Systems and methods for optimizing component life versus image quality balance 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 setting assistance method (100) comprising: 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 ​​of one or more parameters of the imaging procedure, the one or more values ​​reducing wear accumulated on components (10) of the medical imaging device during the execution of the imaging procedure while satisfying the minimum image quality; and outputting a recommendation (32) for one or more values ​​of the determined one or more parameters of the imaging procedure.

Description

Technical Field

[0001] This invention generally relates to equipment and system monitoring technology, radiological technology, image quality technology, imaging equipment component wear technology and related technologies. Background Technology

[0002] Medical imaging systems are used in hospitals for diagnostic and interventional purposes. The use of such systems causes these devices or their components to wear out over time until failure occurs. Failed components are then replaced with new or refurbished parts. Accurately estimating when components will fail in the near future and understanding how to use and / or extend their lifespan is extremely useful. This insight is invaluable to users because, by providing appropriate feedback, they can adjust how they use the equipment to extend component lifespan.

[0003] Medical imaging systems, such as magnetic resonance (MR) systems, computed tomography (CT) systems, direct X-ray (DXR) systems, and image-guided therapy (IGT) systems, produce medical images that provide crucial information about parts of a patient's body. Clinical images should have good image quality, but simultaneously, a long lifespan for the imaging system and its components is also desirable. These two goals can be mutually exclusive. For example, users might choose certain scanning protocols to maximize image quality—such as using high voltage, high current, and a small focal spot in CT or DXR—leading to rapid aging of the X-ray tube anode. Conversely, choosing parameters that maximize component lifespan can negatively impact image quality. Furthermore, the aging of imaging system components depends on previously performed scans and upcoming scans, and is also affected by factors such as system cooling downtime. Users are often unaware of how to achieve the optimal trade-off between ideal image quality and long lifespan, and therefore, the parameter settings they often choose may place undue stress on the selected imaging applications. This also applies to scan sequences that could have been optimized during scheduling.

[0004] The current static protocol optimization of imaging components (especially X-ray tubes, generators, etc.) has a significant impact on component aging and lifespan, and if the scanning protocol is not adjusted and dynamically optimized based on the current component health status (e.g., based on the number of scans performed and their related statistics, geometry, patient condition, and estimates of certain key dynamic characteristics such as temperature, current / voltage, or impedance changes, combined with various usage paradigms of the system), it may lead to sudden failures and rapid deterioration.

[0005] Static protocols do not self-regulate based on the use of clinical models and current clinical conditions. They stress components as if they were still in a brand-new state and with initial conditions. However, in the process, components are subjected to inappropriate stress relative to their lifespan, leading to sudden failures.

[0006] The following section discloses some improvements to overcome these and other issues. Summary of the Invention

[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 comprising: 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 ​​of one or more parameters of the imaging procedure, the one or more values ​​reducing wear accumulated on components of the medical imaging device during the execution of the imaging procedure while satisfying the minimum image quality; and outputting a recommendation for the determined one or more values ​​of the one or more parameters of the imaging procedure.

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

[0009] In some embodiments disclosed herein, a non-transitory computer-readable medium stores instructions that can be read and executed by an electronic processor to perform an imaging method, the imaging method comprising: receiving historical data related to wear rates of components of a medical imaging device performing an imaging procedure; grouping imaging procedures with similar wear rates; and monitoring the operational history of the medical imaging devices used in the grouped imaging procedures to determine each medical imaging device. i Perform each imaging procedure type k Total time to date T i,k ; Determine each process type k Wear rate β k Output the determined wear rate β k Instructions.

[0010] One advantage is that it improves the quality of medical images while reducing wear and tear on the components of the medical imaging equipment used to acquire the images.

[0011] Another advantage is the ability to dynamically optimize the imaging protocol to reduce wear on imaging components while producing high-quality images.

[0012] Another advantage is that it allows for the estimation of wear and tear on a component by comparing its usage history with that of components that have failed.

[0013] Another advantage is that it allows for adjustments to how medical device components are used, thus delaying component failure.

