Intelligent intervention distribution for medical imaging scanners

Abstract

Systems / techniques are provided that facilitate intelligent intervention distribution for a medical imaging scanner. In various embodiments, a system may access a set of medical imaging interventions (e.g., 106) to be performed on multiple medical imaging scanners (e.g., 104). In various aspects, the system may determine a recommended assignment (e.g., 404) based on a plurality of digital scanner twins (e.g., 402) each including one or more cathode filament wear models (e.g., 402 (1) (1), 402 (1) (m), 402 (n) (1), 402 (n) (m)) for a respective one of the plurality of medical imaging scanners, the recommended assignment indicates how to assign the set of medical imaging interventions among the plurality of medical imaging scanners. In various examples, the system may assign the set of medical imaging interventions among the plurality of medical imaging scanners according to the recommended assignment.

Description

Technical Field

[0001] The present disclosure relates generally to medical imaging scanners, and more particularly to intelligent intervention allocation for medical imaging scanners. Background Art

[0002] Medical imaging scanners may utilize one or more X-ray tubes, each of which may include one or more cathode filaments. How the medical imaging scanner is used can affect how the cathode filaments are depleted or consumed, which can complicate maintenance or repair of the medical imaging scanner.

[0003] Therefore, systems or techniques that can address one or more of these technical issues may be desirable. Summary of the invention

[0004] The following is a summary of the invention to provide a basic understanding of one or more embodiments. The summary is not intended to identify key or important elements, nor is it intended to delineate any scope of a specific embodiment or any scope of the claims. Its sole purpose is to present concepts in a simplified form as a prelude to a more detailed description presented later. In one or more embodiments described herein, an apparatus, system, computer-implemented method, device, or computer program product that facilitates intelligent intervention allocation for a medical imaging scanner is described.

[0005] According to one or more embodiments, a system is provided. The system may include a non-transitory computer readable memory that may store computer executable components. The system may also include a processor that may be operably connected to the non-transitory computer readable memory and may execute the computer executable components stored in the non-transitory computer readable memory. In various embodiments, the computer executable component may include an access component that may access a set of medical imaging interventions to be performed on multiple medical imaging scanners. In various aspects, the computer executable component may include an allocation component that may determine a recommended allocation based on multiple digital scanner twins, each of which includes one or more cathode filament wear models of a corresponding one of the multiple medical imaging scanners, and the recommended allocation indicates how to allocate the set of medical imaging interventions between the multiple medical imaging scanners. In various instances, the computer executable component may include a result component that may allocate the set of medical imaging interventions between the multiple medical imaging scanners according to the recommended allocation. In various cases, the recommended assignment may indicate that a first medical imaging intervention in the set of medical imaging interventions is to be assigned to a first medical imaging scanner in the plurality of medical imaging scanners, and the results component may instruct the first medical imaging scanner to locally submit or prepare the first medical imaging intervention.

[0006] According to one or more embodiments, a computer-implemented method is provided. In various embodiments, the computer-implemented method may include: accessing, by a device operatively connected to a processor, a set of medical imaging interventions to be performed on a plurality of medical imaging scanners. In various aspects, the computer-implemented method may include: determining, by the device and based on a plurality of digital scanner twins, the plurality of digital scanner twins each including one or more cathode filament wear models of a corresponding one of the plurality of medical imaging scanners, the recommended allocation indicating how to allocate the set of medical imaging interventions among the plurality of medical imaging scanners. In various instances, the computer-implemented method may include: allocating, by the device, the set of medical imaging interventions among the plurality of medical imaging scanners according to the recommended allocation. In various instances, the recommended allocation may indicate that a first medical imaging intervention in the set of medical imaging interventions is to be allocated to a first medical imaging scanner in the plurality of medical imaging scanners, and the allocation may include instructing, by the device, the first medical imaging scanner to locally submit or prepare the first medical imaging intervention.

[0007] According to one or more embodiments, a computer program product for facilitating intelligent intervention allocation of medical imaging scanners is provided. In various embodiments, the computer program product may include a non-transitory computer readable memory having program instructions embodied therein. In various aspects, the program instructions may be executable by a processor to enable the processor to access a set of medical imaging interventions to be performed on multiple medical imaging scanners. In various instances, the program instructions may be further executable to enable the processor to determine a recommended allocation based on multiple digital scanner twins, each of which includes one or more cathode filament wear models of a corresponding one of the multiple medical imaging scanners, the recommended allocation indicating how to allocate the set of medical imaging interventions between the multiple medical imaging scanners. In various cases, the program instructions may be further executable to enable the processor to allocate the set of medical imaging interventions between the multiple medical imaging scanners according to the recommended allocation. In various aspects, the recommended allocation may indicate that a first medical imaging intervention in the set of medical imaging interventions will be allocated to a first medical imaging scanner in the multiple medical imaging scanners, and the processor may instruct the first medical imaging scanner to submit or prepare the first medical imaging intervention locally. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 A block diagram of an example non-limiting system that facilitates intelligent intervention allocation for a medical imaging scanner in accordance with one or more embodiments described herein is illustrated.

[0009] Figure 2An example non-limiting block diagram of multiple medical imaging scanners is illustrated in accordance with one or more embodiments described herein.

[0010] Figure 3 An example non-limiting block diagram of a set of medical imaging interventions according to one or more embodiments described herein is shown.

