26 may, by way of non-limiting illustrative example, include one or more of a magnetic disk, RAID, or other magnetic storage medium; a solid-state drive, flash drive, electronically erasable read-only memory (EEROM) or other electronic memory; an optical disk or other optical storage; various combinations thereof; or so forth; and may be for example a network storage, an internal hard drive of the workstation 18, various combinations thereof, or so forth. It is to be understood that any reference to a non- transitory medium or media 26 herein is to be broadly construed as encompassing a single medium or multiple media of the same or different types. Likewise, the electronic processor 20 may be embodied as a single electronic processor or as two or more electronic processors. The non- transitory storage media 26 stores instructions executable by the at least one electronic processor 20. The instructions include instructions to generate a visualization of a graphical user interface (GUI) 28 for display on the display device 24.

[0028] The apparatus 10 is configured as described above to perform a method or process 100 of monitoring a component of a medical device. Although described herein as the medical device being the medical imaging device 2 and the component being the X-ray tube 10, the method100 can apply for any suitable component of any suitable medical device. The non-transitory storage medium 26 stores instructions which are readable and executable by the at least one electronic processor 20 to perform disclosed operations including performing the monitoring method or process 100. In some examples, the methods 100 may be performed at least in part by cloud processing.

[0029] With reference to FIGURE 2, an illustrative embodiment of an instance of the monitoring method 100 is diagrammatically shown as a flowchart. At an operation 102, a minimum image quality for imaging an anatomical region of interest to be imaged by the medical imaging device 2 is received at the electronic processing device 18. In some embodiments, an input indicative of the minimum image quality in the anatomical region of interest is received via a user input provided by a clinician using the at least one user input device 22 (e.g., mouse clicks, keystrokes, fingers taps or swipes, and so forth). In other embodiments, the minimum image quality in the anatomical region of interest is determined electronic processing device 18 by the from information related to the imaging procedure. In one example, the minimum image quality comprises a signal-to-noise ratio (SNR). In another example, the minimum image quality comprises a contrast-to-noise ratio (CNR).

[0030] At an operation 104, one or more values for one or more parameters of the imaging procedure are determined that reduce an amount of wear accrued on a component (e.g., the X-ray tube 10) of the medical imaging device 2 while satisfying the minimum image quality. For example, the determined one or more parameters of the imaging procedure comprises one or more of a focal spot temperature, a focal track temperature, and a bulk anode temperature of the X-ray tube 10.

[0031] In some embodiments, a model 30 (e.g., an artificial neural network (ANN)) stored in the non-transitory storage medium 26 is configured to generate a digital simulation of an operation of the X-ray tube from information related to the imaging procedure to determine the one or more parameters of the imaging procedure. This information can include, for example, one or more of an X-ray tube housing temperature, X-ray tube operating parameters, an X-ray tube bias, a filament current, and a pulse width. In some embodiments, the simulation performed by the ANN 30 can also show a comparison of the imaging system 1 performance with a certain part (e.g., the tube 10) versus a new part. In some cases, third parties may use a refurbished tube 10that needs to operate at a higher energy than a new tube 10. Thus, image quality is achieved by system adjustments and at the cost of both dosage and wear on the component.

[0032]

[0033] At an operation 106, a recommendation 32 of the determined one or more values for the one or more parameters of the imaging procedure are output, for example, on the display device 24. The recommendation 32 can be displayed by populating parameter value fields of the representation of the imaging procedure with the determined one or more values. In some embodiments, a user dialog 34 (e.g., a slider bar) indicative of a balance between the minimum image quality and the one or more parameters of the imaging procedure that affect the amount of wear on the X-ray tube 10 is displayed on the GUI 28. A user input indicative of an adjustment of the slider bar 34 (e.g., (e.g., mouse clicks, keystrokes, fingers taps or swipes, and so forth) is received to adjust the recommended one or more settings. The imaging procedure can be adjusted with the one or more adjusted settings.