[0014] The given embodiments may not provide the foregoing advantages, provide one, two, more or all of the foregoing advantages, and / or may provide other advantages that will become apparent to those skilled in the art upon reading and understanding this disclosure. Attached Figure Description

[0015] This disclosure can take the form of various components and component arrangements, as well as various steps and step arrangements. The accompanying drawings are for illustrative purposes only and should not be construed as limiting this disclosure.

[0016] Figure 1 An imaging apparatus according to this disclosure is shown schematically.

[0017] Figure 2 , Figure 3 and Figure 4 The use is illustrated schematically. Figure 1 Imaging methods of the device. Detailed Implementation

[0018] The following discloses methods for dynamically recommending imaging workflow settings that balance image quality with degradation. To this end, a minimum image quality is provided for the anatomical region of interest. This minimum image quality can be manually entered by the imaging technician (or specified in the radiology requisition form or in the notes for the upcoming examination entered by the reviewing radiologist), or it can be automatically determined based on relevant information from the radiology examination. As an example of the latter, if the examination is for a type that benefits from high resolution (e.g., detecting potentially small carcinogenic nodules), the minimum image quality can be set to a higher value than if the examination is for assessing coarser features (e.g., pulmonary effusion). The minimum image quality can be quantified in various ways, such as based on the signal-to-noise ratio (SNR) or by dose-normalized contrast-to-noise ratio (CNR).

[0019] Next, parameters of the imaging process that affect the amount of wear on the X-ray tube (or more generally, the amount of wear on the components whose wear is to be controlled) are determined, minimizing wear while meeting the minimum image quality. For X-ray tubes, key factors affecting wear typically include focal spot temperature, focal track temperature, and anode body temperature. However, these temperatures cannot be directly measured (at least in most X-ray tubes). To determine these temperatures, the operation of the X-ray tube is digitally simulated based on available information (e.g., measured X-ray tube shell temperature) and X-ray tube operating parameters (e.g., tube bias voltage in kV, filament current in mA, and pulse width (PW)). This model is developed based on historical data, thermodynamic simulations, etc. By applying this model to known operating parameters (e.g., kV, mA, and PW) and available information, the relevant anode temperatures are determined for a given imaging process / setup. Therefore, different imaging processes and settings can be modeled to determine recommended imaging processes and settings that minimize X-ray tube wear while providing the desired minimum image quality. These settings can be recommended to operators and can optionally be automatically set as the default process / settings.

[0020] In some embodiments, a slider or other user dialog box may be provided, through which the user manually adjusts the balance between image quality and wear. By moving the slider to adjust this balance, the system recalculates the optimal imaging process and imaging parameters after each adjustment.

[0021] In other publicly disclosed aspects, component wear is estimated based on historical data. To facilitate this method, available imaging procedures are grouped into types expected to produce similar wear rates. The operational history of a set of imaging devices is monitored to record the data for each device. i Perform each imaging procedure type k Total time to date Process type The resulting wear rate is expressed as Then, the total wear and tear is for all process types. The sum of the above .because The wear rate and total wear are known, so the wear rate can be determined by solving this linear equation. .

[0022] In some embodiments, a table of process type k can be used along with the corresponding parameter values ​​and the current imaging device. Cumulative time and the resulting wear and tear The data is presented together. The operator can then decide which workflow to use based on the wear levels listed in the table and the corresponding parameter values ​​(assuming the operator understands the impact of these parameters on image quality). Optionally, this information can be automatically analyzed along with a workflow equivalence table to provide the operator with suggested alternative workflows that reduce wear by using different parameter settings.

[0023] refer to Figure 1 An exemplary imaging system 1 is shown. Imaging system 1 may include a medical imaging device 2 configured to perform an imaging procedure. The medical imaging device 2 may, for example, include a computed tomography (CT) imaging device (shown), or a C-arm imaging device (sometimes used for cardiac imaging), or an image-guided therapy (IGT) system using X-ray imaging, fluoroscopy, digital radiography (DR) imaging device, or other imaging devices utilizing X-ray imaging (hereinafter referred to as "X-ray device" or variations thereof).