[0011] Figure 4 A block diagram of an example non-limiting system including multiple digital scanner twins and recommended intervention assignments that facilitate intelligent intervention assignments for a medical imaging scanner is illustrated in accordance with one or more embodiments described herein.

[0012] Figure 5 An example non-limiting block diagram of multiple digital scanner twins according to one or more embodiments described herein is illustrated.

[0013] Figure 6 to Figure 7 An example non-limiting block diagram showing how an optimizer engine may be utilized to generate a recommended intervention allocation in accordance with one or more embodiments described herein is illustrated.

[0014] Figures 8 to 9 An example non-limiting block diagram showing how a deep learning neural network may be utilized to generate a recommended intervention allocation in accordance with one or more embodiments described herein is illustrated.

[0015] Fig.10 A flow chart illustrating an example non-limiting computer-implemented method that facilitates intelligent intervention allocation for a medical imaging scanner in accordance with one or more embodiments described herein.

[0016] Fig.11 A block diagram illustrating an example non-limiting operating environment in which one or more embodiments described herein may be facilitated.

[0017] Fig.12 An example networking environment is illustrated that is operable to perform various implementations described herein. DETAILED DESCRIPTION

[0018] The following detailed description is merely illustrative and is not intended to limit the embodiments or the application / use of the embodiments. In addition, it is not intended to be bound by any express or implied information set forth in the aforementioned "background technology" or "invention content" section or "detailed description" section.

[0019] One or more embodiments are now described with reference to the accompanying drawings, wherein the same reference numerals are used to represent the same elements throughout. In the following description, for the purpose of explanation, many specific details are set forth in order to provide a more thorough understanding of one or more embodiments. However, it is apparent that in various cases, one or more embodiments may be practiced without these specific details.

[0020] Medical imaging scanners (e.g., X-ray scanners, computed tomography (CT) scanners, positron emission tomography (PET) scanners) can generate medical images using one or more X-ray tubes (e.g., fixed or Coolidge X-ray tubes, rotating X-ray tubes, microfocus X-ray tubes). In particular, a medical patient can be positioned on a table or within a gantry of the medical imaging scanner; the medical imaging scanner can emit radiation beams from the one or more X-ray tubes; such radiation beams can pass through the medical patient and be altered thereby (e.g., different anatomical structures within the medical patient can absorb or transmit radiation of different or corresponding amounts, amplitudes, or wavelengths); and one or more detectors (e.g., photographic plate detectors, photographic film detectors) can capture those altered radiation beams, thereby generating a medical image of the medical patient (e.g., in some cases, the detectors can capture sinograms, and such sinograms can be reconstructed into a medical image, such as via filtered backprojection).

[0021] An X-ray tube may include or otherwise rely on one or more cathode filaments (e.g., tungsten filaments). In particular, an electric current may be passed through such one or more cathode filaments so as to produce one or more electron streams that strike a target (e.g., a tungsten target) of the X-ray tube, where the target may be attached to an anode of the X-ray tube. Such impacts between the one or more electron streams and the target may cause a radiation beam to be emitted from the target. The inventors of the various embodiments described herein recognized that, depending on the frequency or intensity with which a medical imaging scanner is used, the cathode filaments may be unevenly depleted or consumed, which may complicate maintenance or repair of the medical imaging scanner.

[0022] As a non-limiting example, when at least one cathode filament of an X-ray tube is exhausted, the X-ray tube may be considered to have reached the end of its useful life, thereby requiring replacement (e.g., although the X-ray tube may technically still be usable even with the cathode filament exhausted, the post-exhaustion performance of the X-ray tube may be significantly degraded). However, if less than all of the cathode filaments in the X-ray tube are exhausted when the X-ray tube reaches the end of its useful life, replacement of the X-ray tube may be considered at least partially wasteful (e.g., any unexhausted cathode filaments of the X-ray tube are thrown away along with the at least one exhausted cathode filament).

[0023] As another non-limiting example, when a medical imaging scanner utilizes multiple X-ray tubes, individual X-ray tubes in such multiple X-ray tubes may be individually replaced when they each reach the end of their useful lives. However, if such multiple X-ray tubes reach the end of their useful lives at different or staggered times, multiple staggered maintenance visits to the medical imaging scanner may be required, which may be considered time consuming or inconvenient.

[0024] The complexity of such maintenance or repair may be exacerbated where an entire fleet of medical imaging scanners is involved. For example, a hospital network may manage dozens, hundreds, or even thousands of medical imaging scanners and may encounter problems when multiple scanners require maintenance at the same time (e.g., excessive or widespread scanner downtime may ensue; increased travel or inconvenience to the repair team may ensue).

[0025] Therefore, systems or techniques that can address one or more of these technical issues may be desirable.