[0034] In another embodiment, at an operation 108 the medical imaging device 2 is controlled to perform the imaging procedure with the recommended one or more settings.

[0035] In a particular example, the method 100 can be performed by determining a wear rate for different procedures of the X-ray tube 10. With reference now to FIGURE 3, another illustrative embodiment of an instance of the monitoring method 200 is diagrammatically shown as a flowchart. At an operation 202, historical data related to wear rates of X-ray tubes 10 of multiple imaging devices 2 is received at the electronic processing device 18. At an operation 204, imaging procedures having similar wear rates are grouped together.

[0036] At an operation 206, an operational history of the medical imaging devices 2 is monitored to determine a total time Ti kto date by each medical imaging device i performing each imaging procedure type k. To do so, a table 34 (stored in the non-transitory storage medium 26) of the available procedure types k along with the corresponding parameter values and aggregated time Ti kfor the current medical imaging device i and the resulting wear / 3k- Tk. A selection of one of the available procedure types k is received via a user input provided via the at least one user input device 22. The table 34 can be analyzed with a procedure equivalence table 36 to generate a suggested substitute procedure that may provide reduced wear by employing a different value of the parameter setting.

[0037] At an operation 208, a wear rate / ? / c. is determined for each procedure type k by, for example, determining a total wear (3k■ Tksummed over all procedure types k. At an operation 210, an indication of the determined wear rate (3kis output, for example on the display device 24.EXAMPLE

[0038] The following describes the apparatus 10 and the method 100 in more detail. With the continuous monitoring of the system, it is possible to identify risk situations of high stress for the components. The additional information of the patient schedule and the planned imaging procedures also allow a prediction of the following imaging procedure impact.

[0039] With reference to FIGURE 4, another nonlimiting illustrative embodiment of the method 100 (denoted as method 300) is shown. In an operation 302, real time data collection of temperature information is performed. Referring back to FIGURE 1, these temperatures may not include some key temperatures for wear monitoring, such as temperatures of the focal spot A and focal track B, due to there typically not being a suitable thermocouple, thermoresistor, or other temperature sensor at these locations. Hence, in an operation 304, the model 30 is utilized to estimate these temperatures from the available data collected at operation 302, such as the bulk anode temperature. In an operation 306, the temperatures during upcoming scans of a patient schedule 158 are estimated. This may optionally also make use of the model 30. The imaging system parameters can be tuned in an operation 310 in a way that the balance between desired image quality and lifetime targets 312, taking into account the estimated remaining lifetime (e.g., determined based on the wear estimated in operation 304). The image quality and decreased aging / increased lifetime influence is optimized in operation 310 depending on the selected trade-off preferences 312.

[0040] Note that the targets inputs 312 correspond to the minimum image quality input 102 of the embodiment of FIGURE 2, but may include additional targets such as the lifetime target. It will be similarly appreciated that the operations 304, 306, and 310 present a more detailed example of the parameters determination operation 104 of the embodiment of FIGURE 2. These customized preferences 312 could, in some nonlimiting examples, be selected based on the payment model but also the service contract. A more detailed monitoring and customized model based simulation and prediction could be selected by the customer as an extra service via “software as a service”. The input for performance and aging prediction is based on the monitoring data 302and patient scheduling data 158 including exact timing for scans, cooling times as well as component history data, actual environmental conditions (room temperature, ...) and the risk database that has information about critical combinations leading to failures. In some cases, the algorithm takes care of the minimum image quality that has been guaranteed and would be necessary for the patient diagnostic.

[0041] For the image processing like CT reconstruction the selected system parameter setting(s) 314 is reported to the reconstruction unit and taken into account to have optimized image quality. This is also valid for some image processing steps in DXR, IGT and mobile systems. The settings may be displayed, and the user may optionally adjust the IQ / lifetime balance using a slider, e.g., as previously described for operation 106 of the embodiment of FIGURE 2. The scan 316 is then performed using the parameter setting and timing 314, e.g., corresponding to the operation 108 of the embodiment of FIGURE 2.