[0024] like Figure 1 As shown, the medical imaging device 2 may include an X-ray tube 10. Figure 1 The X-ray tube is shown in the image by partially removing the housing of the CT scanner 2, and... Figure 1 The illustration A in the upper left corner schematically shows a cathode 12 and an anode 14. The cathode 12 includes a filament 15, which is subjected to a filament current I. f When heated, electrons are emitted, and these electrons can generate a tube voltage V between the cathode 12 and the anode 14. t Under the influence of the target region, electrons are attracted to anode 14, and anode 14 (in some examples, a rotating anode that rotates around axis 13 to dissipate heat) includes the target region. - The electrons strike the target area, generating X-rays that form the X-ray beam used during image acquisition. - The tube current I forming the X-ray tube 10 t The intensity of the X-ray beam produced by X-ray tube 10 typically depends in a highly nonlinear manner on various operating parameters, such as the aforementioned tube voltage V. t Tube current and filament current I fThe X-ray tube 10 shown schematically is a simplified representation—modern commercial X-ray tubes used in X-ray imaging equipment typically include additional components, such as grids, 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 can introduce additional X-ray tube performance variables, such as grid voltage. The wear of the X-ray tube 10 depends on the operating temperature of the X-ray tube at critical locations, such as the temperature of the focal spot A on the rotating anode 14, the focal track temperature (where the focal track B is the path traversed by focal spot A as it rotates on the rotating anode 14, and is schematically indicated by dashed lines in illustration A), and the anode body temperature of the rotating anode 14. These temperatures for a given scan depend on various operating parameters, such as the tube voltage V. t Tube current and filament current I f .

[0025] X-ray detector 16 is configured to detect X-ray radiation. For example... Figure 1 As shown, detector 16 typically includes a detector array. Detector 16 also communicates electronically with electronic processing device 18 (e.g., a workstation computer, or more generally, a computer). Images generated by X-ray radiation produced by cathode 12 and anode 14 in medical imaging device 2 are processed by electronic processing device 18. The exemplary medical imaging device 2 employs tomographic imaging, where X-ray tube 10 and detector 16 rotate together around the object being imaged to acquire three-dimensional (3D) images of the object. In other types of X-ray imaging devices, these components may be fixed in position rather than rotating around the object being imaged, thus providing two-dimensional (2D) images. In a C-arm configuration, X-ray tube 10 and detector 16 can be moved to different perspectives (referred to as “views”) around the patient, providing, for example, a clinically significant view of the heart or a selected view of the interventional procedure in an IGT. In some embodiments, electronic processing device 18 may also function as a device controller for medical imaging device 2.

[0026] Electronic processing device 18 may also include one or more server computers, for example, interconnected to form a server cluster, cloud computing resources, etc., to perform more complex computing tasks. For example, in a common configuration, the local electronic processing device acts as a controller to control the imaging device 1 to perform image acquisition and also to record machine log data; while the server receives updates to the machine log data occasionally via a hospital network and / or Internet connection. The server analyzes the uploaded machine log data, for example, by applying a fault prediction model to predict when the X-ray tube 10 will fail. Additionally, in the embodiments disclosed herein, the server performs predictive analytics to determine when the operator should be alerted to stop manual calibration of the X-ray tube 10. Workstation 18 includes typical components such as electronic processor 20 (e.g., microprocessor), at least one user input device (e.g., mouse, keyboard, trackball, etc.) 22, and display device 24 (e.g., LCD display, plasma display, cathode ray tube display, etc.). In some embodiments, display device 24 may be a component separate from workstation 18, or may include two or more display devices. In some embodiments, when the electronic processing device 18 can also be used as a device controller for the medical imaging device 2, the display device 24 includes a controller display configured to present a representation of the imaging process and allow editing of the imaging process.

[0027] Electronic processor 20 is operatively connected to one or more non-transitory storage media 26. As a non-limiting illustrative example, non-transitory storage media 26 may include one or more of the following: disks, RAID or other magnetic storage media; solid-state drives, flash drives, electrically erasable read-only memory (EEROM) or other electronic storage; optical disks or other optical storage devices; various combinations thereof; and may be, for example, network storage devices, internal hard disk drives of workstation 18, various combinations thereof. It should be understood that any reference herein to one or more non-transitory media 26 should be broadly interpreted to cover a single medium or multiple media of the same or different types. Similarly, electronic processor 20 may be embodied as a single electronic processor or two or more electronic processors. Non-transitory storage media 26 stores instructions executable by at least one electronic processor 20. These instructions include instructions for generating a visualization of a graphical user interface (GUI) 28 displayed on display device 24.