[0026] Various embodiments described herein may address one or more of these technical issues. One or more embodiments described herein may include a system, a computer-implemented method, an apparatus, or a computer program product capable of facilitating intelligent intervention allocation for medical imaging scanners. In particular, given a list of medical imaging interventions to be implemented on a fleet of medical imaging scanners, various embodiments described herein may include determining how to allocate the list of such medical imaging interventions among the fleet of medical imaging scanners so as to reduce cathode filament waste or so as to increase the ease of servicing the fleet of medical imaging scanners, hence the term "intelligent". Various embodiments described herein may facilitate such intelligent allocation by utilizing a digital scanner twin that models the accumulation of cathode filament wear for a fleet of medical imaging scanners, respectively. In some cases, various embodiments may involve an optimization engine that simulates multiple (e.g., all possible) intervention allocations on a digital scanner twin in order to identify which specific intervention allocation satisfies any suitable optimization criteria, where the optimization criteria may serve as a proxy for reduced cathode filament waste or increased scanner maintenance convenience (e.g., the optimization criteria may be minimization of cathode filament wear differences within medical imaging scanners in the fleet; the optimization criteria may be maximization of cathode filament wear differences between different medical imaging scanners in the fleet; the optimization criteria may be minimization of cathode filament wear differences between different medical imaging scanners in the fleet that are physically located at the same location as each other). In other cases, various embodiments may involve utilizing a deep learning neural network that may receive as input a list of medical imaging interventions and may also receive as input a current time cathode filament wear state indicated by a digital scanner twin, and may determine as output an intervention allocation that reduces cathode filament waste or increases scanner maintenance convenience. In any case, the inventors recognize that different medical imaging interventions may result in different incremental increases in cathode filament wear for different medical imaging scanners. Thus, various embodiments described herein may involve utilizing digital scanner twins that respectively correspond to a fleet of medical imaging scanners in order to identify how to wisely or beneficially distribute medical imaging interventions among the fleet of medical imaging scanners.

[0027] The various embodiments described herein may be considered to be computerized tools (e.g., any suitable combination of computer executable hardware or computer executable software) that can facilitate intelligent intervention allocation for medical imaging scanners. In various aspects, such computerized tools may include an access component, an allocation component, or a result component.

[0028] In various embodiments, there may be a plurality of medical imaging scanners. In various aspects, each of the plurality of medical imaging scanners may be any suitable medical imaging scanner capable of capturing or generating medical scan images via one or more X-ray tubes, wherein each X-ray tube includes one or more cathode filaments (e.g., any of the plurality of medical imaging scanners may be an X-ray scanner, a CT scanner, or a PET scanner). In various instances, different medical imaging scanners of the plurality of medical imaging scanners may be deployed or positioned in the same or different physical locations relative to each other (e.g., different medical imaging scanners may be located in the same hospital building or hospital ward relative to each other; different medical imaging scanners may be located in different hospital buildings or hospital wards relative to each other).

[0029] In various embodiments, there may be a set of medical imaging interventions. In various aspects, each medical imaging intervention in the set of medical imaging interventions may be a respective scanning protocol or scanning procedure that is or has been customized for a respective medical patient and is to be performed on any one of a plurality of medical imaging scanners.

[0030] In various instances, it may be desirable to determine how to distribute the set of medical imaging interventions among multiple medical imaging scanners in order to reduce cathode filament waste associated with the multiple medical imaging scanners, or in order to increase maintenance convenience associated with the multiple medical imaging scanners. As described herein, a computerized tool can facilitate such a determination.

[0031] In various embodiments, the access component of the computerized tool can electronically access multiple medical imaging scanners or the set of medical imaging interventions. In particular, in various aspects, the access component can electronically communicate with multiple medical imaging scanners (e.g., send electronic data to the medical imaging scanners, receive electronic data from the medical imaging scanners). In addition, in various instances, the access component can electronically receive, retrieve, or obtain the set of medical imaging interventions from any suitable centralized or decentralized data structure (e.g., a graph data structure, a relational data structure, a hybrid data structure), whether remote from the access component or local to the access component. In any case, the access component can electronically access multiple medical imaging scanners or the set of medical imaging interventions, so that the access component can act as a conduit through which or by means of which other components of the computerized tool can electronically interact with multiple medical imaging scanners or the set of medical imaging interventions (e.g., read, write, edit, copy, manipulate, control).

[0032] In various embodiments, an allocation component of a computerized tool may electronically store, maintain, control, or otherwise access a plurality of digital scanner twins that correspond to a plurality of medical imaging scanners, respectively. In various aspects, the digital scanner twins may be considered to be virtual counterparts of the respective medical imaging scanners that may be used for virtualized simulation or testing. In various examples, the allocation component may electronically determine how the set of medical imaging interventions should be allocated among the plurality of medical imaging scanners by utilizing the plurality of digital scanner twins.

[0033] More specifically, each digital scanner twin may correspond to a respective medical imaging scanner, such that the digital scanner twin may include a respective cathode filament wear model for each cathode filament of the medical imaging scanner. In various aspects, the cathode filament wear model may be any suitable computational or analytical physics-based model (e.g., utilizing an energy balance equation, a heat transfer equation, a charge conservation equation) that may represent the amount of cumulative wear, degradation, or exhaustion that the respective cathode filament has experienced to date. In various instances, the cathode filament wear model may be based on (e.g., may be utilized as a coefficient) any suitable property of its respective cathode filament (e.g., material composition, resistance, thermal conductivity, heat capacity, thickness, coil length). In various cases, the cathode filament wear model may be further based on any suitable operational input parameter (e.g., magnitude of applied current; magnitude of applied voltage; power-on time) experienced by its respective cathode filament (e.g., may be considered as an independent variable). Note that the properties of the cathode filament may be considered as constants, and the operational input parameters of the cathode filament may alternatively be considered as variables. Indeed, the operating input parameters of the cathode filament may depend on or otherwise vary with any medical imaging intervention being implemented using the cathode filament (e.g., different scanning protocols may involve applying different currents or voltages to the cathode filament for different time spans; different medical patient sizes or body mass indexes may require different currents or voltages to be applied to the cathode filament for different time spans; different medical patient pathologies or health states may require different currents or voltages to be applied to the cathode filament for different time spans).