[0042] Parameters that could be included in the trade-off optimization 310 are e.g. kV, duty cycle, peak mA, pulse width, PPS and FPS, acquisition time, focal spot size including continuous size changes between a typical small and a typical large focal spot (claim) as well as small focal spot position changes on the anode (like a fine-tuned DFS (dynamic / dual focal spot) or QFS (quad focal spot - 4 positions) mode with slight offset but not impacting the focus alignment and ASG (antiscatter-grid) shadowing and taking beam geometry formation into account). It can also consider various critical parameters related to the patient (motion associated with clinical task and scatter based on volume of imaging) and optimize the geometry and acquisition parameters in synchronization to get optimal IQ without stressing the IC components.

[0043] The optimization 310 can also make use of the minimum requested image quality (IQ) component of the targets 312 (considering the patient dose aspects) for a defined application and select between different trade-off parameters which include the usage pattern / loading factors that have been used and lead to a certain temperature distribution on the anode / inside the tube / at the tube housing / inside the system / at the cooling unit / generator. There is a difference between few high peak power pulses at small focal spot and long x-ray acquisition with large focal spot in the heat load and the heat distribution including the time delay for cooling. By taking all parameters into account it is possible to go closer to the limits getting better image quality without risking the defect of the tube and / or deliberately relaxing the thermal load on the system but stillhaving a view dependent defined minimum image quality (defined on the views for the region of interest) and / or the customized trade-off settings.

[0044] The temperature can be an important parameter for estimating wear of the X-ray tube 10 as there are different sources of heating and cooling in a system. The X-ray tube 10 with the rotating anode 14 and the cooling system but also the fans in the system (features not shown in INSET A of FIGURE 1) and the air-condition system in the room. Not always is the environmental temperature setting as it should be and also the history of the system operation has big influence on the actual temperature of components and system. All these parameters can be simulated with the models 30 and a real-time validation update with temperature sensors at locations that are accessible help to get a reliable and more or less real-time temperature profile that can be used to calculate the risk of mechanical stress and damage for upcoming scans.

[0045] Exact modelling of the physical parameters using digital twins (e.g., model 30) helps to get a good understanding of the most stressful combination of operation parameters. At the same time the image quality prediction depending on the parameters is also estimated and could even be monitored in real time via signal to noise analysis from the detector, beam position and shape stability monitoring.

[0046] The image protocol defines the image quality requirements. E.g., in the case of CT scans this depends on the actual scanning position during a scan and could be dynamically adapted (depending on view and field of interest). In the case of DXR it depends on the patient properties but also on the region of interest with the exact x-ray absorption condition.

[0047] Data from the patient that has been acquired prior to the actual scan (or comparable patient data) can even further improve the prediction of the required and necessary imaging parameter. This especially is valuable as e.g., comparable temperature changes due to the operation that has been used for comparable patient situations are available and can be used for the prediction of temperatures in the actual situation when also considering the expected time of operation.

[0048] The usage of history-based analysis data for the real-time prediction in a simulation model is a key functionality for situation adaptive parameter optimization.

[0049] For implementation of a real-time algorithm a model, e.g., for temperature estimation, has to run which includes the estimation of temperatures using the data of the scheduled patients / scans to predict the performance over time and identify the temperature induced risks.Then an optimizer can run with variation of the scan parameters and timing of the scheduled patients to identify the best operation parameters depending on the cost function. The cost function is the trade-off between image quality and the purchased / expected / guaranteed component lifetime.

[0050] To perform the optimization procedure, real time data from the imaging system is collected from several sensors. With the actual data and data from the scans before the actual temperature profile is simulated and predicted for all critical components / locations, the information from the scheduling systems allows for load profile simulation of the next upcoming scans. The information of the upcoming scans and the correlated temperature prediction based on the actual status allows for a risk analysis for components and impact for aging / failures. A tradeoff simulation procedure is performed using boundary conditions from the service contract / payment model (minimum guaranteed lifetime, minimum image quality, minimum number of patient scans, and so forth) can be done and the possible parameter- space can be simulated. The best matching parameter will be selected and used for the upcoming scan.