[0028] Device 10 is configured as described above to perform a method or process 100 for monitoring a component of a medical device. Although the medical device is described herein as a medical imaging device 2 and the component as an X-ray tube 10, method 100 can be applied to any suitable component of any suitable medical device. Non-transitory storage medium 26 stores instructions that can be read and executed by at least one electronic processor 20 to perform the disclosed operations, including performing the monitoring method or process 100. In some examples, method 100 may be performed at least partially by cloud processing.

[0029] refer to Figure 2 An illustrative embodiment of an example of monitoring method 100 is schematically shown as a flowchart. At operation 102, the electronic processing device 18 receives the minimum image quality for imaging an anatomical region of interest to be imaged by the medical imaging device 2. In some embodiments, input indicating the minimum image quality in the anatomical region of interest is received via user input provided by a clinician using at least one user input device 22 (e.g., mouse click, key press, finger click, or swipe, etc.). In other embodiments, the minimum image quality in the anatomical region of interest is determined by the electronic processing device 18 based on information related to the imaging process. In one example, the minimum image quality includes the signal-to-noise ratio (SNR). In another example, the minimum image quality includes the contrast-to-noise ratio (CNR).

[0030] At operation 104, one or more values ​​of one or more parameters of the imaging process are determined, which reduce the amount of wear accumulated on components of the medical imaging device 2 (e.g., X-ray tube 10) while meeting minimum image quality. For example, the determined imaging process parameters include one or more of the focal spot temperature, focal track temperature, and anode body temperature of the X-ray tube 10.

[0031] In some embodiments, a model 30 (e.g., an artificial neural network (ANN)) stored in non-transitory storage medium 26 is configured to generate a digital simulation of the X-ray tube's operation based on information related to the imaging process to determine one or more parameters of the imaging process. This information may include, for example, one or more of the following: X-ray tube housing temperature, X-ray tube operating parameters, X-ray tube bias voltage, filament current, and pulse width. In some embodiments, the simulation performed by ANN 30 may also show a comparison of the performance of the imaging system 1 using a certain component (e.g., tube 10) with its performance using a new component. In some cases, a third party may use a refurbished tube 10, which may require higher energy to operate than a brand new tube 10. Therefore, image quality is achieved through system tuning at the cost of increased radiation dose and wear on components.

[0032] At operation 106, a recommendation 32 for one or more values ​​of one or more parameters of the determined imaging process is output, for example, to display device 24. The recommendation 32 can be displayed by filling parameter value fields in a representation of the imaging process with the determined values. In some embodiments, a user dialog box 34 (e.g., a slider) is displayed on GUI 28, indicating a balance between minimum image quality and one or more parameters in the imaging process that affect wear on X-ray tube 10. User input (e.g., mouse click, key press, finger click, or swipe, etc.) instructing adjustments to the slider 34 is received to adjust one or more recommended settings. The adjusted settings can be used to adjust the imaging process.

[0033] In another embodiment, at operation 108, the medical imaging device 2 is controlled to perform an imaging procedure using one or more recommended settings.

[0034] In a specific example, method 100 can be performed by determining the wear rate of different processes of the X-ray tube 10. Now refer to... Figure 3 Another illustrative embodiment of the monitoring method 200 is schematically shown as a flowchart. At operation 202, the electronic processing device 18 receives historical data related to the wear rate of the X-ray tubes 10 of the plurality of imaging devices 2. At operation 204, imaging processes with similar wear rates are grouped together.

[0035] At operation 206, the operational history of medical imaging device 2 was monitored to determine the status of each medical imaging device. Perform each imaging procedure type Total time to date Therefore, a usable process type is generated. Table 34 (stored in non-transitory storage medium 26) along with the corresponding parameter values ​​and the current medical imaging equipment Cumulative time and the resulting wear and tear The user input provided via at least one user input device 22 is used to receive information about the available process type. One option is to analyze Table 34 using the process equivalence table 36 to generate suggested alternative processes that can reduce wear by using parameter settings with different values.