[0034] In any case, the allocation component can determine how to wisely or beneficially allocate the set of medical imaging interventions among the plurality of medical imaging scanners by utilizing the plurality of digital scanner twins.

[0035] In various embodiments, the allocation component can facilitate such allocation determination by utilizing an optimizer engine. In various examples, the optimizer engine can be any suitable computing or software-based platform (e.g., The optimizer engine may be configured to electronically simulate how any given medical imaging intervention will affect the wear accumulated by the cathode filaments of any given medical imaging scanner. In other words, the optimizer engine may determine how much incremental wear the cathode filaments of the given medical imaging scanner will experience if the given medical imaging intervention is performed on the given medical imaging scanner. Thus, for any given distribution of the set of medical imaging interventions among the multiple medical imaging scanners, the optimizer engine may electronically simulate on the multiple digital scanner twins how the given distribution will affect the cathode filament wear profiles of the multiple medical imaging scanners. Thus, in various aspects, the optimizer engine may identify multiple different (e.g., all possible) ways of allocating the set of medical imaging interventions among multiple medical imaging scanners, the optimizer engine may determine how each of those multiple different allocations will affect the cathode filament wear profiles of the multiple medical imaging scanners, and the optimizer engine may select that one of those multiple different allocations whose cathode filament wear profile best satisfies any suitable optimization criteria.

[0036] As a non-limiting example, the optimization criterion can be the minimization of the total intra-scanner filament wear difference exhibited by the plurality of digital scanner twins. In particular, as described above, each cathode filament wear model of the plurality of digital scanner twins can be considered to model, track, or otherwise output as a dependent variable the total amount of wear accumulated by the respective cathode filaments (e.g., the total amount of wear accumulated since the respective cathode filaments were new or installed). In various aspects, the intra-scanner cathode filament wear difference can be the absolute value of the difference between the total accumulated wear of any two cathode filaments belonging to the same medical imaging scanner as each other. In various instances, for each of the plurality of medical imaging scanners having two or more cathode filaments, at least one corresponding intra-scanner cathode filament wear difference can be calculated (e.g., the corresponding difference of cathode filaments for each unique pair of cathode filaments belonging to the same scanner as each other). Thus, for any given allocation of the set of medical imaging interventions among the plurality of medical imaging scanners, the optimizer engine can calculate various intra-scanner cathode filament wear differences. In various cases, the optimizer engine may total (e.g., via summing, via averaging, via multiplication) such various intra-scanner cathode filament wear differences, thereby generating a total intra-scanner cathode filament wear difference. Thus, for each of the plurality of different intervention allocations identified by the optimizer engine, the optimizer engine may calculate a corresponding total intra-scanner cathode filament wear difference. In various instances, that one of the plurality of different intervention allocations that has the lowest or minimized total intra-scanner cathode filament wear difference may be considered to balance cathode filament wear across the plurality of medical imaging scanners most sensibly or beneficially. In fact, such an allocation may be considered to achieve the lowest total or overall amount of cathode filament waste within each of the plurality of medical imaging scanners. In other words, such an allocation may be considered to reduce or minimize the situation where any single X-ray tube has reached the end of its useful life while one or more of its cathode filaments are still far from being exhausted. Such an allocation may also be considered to be the most convenient from a maintenance perspective. That is, such an allocation may be considered to reduce or minimize the situation where different X-ray tubes of any single medical imaging scanner reach the end of their useful life at completely different or staggered times.

[0037] As another non-limiting example, the optimization criterion can be the maximization of the total inter-scanner cathode filament wear difference exhibited by the plurality of digital scanner twins. In particular, as described above, each cathode filament wear model of the plurality of digital scanner twins can be considered to model, track, or otherwise output as a dependent variable the total amount of wear accumulated by the respective cathode filaments. In various aspects, the wear indicated by the cathode filament wear model of a single individual medical imaging scanner can be aggregated, thereby producing a scanner-by-scanner cathode filament wear total for the single medical imaging scanner. Thus, after simulating any given medical imaging intervention on the digital scanner twin of any given medical imaging scanner, the optimization engine can calculate the scanner-by-scanner cathode filament wear difference for the given medical imaging scanner. Thus, for any given allocation of the set of medical imaging interventions among the plurality of medical imaging scanners, the optimizer engine can calculate the corresponding scanner-by-scanner cathode filament wear total for each of the plurality of medical imaging scanners. Now, in various instances, the inter-scanner cathode filament wear difference can be the absolute value of the difference between the scanner-by-scanner cathode filament wear totals of any two medical imaging scanners. In various cases, the optimizer engine may calculate a corresponding inter-scanner cathode filament wear difference between each unique pair of medical imaging scanners of the plurality of medical imaging scanners, and the optimizer engine may total all such inter-scanner cathode filament wear differences, thereby producing a total inter-scanner cathode filament wear difference. Thus, for each intervention allocation in the plurality of different intervention allocations identified by the optimizer engine, the optimizer engine may calculate a corresponding total inter-scanner cathode filament wear difference. In various instances, that intervention allocation in the plurality of different intervention allocations that has the highest or maximized total inter-scanner cathode filament wear difference may be considered to most judiciously or beneficially distribute cathode filament wear across the plurality of medical imaging scanners. In fact, such an allocation may be considered to achieve the most evenly spaced amount of staggering between the scanner-by-scanner cathode filament wear totals of the plurality of medical imaging scanners. In other words, such an allocation may be considered to reduce or minimize the situation where multiple medical imaging scanners require maintenance or repair at the same time, thereby avoiding widespread scanner downtime.