[0051] For the method 200, a set of failed parts of a certain type, say the X-ray tube 10, is used for which there are detailed usage records in terms of procedures, their duration, the parameter settings used, etc. These devices are grouped in a relatively small set of clusters, based on procedure similarity. Assuming that wear increases linearly in the duration of a procedure, for each procedure cluster or type, a wear factor is estimated, by which the accumulated wear of a part can be estimated based on its usage profile. This can next be used to estimate its remaining lifetime. By comparing a usage profile with other usage profiles, possibilities are created to prolong the part’s lifetime.

[0052] To do so, a relatively large set of n failed parts is used, numbered 1, 2, .... n, that are fitted in devices 1, 2, ... , n, respectively, for which there are records of procedures executed on them. Each record consists of an array of m pairs (pj7, tjj) , with j = 1, 2, ... , m, summarizing all the m procedures executed on device i during its entire lifetime, where Pij denotes the parameter setting used during procedure j and denotes the time spent in procedure j. This time could be an exposure time to make a single image or a sequence of images, i.e., a video. A parameter setting can be thought of as having the form of a k -dimensional vector of values, where each value specifies a device setting, e.g., a voltage, a current, the choice of a filament, patient characteristics, et cetera.

[0053] The parameter space covered by all vectors pij can be subdivided into a relatively small group of p procedure types, based on parameter setting similarity, for example using Euclidian distance and one of a number of well-known clustering techniques. Hereby, clusters that are too small in terms of the number of procedures it contains, may be discarded, and considered as outliers. Let the identified procedure types be denoted by Pk, with k = 1, 2, ... , p. With ptj G P, where P is one of the procedure types, then procedure ptj is of type P.

[0054] The individualvalues are aggregated by summing them per procedure type. So, for procedure type Pkand device i, then the total time Tikthat device i has spent in procedure type k as Equation (1):Tik—1j,pijepkip (1)The column vector Tt=Ti2, .... Tip) is called the usage profile of device i.

[0055] The following set of equations can be solved in (3k, with k = 1, 2, ... , p, where (3kquantifies the relative speed at which a device accumulates wear while in procedure type Pk. The equations are, for i = 1, 2, ... , n, as shown in Equation 2whereby 6; is an error term. Hence, an equation exists for each of the n failed parts. The summation involving the terms f>k• Tikcan be seen as the inner product between a vector of dimension p, called the model, and the usage profile of device i. In short, this can be written as

[0056] The process described above is summarized in the diagram in Equation 3:

[0057] Equation 3 shows data flow for a device i, starting from timing of individual procedures, via aggregated timing per procedure type to the weighted aggregation of aggregated timing over procedure types. Assuming that p « n, the set of equations (1) can be solved using a technique known as multiple linear regression.

[0058] In Equation 2, the dependent variable on the left-hand side of the equation is a constant, i.e., 1. In addition, there is no intercept on the right-hand side, usually denoted by / ?0. If there would be an intercept, the model would yield as solution / ?0= 1 and all other f> -values would be zero, i.e., a hardly useful model.

[0059] The constant value of 1 denotes the normalized, total lifetime. The reason for this is the following. Let’s assume that the obtained model ft is a reasonably good model, i.e., the values are relatively small. Suppose that a still working device d has a usage profile Tdand suppose that this is exactly one third of the usage profile of some failed device i G {1, 2, Then it would be reasonable to assume that this device d has reached one third of its lifetime. And, indeed, the inner product alluded to above would yield a value of approximately one third.

[0060] Let the current absolute age of device d be denoted by adand its total, absolute lifetime be denoted by ld. Then Equation 4 is generated:

[0061] Equation 4 can be rewritten into Equation 5:

[0062] So, its absolute remaining lifetime rd= ld— adcan be written as Equation 6:

[0063] Table 1 illustrates the usage of a device i in three columns: the model, consisting of p = 5 dimensions, a usage profile and a contribution per procedure type. At the bottom of the column contributions, the sum of the contributions is given, representing the current, relative age of the device. A clear view is shown of how the device has been used thus far per procedure type. It allows the user to easily understand how to improve the remaining lifetime of the devices.