[0036] At operation 208, by, for example, determining all process types Total wear accumulated To determine each process type Wear rate At operation 210, the determined wear rate is output. Instructions, for example, are output on display device 24.

[0037] Example The apparatus 10 and method 100 are described in more detail below. Continuous monitoring of the system allows for the identification of risky situations where components are under high stress. Additional information regarding the patient's schedule and planned imaging procedures also allows for the prediction of the impact of subsequent imaging procedures.

[0038] refer to Figure 4 Another non-limiting illustrative embodiment of method 100 (referred to as method 300) is shown. In operation 302, real-time data collection of temperature information is performed. (Return to Reference) Figure 1 These temperatures may not include some key temperatures used for wear monitoring, such as the temperature of focal spot A and focal rail B, because suitable thermocouples, thermistors, or other temperature sensors are typically not available at these locations. Therefore, in operation 304, these temperatures are estimated using model 30 based on available data (such as anode body temperature) collected in operation 302. In operation 306, the temperatures during the upcoming scans in the patient schedule 158 are estimated. This may also optionally utilize model 30. In operation 310, the estimated remaining lifetime (e.g., determined based on the wear estimated in operation 304) may be considered, and the imaging system parameters are adjusted in a way that balances desired image quality with lifetime target 312. In operation 310, image quality and the reduction of aging / increased lifetime effects are optimized according to the selected trade-off preference 312.

[0039] Note that the target input 312 corresponds to Figure 2 The lowest image quality input 102 in the embodiment can include additional targets such as lifetime targets. Similarly, it can be understood that operations 304, 306, and 310 provide... Figure 2 A more detailed example of parameter determination operation 104 is provided in the embodiments. In some non-limiting examples, these customization preferences 312 may be selected based on payment models and service contracts. Customers can select more detailed monitoring and simulation and prediction based on customized models as additional services via "Software as a Service". Inputs to performance and aging prediction are based on monitoring data 302 and patient schedule data 158, including precise scan time, cooling time, and component history data, actual environmental conditions (room temperature, etc.), and a risk database containing key combination information that could lead to failure. In some cases, the algorithm ensures a minimum image quality that is guaranteed and necessary for patient diagnosis.

[0040] For image processing like CT reconstruction, the selected system parameter setting 314 is reported to the reconstruction unit and taken into account to achieve optimized image quality. This also applies to some image processing steps in DXR, IGT, and mobile systems. These settings can be displayed, and the user can optionally use sliders to adjust the balance between image quality and lifetime, for example, as described above for... Figure 2 As described in embodiment 106. Then, using, for example, corresponding to Figure 2 The parameter settings and timing 314 of operation 108 in the embodiment are used to perform scan 316.

[0041] The parameters that can be included in the trade-off optimization 310 include, for example, kV, duty cycle, peak mA, pulse width, PPS and FPS, acquisition time, focal spot size (including continuous size variations between typical small and large focal spots), and small focal spot position variations on the anode (such as a fine-tuned DFS (dynamic / dual-focal spot) or QFS (quad-focal spot - 4 position) mode with minor offsets that do not affect focal spot alignment and ASG (anti-scattering grid) occlusion, and taking into account beam geometry formation). It can also consider various patient-related key parameters (motion associated with clinical tasks and scattering based on imaging volume) and simultaneously optimize the geometry and acquisition parameters to obtain optimal image quality without stressing the IC components.

[0042] Optimization 310 can also utilize the minimum required image quality (IQ) component for a specific application (considering patient dose) in objective 312 and select between different trade-off parameters, including the usage mode / load factor already used and resulting in a specific temperature distribution on the anode / tube interior / tube shell / system interior / cooling unit / generator. Several high peak power pulses under a small focal spot differ from long-duration X-ray acquisition under a large focal spot in terms of heat load and heat distribution (including time delays for cooling). By considering all parameters, it is possible to approach the limits and obtain better image quality without risking damage to the X-ray tube and / or intentionally reducing the system's heat load, while still maintaining view-dependent, defined minimum image quality (defined on the view of the region of interest) and / or customized trade-off settings.