[0038] As even another non-limiting example, the optimization criterion may be the minimization of the total intra-scan position filament wear difference exhibited by the plurality of digital scanner twins. In particular, as described above, for any given allocation of the set of medical imaging interventions among the plurality of medical imaging scanners, the optimizer engine may calculate a corresponding per-scanner cathode filament wear total for each of the plurality of medical imaging scanners. Now, in various instances, some of the medical imaging scanners may be located in physical locations with respect to one another (e.g., in the same hospital ward, in the same hospital building). In various cases, the intra-position cathode filament wear difference may be the absolute value of the difference between the per-scanner cathode filament wear totals of any two medical imaging scanners deployed at the same physical location with respect to one another. In various aspects, the optimizer engine may total the intra-position cathode filament wear differences across all of the plurality of medical imaging scanners, thereby generating a total intra-position cathode filament wear difference. Thus, for each of the plurality of different intervention allocations identified by the optimizer engine, the optimizer engine may calculate a corresponding total intra-position cathode filament wear difference. In various examples, that one of the plurality of different intervention allocations that has the lowest or minimized total intra-location cathode filament wear variance can be considered to most judiciously or beneficially distribute cathode filament wear across the plurality of medical imaging scanners. Indeed, such an allocation can be considered to conveniently synchronize cathode filament wear such that any medical imaging scanners located in the same physical location as one another are likely to require maintenance at the same or similar times. In other words, such an allocation can be considered to increase or maximize the instances where multiple medical imaging scanners located in the same physical location as one another are ready for maintenance or repair at the same time.

[0039] In other embodiments, the allocation component can facilitate such allocation determination by utilizing a deep learning neural network, rather than utilizing an optimizer engine. In particular, the allocation component can electronically store, maintain, control, or otherwise access the deep learning neural network. In various aspects, the deep learning neural network may have any suitable deep learning internal architecture. For example, the deep learning neural network may include any suitable number of layers of any suitable type (e.g., input layer, one or more hidden layers, output layer, any of which may be a convolutional layer, a dense layer, a nonlinear layer, a pooling layer, a batch normalization layer, a long short-term memory (LSTM) layer, or a padding layer). As another example, the deep learning neural network may include any suitable number of neurons in various layers (e.g., different layers may have the same or different numbers of neurons). As yet another example, the deep learning neural network may include any suitable activation function (e.g., softmax, sigmoid, hyperbolic tangent, rectified linear unit) in various neurons (e.g., different neurons may have the same or different activation functions). As yet another example, the deep learning neural network may include any suitable inter-neuron connection or inter-layer connection (e.g., forward connection, skip connection, recursive connection).

[0040] Regardless of its internal architecture, the deep learning neural network can be configured to determine how to distribute the input medical imaging interventions across the input medical imaging scanners. Thus, the allocation component can determine how to wisely or beneficially distribute the set of medical imaging interventions across multiple medical imaging scanners by executing the deep learning neural network.

[0041] More specifically, and as described above, each cathode filament wear model of the plurality of digital scanner twins may be considered to model, track, or otherwise output as a dependent variable the total amount of wear accumulated by the respective cathode filaments. In various cases, the total amount of accumulated wear of any given cathode filament prior to simulating or actually implementing the set of medical imaging interventions may be referred to as the current time wear state of the given cathode filament. Thus, in various aspects, all accumulated cathode filament wear currently or currently indicated by the plurality of digital scanner twins may be collectively referred to as a plurality of current time cathode filament wear states. In various instances, the allocation component may execute a deep learning neural network on both the plurality of current time cathode filament wear states and the set of medical imaging interventions, and such execution may enable the deep learning neural network to determine a wise or beneficial allocation of interventions (e.g., to determine an allocation that reduces or minimizes cathode filament waste or otherwise increases or maximizes scanner maintenance convenience). In particular, the allocation component may cascade the set of medical imaging interventions and the plurality of current time cathode filament wear states together, and the allocation component may feed the cascade to an input layer of the deep learning neural network. In various cases, the cascade may complete a forward pass through one or more hidden layers of the deep learning neural network. In various aspects, an output layer of a deep learning neural network can compute sensible or beneficial intervention allocations based on activation maps provided by one or more hidden layers of the deep learning neural network.

[0042] In order for the deep learning neural network to reliably or confidently determine such a wise or beneficial intervention allocation, the deep learning neural network should first be trained. In various aspects, as described herein, the deep learning neural network can be trained via any suitable training paradigm (e.g., supervised training using ground truth intervention allocations, unsupervised training in the absence of ground truth intervention allocations, reinforcement learning via reward or punishment strategies).

[0043] In any case, the allocation component may utilize multiple digital scanner twins in order to determine how the set of medical imaging interventions should be allocated among the multiple medical imaging scanners in order to reduce cathode filament waste or increase scanner serviceability.

[0044] In various embodiments, the result component of the computerized tool can initiate or facilitate any suitable electronic action based on the intervention allocation determined or selected by the allocation component. In some cases, the result component can electronically present the determined or selected intervention allocation on any suitable computer screen or monitor. In other cases, the result component can electronically send the determined or selected intervention allocation to any suitable computing device. In yet other cases, the result component can electronically instruct multiple medical imaging scanners to prepare to implement the determined or selected allocation (e.g., each scanner can be ordered to locally submit a scanning protocol for any intervention according to the determined or selected allocation).