[0064] For example, if, on a device i, instead of a procedure type Pk, a procedure type Pk' can be executed with the same time and / 3k' < ftk, then executing Pk' instead of Pkwould be less costly in terms of wear. If the timing differs, i.e., there are two different times t and t', for Pkand Pk', respectively, the choice for Pkiwould be preferable if / 3k' • t' < (3k• t. These times t and t' are related in the sense that, if procedure type Pkwould take a time t, then the replacing procedure type Pk' would take a time t'.Table 1. Example dashboard illustrating the usage of a device.

[0065] If a table is available that lists the replaceability among procedure types, together with time information as just described, it is relatively straightforward to automate this process and provide recommendations of this kind to the user automatically. It is even possible to recommend a list of, say, r recommendations in order of decreasing effectiveness.

[0066] By showing to the user the model as well as its current usage profile for a device, they can obtain insight into which procedure types are ‘costly’ in terms of wear and which are not, as well as obtain insight into their own usage profile, i.e., how much a procedure type is used. By suggesting a user profile of one of the failed devices that is close to the user’s usage profile, considerable savings may be attainable by changing their profile accordingly. For example, if, instead of procedure type P15procedure type P3can be used instead, whereas the timing is identical, then the contribution of P decreases to 0.00 and that of P3increases by 0.02 to 0.07, resulting in a net improvement of 0.03, which is more than 10% of the current, relative age.

[0067] If, however, P3takes three times as much time as P15the contribution of Pj would still decrease to 0, but that of P3would increase by 3 - 0.02 = 0.06 to 0.11, resulting in a net loss of 0.01. this case, P3had better be replaced by P15resulting in a net improvement of approx. 0.0083.

[0068] The disclosure has been described with reference to the preferred embodiments. Modifications and alterations may occur to others upon reading and understanding the preceding detailed description. It is intended that the exemplary embodiment be construed as including all such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.

Claims

CLAIMS:

1. An imaging system (1), comprising: a medical imaging device (2) configured to perform an imaging procedure; and an electronic processor (18) programmed to perform an imaging procedure setup assistance method (100), the imaging procedure setup assistance method including: receiving a minimum image quality for imaging an anatomical region of interest to be imaged by the medical imaging device performing the imaging procedure; determining one or more values for one or more parameters of the imaging procedure that reduce an amount of wear accrued on a component (10) of the medical imaging device while performing the imaging procedure while satisfying the minimum image quality; and outputting a recommendation (32) of the determined one or more values for the one or more parameters of the imaging procedure.

2. The imaging system (1) of claim 1, wherein: the medical imaging device (10) includes a controller display (24) configured to present a representation of the imaging procedure and enabling editing of the imaging procedure; and the outputting of the recommendation (32) comprises populating parameter value fields of the representation of the imaging procedure with the determined one or more values.

3. The imaging system (1) of claim 1, wherein the medical imaging device (2) includes a medical imaging device controller (18) comprising the electronic processor.

4. The imaging system (1) of any one of claims 1-3, wherein the medical imaging device (2) comprises an X-ray imaging device, the component (10) comprises an X-ray tube, and the determined one or more parameters of the imaging procedure comprises one or more of a focal spot temperature, a focal track temperature, and a bulk anode temperature.

5. The imaging system (1) of claim 4, wherein the determining includes: generating, using a model (30), a digital simulation of an operation of the X-ray tube from information related to the imaging procedure.

6. The imaging system (1) of claim 5, wherein the information related to the imaging procedure includes one or more of X-ray tube housing temperature, X-ray tube operating parameters, an X-ray tube bias, a filament current, and a pulse width.