[0043] Temperature can be an important parameter for estimating wear on the X-ray tube 10 because there are various heat and cooling sources within the system. This includes the X-ray tube 10 with rotating anode 14 and the cooling system, as well as the fan in the system (…). Figure 1Features not shown in Illustration A) and the room's air conditioning system. Ambient temperature settings are not always ideal, and the system's operational history significantly influences the actual temperatures of components and the system. All these parameters can be simulated using Model 30 and validated in real-time via temperature sensors located in accessible locations, contributing to a reliable and near-real-time temperature distribution that can be used to calculate the mechanical stress and damage risk faced by the upcoming scan.

[0044] Using digital twins (e.g., Model 30) to accurately model physical parameters helps to gain a deeper understanding of the most stressful combinations of operating parameters. Simultaneously, image quality predictions that depend on these parameters are also estimated, and can even be monitored in real time via signal-to-noise ratio analysis from the detector, beam position, and shape stability monitoring.

[0045] Image protocols define image quality requirements. For example, in the case of CT scans, this depends on the actual scanning location during the scan and can be dynamically adjusted (depending on the viewpoint and field of interest). In the case of DXR, it depends both on patient characteristics and on the region of interest with precise X-ray absorption conditions.

[0046] Patient data (or comparable patient data) acquired prior to the actual scan can further improve the prediction of the required and necessary imaging parameters. This is particularly valuable because, for example, similar temperature changes resulting from procedures already performed on comparable patient cases are available, and these temperature changes can also be used to predict the actual temperature when considering the expected operation time.

[0047] Using historical analysis data for real-time prediction in simulation models is a key function of adaptive parameter optimization.

[0048] To implement a real-time algorithm, a model for temperature estimation, for example, must be run. This model involves estimating temperature using data from scheduled patients / scans to predict performance changes over time and identify temperature-related risks. The optimizer can then run using variations in scan parameters and the scheduling of patients to identify optimal operating parameters based on a cost function. This cost function is a trade-off between image quality and the purchased / expected / guaranteed lifetime of components.

[0049] To execute the optimization procedure, real-time data from several sensors are collected from the imaging system. Information from the scheduling system, utilizing actual data and data from the scans prior to simulating and predicting actual temperature distributions for all critical components / locations, allows for load distribution simulations of the upcoming scan. Information from the upcoming scan and relevant temperature predictions based on actual conditions allow for analysis of component risk and the impact of aging / failure. A trade-off simulation process can be performed using boundary conditions from the service contract / payment model (minimum guaranteed lifespan, minimum image quality, minimum number of patient scans, etc.), and simulations can be performed across a possible parameter space. Optimal matching parameters are selected and used for the upcoming scan.

[0050] For method 200, a specific set of faulty components (e.g., X-ray tube 10) with detailed usage records are used, including process type, duration of each process, parameter settings used, etc. Based on process similarity, these components are grouped into a relatively small number of clusters. Assuming wear increases linearly with process duration, a wear factor is estimated for each process cluster or type, which allows estimation of the component's cumulative wear based on its usage profile. This can then be used to estimate its remaining lifespan. By comparing one usage profile with other usage profiles, possibilities for extending the component's lifespan are created.

[0051] Therefore, a relatively large set of [methods / mechanisms] is used. One faulty component, numbered as They are installed in the equipment respectively. These devices contain records of the executed processes. Each record is maintained by... right The array consists of, where It summarizes the equipment. All performed throughout the entire lifespan The process, among which Representation process The parameter settings used during the process Representation process The time spent. This time can be the exposure time for generating a single image or a sequence of images (i.e., video). Parameter settings can be considered as... The value is in the form of a value vector, where each value specifies a device setting, such as voltage, current, filament selection, patient characteristics, etc.

[0052] All vectors The covered parameter space can be subdivided into a relatively small one based on the similarity of parameter settings. The process type is grouped, for example, using Euclidean distance and one of several well-known clustering techniques. Therefore, clusters containing too few processes can be discarded and considered outliers. The identified process type is denoted as... ,in .like ,in If it is one of the process types, then the process Belongs to type .