[0045] Various embodiments described herein may be employed to solve problems that are highly technical in nature (e.g., to facilitate intelligent intervention allocation for medical imaging scanners) using hardware or software, which are not abstract and cannot be performed by humans as a set of mental actions. In addition, some of the processes performed may be performed by specialized computers (e.g., including digital twins that compute or analyze cathode filament wear models; deep learning neural networks with internal parameters such as convolution kernels or long short-term memory layers) for performing defined actions related to medical imaging scanners.

[0046] For example, such defined actions may include: accessing, by a device operably connected to a processor, a set of medical imaging interventions to be performed on multiple medical imaging scanners; determining, by the device and based on multiple digital scanner twins, a recommended allocation, each of the multiple digital scanner twins including one or more cathode filament wear models for a corresponding one of the multiple medical imaging scanners, the recommended allocation indicating how to allocate the set of medical imaging interventions among the multiple medical imaging scanners; and allocating, by the device, the set of medical imaging interventions among the multiple medical imaging scanners according to the recommended allocation, wherein the recommended allocation indicates that a first medical imaging intervention in the set of medical imaging interventions is to be assigned to a first medical imaging scanner among the multiple medical imaging scanners, and wherein the allocation includes instructing, by the device, the first medical imaging scanner to locally submit or prepare the first medical imaging intervention. In some cases, the device may simulate multiple different allocations of the set of medical imaging interventions between multiple medical imaging scanners on multiple digital scanner twins, and the recommended allocation may be the one of the multiple different allocations that minimizes or maximizes the optimization criterion (e.g., minimizing the total intra-scanner cathode filament wear difference indicated by the multiple digital scanner twins; maximizing the total inter-scanner cathode filament wear difference indicated by the multiple digital scanner twins; minimizing the total intra-location cathode filament wear difference indicated by the multiple digital scanner twins). In other cases, the device may determine the recommended allocation by executing a machine learning model (e.g., a deep learning neural network) on the set of medical imaging interventions and on the current time cathode filament wear state indicated by the multiple digital scanner twins.

[0047] Such defined actions are not manually performed by a human. In fact, neither a human brain nor a person with a pen and paper can electronically utilize a digital twin that models or simulates the cathode filament degradation experienced by a fleet of medical imaging scanners in conjunction with an optimizer engine or a deep learning neural network to identify how to best distribute a series of planned medical imaging interventions across a fleet of medical imaging scanners. In fact, the cathode filament is a concrete, tangible component of a medical imaging scanner, and the human brain simply cannot realize it in any way without a computer. In addition, the digital twin is an inherently computerized virtualization, and the human brain cannot meaningfully realize it in any way without a computer. In addition, the deep learning neural network and optimizer engine are inherently computerized constructs, and similarly, the human brain cannot meaningfully execute or train it in any way without a computer. In other words, even with the help of pen and paper, the human brain cannot simply simulate or analyze the cathode filament wear profile by using a digital scanner twin in conjunction with an optimizer engine or a deep learning neural network. Therefore, a computerized tool that utilizes a digital scanner twin that models accumulated cathode filament wear of a medical imaging scanner in conjunction with an optimizer engine or deep learning neural network to recommend or suggest how to distribute medical imaging interventions among medical imaging scanners is inherently computerized and cannot be implemented in any reasonable, practical, or rational manner without a computer.

[0048] In addition, various embodiments described herein can integrate various teachings related to intelligent intervention allocation for medical imaging scanners into practical applications. As described above, the frequency or intensity with which a medical imaging scanner is used can affect the wear experienced by the cathode filaments of the X-ray tubes of the medical imaging scanner. If medical imaging interventions are performed on a medical imaging scanner in a random, unplanned, or otherwise unthought-out order or sequence, the accumulated cathode filament wear of the medical imaging scanner can result in undesirable waste or maintenance inconvenience.

[0049] For example, an X-ray tube of a medical imaging scanner may reach the end of its useful life when at least one of its cathode filaments is exhausted (e.g., when at least one of its cathode filaments reaches a threshold level of accumulated wear). However, if one or more other cathode filaments of the X-ray tube have not yet been exhausted (e.g., have not yet reached a threshold level of accumulated wear), then replacement of the X-ray tube may be considered necessary but wasteful (e.g., necessary to maintain proper performance of the X-ray tube, but wasteful because one or more other cathode filaments are discarded despite not being exhausted). For example, assume that an X-ray tube has cathode filament A and cathode filament B. Furthermore, assume that cathode filament A has been exhausted, and further assume that cathode filament B will still accumulate a significant amount of wear before exhaustion. In this case, exhaustion of cathode filament A may require replacement of the X-ray tube. But replacement of the X-ray tube may be considered to waste a significant unexhausted portion of cathode filament B. In other words, if cathode filament B is also exhausted or at least nearly exhausted when cathode filament A is exhausted, then the waste will be much less.