7. A non-transitory computer readable medium (26) storing instructions readable and executable by an electronic processor (20) to perform an imaging method (100), the imaging method comprising: receiving a minimum image quality for imaging an anatomical region of interest to be imaged by a medical imaging device performing an imaging procedure; determining one or more values for one or more parameters of the imaging procedure that reduce an amount of wear accrued on a component (10) of a medical imaging device (2) to be used in performing the imaging procedure while satisfying the minimum image quality; and outputting a recommendation (32) of the determined one or more values for the one or more parameters of the imaging procedure.

8. The non-transitory computer readable medium (26) of claim 7, wherein receiving a minimum image quality in an anatomical region of interest to be imaged in an imaging procedure comprises: receiving, via a user input provided by a clinician, an input indicative of the minimum image quality in the anatomical region of interest.

9. The non-transitory computer readable medium (16) of claim 7, wherein receiving a minimum image quality in an anatomical region of interest to be imaged in an imaging procedure comprises: determining the minimum image quality in the anatomical region of interest frominformation related to the imaging procedure.

10. The non-transitory computer readable medium (26) of any one of claims 7-9, wherein the minimum image quality comprises a signal-to-noise ratio (SNR).

11. The non-transitory computer readable medium (16) of any one of claims 7-9, wherein the minimum image quality comprises a contrast-to-noise ratio (CNR).

12. The non-transitory computer readable medium (26) of any one of claims 7-11, wherein the medical imaging device (2) comprises an X-ray imaging device, the component (10) comprises an X-ray tube, and the determined one or more parameters of the imaging procedure comprises one or more of a focal spot temperature, a focal track temperature, and a bulk anode temperature.

13. The non-transitory computer readable medium (26) of claim 12, wherein the determining includes: generating, using a model (30), a digital simulation of an operation of the X-ray tube from information related to the imaging procedure.

14. The non-transitory computer readable medium (26) of claim 13, wherein the information related to the imaging procedure includes one or more of X-ray tube housing temperature, X-ray tube operating parameters, an X-ray tube bias, a filament current, and a pulse width.

15. The non-transitory computer readable medium (26) of either one of claims 13 and 14, wherein the method (100) further includes: determining, using the model (30), a digital simulation of an operation of a refurbished X-ray tube and a new X-ray tube; and outputting the recommendation (32) of whether to use the refurbished X-ray tube or the new X-ray tube.

16. The non-transitory computer readable medium (26) of any one of claims 7-15, wherein the method (100) further includes: displaying, on a display device (24) of an electronic processing device (18) operatively connected with the medical imaging device (2), a user dialog (34) indicative of a balance between the minimum image quality and the one or more parameters of the imaging procedure that affect the amount of wear on the component (10); receiving a user input indicative of an adjustment of the user dialog to adjust the recommended one or more settings; and adjusting the imaging procedure with the one or more adjusted settings.

17. A non-transitory computer readable medium (26) storing instructions readable and executable by an electronic processor (20) to perform an imaging method (100), the imaging method comprising: receiving historical data related to wear rates of a component (10) of medical imaging devices (2) performing imaging procedures; grouping imaging procedures having similar wear rates; monitoring an operational history of medical imaging devices used in the grouped imaging procedures to determine a total time Ti kto date by each medical imaging device i performing each imaging procedure type fc; determining a wear rate / ?fe.for each procedure type fc; outputting an indication of the determined wear rate f>k.

18. The non-transitory computer readable medium (26) of claim 17, wherein determining a wear rate (3k.f r each procedure type k includes: determining a total wear (3k■ Tksummed over all procedure types k.

19. The non-transitory computer readable medium (26) of either one of claims 17 and 18, wherein the monitoring includes: generating a table (34) of the available procedure types k along with the corresponding parameter values and aggregated time Ti kfor the current medical imaging device i and the resulting wear / 3k- Tkandreceiving a selection of one of the available procedure types k.

20. The non-transitory computer readable medium (26) of claim 19, wherein the monitoring further includes: analyzing the table (34) with a procedure equivalence table (36) to generate a suggested substitute procedure that may provide reduced wear by employing a different value of the parameter setting.

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