[0053] each The values ​​are summed and aggregated according to the process type. Therefore, for each process type... and equipment ,equipment In process type Total time spent Equation (1): (1) column vector Known as equipment Usage overview.

[0054] By solving the following about (in The system of equations, The equipment was quantified in the process type. The relative rate of cumulative wear. For The equation is shown in Equation 2: (2) in This is the error term. Therefore, for For each of the faulty components, there exists an equation. The summation of terms can be viewed as a dimensional vector (referred to as models) and equipment The inner product between the usage profiles. (Abbreviated as...) .

[0055] The above process is summarized in Equation 3: (3) Equation 3 shows the device The data flow starts with the timing of a single process, is aggregated by process type, and finally obtains an aggregated timing that is weighted by process type. Assume... The system of equations (1) can be solved using a technique called multiple linear regression.

[0056] In Equation 2, the dependent variable on the left side is a constant, namely 1. Furthermore, the right side of the equation does not have an intercept term (usually denoted as ). If an intercept term exists, the model will provide... The solution, and all others The value will be zero, meaning it's a virtually useless model.

[0057] The constant value of 1 represents the normalized total lifetime. The reason is as follows: assuming the obtained model... It is a fairly good model, that is The value is relatively small. Assume a device that is still working. Usage overview And this profile happens to be of a faulty device. One-third of the usage overview. Therefore, it is reasonable to assume that the device... It has already reached one-third of its lifespan. And, in fact, the inner product mentioned above will produce approximately one-third of its value.

[0058] Make the device The current absolute Age is In general absolute Lifespan is Then, Equation 4 is generated: (4) Equation 4 can be rewritten as Equation 5: (5) Therefore, its absolute remaining lifespan This can be written as Equation 6: Table 1 shows the equipment in three columns. Usage: Dimensions are The table displays the model, usage overview, and contribution for each process type. The sum of contributions, representing the current relative age of the equipment, is given at the bottom of the "Contribution" column. This table clearly shows the equipment's usage to date for each process type. It allows users to easily understand how to extend the equipment's remaining lifespan.

[0059] For example, if in the device Above, you can use process type Replacement process type Both take the same amount of time, and Then execute Instead It is less expensive in terms of wear and tear. If the time consumption is different, that is, for... and There are two different times. and Then when When, choose Better. These times and They are related in the following sense: if the process type It will take time Then replace the process type. It will take time .

[0060] Table 1 is a sample dashboard used to illustrate device usage.

[0061] If a table lists the substitutability between process types along with the aforementioned time information, automating this process and automatically providing such recommendations to users becomes relatively simple. It's even possible to recommend a list of several items in descending order of effectiveness (e.g., (Item) Recommended list.

[0062] By displaying users the current usage profile of the model and equipment, users can understand which process types are "costly" in terms of wear and tear, and which are not. They can also understand their own usage profile, i.e., the degree of use of each process type. By suggesting a usage profile of a faulty device that is similar to their own, users can significantly save life by adjusting their usage profile accordingly. For example, if process types could be used... Replacement process type And since both take the same amount of time, then The contribution will drop to 0.00, while The contribution will increase by 0.02 to 0.07, with a net improvement of 0.03, which is more than 10% of the current relative age.

[0063] However, if The time spent is Three times that, The contribution will still decrease to 0, but The contribution will increase This results in a net loss of 0.01, reaching 0.11. In this case, it is best to use... Alternative This resulted in a net improvement of approximately 0.0083.

[0064] This disclosure has been described with reference to preferred embodiments. Various modifications and variations may arise upon reading and understanding the foregoing detailed description. The exemplary embodiments are intended to be construed as including all such modifications and variations, provided they fall within the scope of the claims or their equivalents.

Claims

1. An imaging system (1), comprising: A medical imaging device (2) is configured to perform an imaging process; and An electronic processor (18) is programmed to execute an imaging process setup assistance method (100), the imaging process setup assistance method comprising: Receive the minimum image quality required to image the anatomical region of interest to be imaged by the imaging process performed by the medical imaging device; Determine one or more values ​​for one or more parameters of the imaging process, wherein the one or more values ​​reduce the amount of wear accumulated on the components (10) of the medical imaging device during the execution of the imaging process while meeting the minimum image quality; and Output a recommendation for one or more values ​​of one or more parameters of the determined imaging procedure (32).