[0050] As another example, a medical imaging scanner may have multiple X-ray tubes. If such multiple X-ray tubes reach the end of their useful lives at significantly different or staggered times, maintenance of the medical imaging scanner may become inefficient or cumbersome. For example, assume that the medical imaging scanner has an X-ray tube C and an X-ray tube D. Furthermore, assume that X-ray tube C has reached the end of its useful life, while X-ray tube D still has a significant amount of useful life remaining. In this case, a first maintenance visit may be made to the medical imaging scanner to replace X-ray tube C. However, after such replacement, it may be the case that X-ray tube D eventually reaches the end of its useful life, while the replaced X-ray tube C still has a significant amount of its useful life remaining. In this case, a second maintenance visit may be made to the medical imaging scanner to replace X-ray tube D. In this way, the number of maintenance visits to the medical imaging scanner may be considered to be unnecessarily increased. In other words, if when X-ray tube C reaches the end of its useful life, X-ray tube D also reaches the end of its useful life or is at least closer to the end of its useful life, so that X-ray tube C and X-ray tube D can be replaced during the same maintenance visit, it will be much less time-consuming and more convenient.

[0051] As even another example, there may be a fleet of medical imaging scanners. If all or many of the medical imaging scanners of such a fleet of medical imaging scanners require maintenance at the same time as one another, many of the medical imaging scanners in the fleet will be shut down or offline at the same time and unable to perform medical imaging interventions. For example, assume that the fleet only includes medical imaging scanner E and medical imaging scanner F. Furthermore, assume that medical imaging scanner E requires a maintenance visit (e.g., replacement of an X-ray tube) at the same time that medical imaging scanner F requires a maintenance visit. In this case, medical imaging scanner E and medical imaging scanner F may be considered to be shut down or offline at the same time, meaning that during such downtime, the fleet may not be able to handle any required or desired medical imaging interventions. In contrast, it would be more beneficial if medical imaging scanner E and medical imaging scanner F required maintenance at different or staggered times. In this way, situations where no scanners are online may be avoided or reduced.

[0052] As yet another example, a medical imaging scanner fleet may include some scanners that are physically located at the same place as each other. If such co-location scanners need to be repaired at significantly different or staggered times, the maintenance of such co-location scanners may become inefficient or cumbersome. For example, it is assumed that medical imaging scanner G and medical imaging scanner H are located in the same hospital building as each other. In addition, it is assumed that medical imaging scanner G needs maintenance (e.g., replacement of an X-ray tube), and medical imaging scanner H still has a significant amount of time or use remaining before maintenance is required. In this case, a first maintenance visit to the hospital building may be made to repair medical imaging scanner G. However, after such maintenance, it may be the case that medical imaging scanner H eventually needs maintenance, and after the service, medical imaging scanner G has a significant amount of time or use remaining before maintenance is required again. In this case, a second maintenance visit to the hospital building may be made to repair medical imaging scanner H. In this way, the number of maintenance visits to the hospital building may be considered to be unnecessarily increased. In other words, if when medical imaging scanner G needs maintenance, medical imaging scanner H also needs maintenance or is at least closer to needing maintenance, so that medical imaging scanner G and medical imaging scanner H can be repaired during the same maintenance visit, it will be much less time-consuming or more convenient.

[0053] Therefore, the manner in which a medical imaging scanner accumulates cathode filament wear or degradation may result in undesirable waste or maintenance inconvenience.

[0054] Various embodiments described herein may solve or improve such technical problems. Specifically, various embodiments described herein may involve determining how medical imaging interventions should be allocated between a medical imaging scanner fleet by utilizing digital scanner twins corresponding to the medical imaging scanner fleet, respectively. In various aspects, each digital scanner twin may perform computational or analytical modeling of cathode filament wear accumulated by the corresponding medical imaging scanner. In various instances, such accumulated cathode filament wear may be utilized by an optimizer engine or a deep learning neural network to determine how to intelligently, wisely, or otherwise beneficially allocate medical imaging interventions between a medical imaging scanner fleet. In various aspects, such intelligent, wise, or beneficial allocation may be configured to minimize or maximize any suitable optimization criteria. In some cases, such an optimization criterion may be the minimization of differences in cathode filament wear within a scanner; this may be considered to reduce or minimize the situation where unexhausted cathode filaments are wasted, or may be considered to reduce or minimize the situation where multiple X-ray tubes of a single medical imaging scanner reach the end of their service life in a staggered manner. In other cases, such optimization criteria may be maximization of differences in cathode filament wear between scanners; this may be considered to reduce or minimize situations where many medical imaging scanners of a medical imaging scanner fleet require maintenance at the same time as one another (e.g., reducing or minimizing large-scale or widespread scanner downtime). In even other cases, such optimization criteria may be minimization of differences in cathode filament wear within a location; this may be considered to reduce or minimize situations where multiple medical imaging scanners located at the same physical location as one another require maintenance in a staggered manner. Thus, the various embodiments described herein may be considered to intelligently or skillfully determine which specific medical imaging interventions to perform on which specific medical imaging scanners in order to reduce cathode filament waste or increase the ease of maintenance of the medical imaging scanners. For at least these reasons, the various embodiments described herein undoubtedly constitute concrete and tangible technical improvements or technical benefits in the field of medical imaging scanners, and therefore such embodiments clearly qualify as useful and practical applications of computers.

[0055] Additionally, various embodiments described herein may control real-world tangible devices based on the disclosed teachings. For example, various embodiments described herein may electronically command a real-world medical imaging scanner (e.g., an X-ray scanner, a CT scanner, a PET scanner) to submit or perform a real-world medical imaging intervention on a real-world medical patient (e.g., a human, an animal, or other).

[0056] It should be understood that the drawings and description herein provide non-limiting examples of various embodiments and are not necessarily drawn to scale.