2. The imaging system (1) according to claim 1, wherein: The medical imaging device (10) includes a controller display (24) configured to present a representation of the imaging process and allow editing of the imaging process; and The output of the recommendation (32) includes filling the parameter value field in the representation of the imaging process with one or more determined values.

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

4. The imaging system (1) according to any one of claims 1-3, wherein, The medical imaging device (2) includes an X-ray imaging device, the component (10) includes an X-ray tube, and one or more parameters of the determined imaging process include one or more of the focal spot temperature, focal track temperature and anode body temperature.

5. The imaging system (1) of claim 4, wherein The determination includes: Using model (30), a digital simulation of the operation of the X-ray tube is generated based on information related to the imaging process.

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

7. A non-transitory computer-readable medium (26) storing instructions that can be read and executed by an electronic processor (20) to perform an imaging method (100), the imaging method comprising: Receive the minimum image quality required to image the anatomical region of interest to be imaged by the imaging process performed by the medical imaging equipment; Determine one or more values ​​for one or more parameters of the imaging process, wherein the one or more values ​​reduce the amount of wear accumulated on the components (10) of the medical imaging device (2) to be used in performing the imaging process while meeting the minimum image quality; and Output a recommendation for one or more values ​​of one or more parameters of the determined imaging procedure (32).

8. The non-transitory computer readable medium (26) of claim 7, wherein, The minimum image quality required to image the anatomical region of interest during the imaging process includes: The system receives user input, provided by a clinician, indicating the minimum image quality for the anatomical region of interest.

9. The non-transitory computer readable medium (16) of claim 7, wherein, The minimum image quality received for the anatomical region of interest to be imaged during the imaging process includes: Based on information related to the imaging process, the lowest image quality in the region of interest is determined.

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

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

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

13. The non-transitory computer readable medium (26) of claim 12, wherein, The determination includes: Using model (30), a digital simulation of the operation of the X-ray tube is generated based on information related to the imaging process.

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

15. The non-transitory computer readable medium (26) according to either one of claims 13 and 14, wherein, The method (100) further includes: Using the model (30), digital simulations of the operation of refurbished X-ray tubes and brand-new X-ray tubes were determined; and Output a recommendation regarding whether to use the refurbished X-ray tube or the brand-new X-ray tube (32).

16. The non-transitory computer-readable medium (26) according to any one of claims 7-15, wherein, The method (100) further includes: On the display device (24) of the electronic processing device (18) operatively connected to the medical imaging device (2), a user dialog box (34) is displayed, which indicates the balance between the minimum image quality and one or more parameters that affect the amount of wear on the component (10) in the imaging process; Receive user input instructing adjustments to the user dialog box to modify one or more recommended settings; and The imaging process can be adjusted using one or more modified settings.

17. A non-transitory computer-readable medium (26) storing instructions that can be read and executed by an electronic processor (20) to perform an imaging method (100), the imaging method comprising: Historical data related to the wear rate of components (10) of the medical imaging device (2) that receives and performs the imaging process; Group imaging procedures with similar wear rates; Monitor the operational history of the medical imaging equipment used in the grouped imaging process to identify each medical imaging device. Perform each imaging procedure type Total time to date ; Determine each process type Wear rate ; Output the determined wear rate Instructions.

18. The non-transitory computer-readable medium (26) according to claim 17, wherein, Determine each process type Wear rate include: Determine all process types Total wear accumulated .

19. The non-transitory computer-readable medium (26) according to any one of claims 17 and 18, wherein, The monitoring includes: Generate usable process types Table (34) along with the corresponding parameter values ​​and current medical imaging equipment Cumulative time and the resulting wear and tear ;and Receive the available process type One of the options.

20. The non-transitory computer-readable medium (26) according to claim 19, wherein, The monitoring also includes: The process equivalence table (36) is used to analyze the table (34) to generate a suggested alternative process that can reduce wear by using parameter settings with different values.

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