[0057] Figure 1A block diagram of an example non-limiting system 100 that can facilitate intelligent intervention allocation for medical imaging scanners according to one or more embodiments described herein is illustrated. As shown, a scanner wear system 102 can be electronically integrated with a plurality of medical imaging scanners 104 and a set of medical imaging interventions 106 via any suitable wired or wireless electronic connection.

[0058] In various aspects, the plurality of medical imaging scanners 104 may include any suitable number of any suitable type of medical imaging scanners. In various examples, the set of medical imaging interventions 106 may include any suitable number of medical imaging interventions, each of which may be implemented, performed, or otherwise performed on or by any one of the plurality of medical imaging scanners 104. Figures 2 to 3 Various non-limiting aspects have been described.

[0059] Figure 2 An example non-limiting block diagram 200 of a plurality of medical imaging scanners 104 is illustrated in accordance with one or more embodiments described herein.

[0060] In various aspects, as shown, the plurality of medical imaging scanners 104 may include n scanners, where n is any suitable positive integer: medical imaging scanner 104(1) to medical imaging scanner 104(n). In various instances, each of the plurality of medical imaging scanners 104 may be any suitable medical image capture modality or equipment that utilizes X-ray tube radiation to generate medical images. As a non-limiting example, each of the plurality of medical imaging scanners 104 may be an X-ray scanner configured to capture or generate an X-ray scan image of any suitable anatomical structure (e.g., an organ, tissue, body cavity, body fluid) of any suitable medical patient (e.g., a human, an animal, or other). As another non-limiting example, each of the plurality of medical imaging scanners 104 may be a CT scanner configured to capture or generate a CT scan image of any suitable anatomical structure of any suitable medical patient. As even another non-limiting example, each of the plurality of medical imaging scanners 104 may be a PET scanner configured to capture or generate a PET scan image of any suitable anatomical structure of any suitable medical patient. As yet another non-limiting example, each of the plurality of medical imaging scanners 104 may be a nuclear medicine (NM) scanner configured to capture or generate NM scan images of any suitable anatomical structure of any suitable medical patient.

[0061] In any case, each of the plurality of medical imaging scanners 104 may include any suitable number of X-ray tubes. For example, the medical imaging scanner 104(1) may include m X-ray tubes, where m is any suitable positive integer: X-ray tube 104(1)(1) through X-ray tube 104(1)(m). As another example, the medical imaging scanner 104(n) may include m X-ray tubes: X-ray tube 104(n)(1) through X-ray tube 104(n)(m). In various aspects, any of such X-ray tubes may have any suitable type, construction, or architecture. As some non-limiting examples, any of such X-ray tubes may be: a fixed anode X-ray tube; a rotating anode X-ray tube; an anode grounded X-ray tube; a cathode grounded X-ray tube; a bipolar X-ray tube; an X-ray diffraction (XRD) X-ray tube; an X-ray fluorescence (XRF) X-ray tube; a beryllium window X-ray tube; a ceramic X-ray tube; a metal ceramic X-ray tube; a non-destructive testing (NDT) X-ray tube; a microfocus X-ray tube; a transmission X-ray tube; or a reflection X-ray tube.

[0062] although Figure 2 Each of the plurality of medical imaging scanners 104 is shown as including m X-ray tubes, but this is merely a non-limiting example for ease of explanation and illustration. In various embodiments, it should be understood that different medical imaging scanners in the plurality of medical imaging scanners 104 may include the same or different numbers of X-ray tubes as one another. Likewise, in various embodiments, it should be understood that different medical imaging scanners in the plurality of medical imaging scanners 104 may include the same or different types of X-ray tubes as one another.

[0063] In various aspects, each X-ray tube of each of the plurality of medical imaging scanners 104 may include any suitable number of cathode filaments of any suitable type. As a non-limiting example, X-ray tube 104(1)(1) may include p filaments, where p is any suitable positive integer: cathode filament 104(1)(1)(1) to cathode filament 104(1)(1)(p). As another non-limiting example, X-ray tube 104(1)(m) may include p filaments: cathode filament 104(1)(m)(1) to cathode filament 104(1)(m)(p). As even another non-limiting example, X-ray tube 104(n)(1) may include p filaments: cathode filament 104(n)(1)(1) to cathode filament 104(n)(1)(p). As yet another non-limiting example, X-ray tube 104(n)(m) may include p filaments: cathode filament 104(n)(m)(1) to cathode filament 104(n)(m)(p). In various examples, any of such cathode filaments may have any suitable type, construction, or physical properties. As some non-limiting examples, any of such cathode filaments may have: any suitable material composition (e.g., tungsten); any suitable coil length; any suitable wire thickness or wire diameter; any suitable resistance; any suitable thermal conductivity; any suitable heat capacity; or any suitable specific emissivity.

[0064] although Figure 2 Each X-ray tube of the plurality of medical imaging scanners 104 is shown as including p cathode filaments, but this is a non-limiting example for ease of explanation and illustration only. In various embodiments, it should be understood that different X-ray tubes of the plurality of medical imaging scanners 104 may include the same or different numbers of cathode filaments as each other. Likewise, in various embodiments, it should be understood that different X-ray tubes of the plurality of medical imaging scanners 104 may include the same or different types of cathode filaments as each other (e.g., may have the same or different physical attributes or properties as each other).

[0065] In various embodiments, each of the plurality of medical imaging scanners 104 may be located, positioned, or deployed at any suitable physical location. As a non-limiting example, the medical imaging scanner 104(1) may be located, positioned, or deployed at a first physical location (e.g., in a first hospital room; on a first hospital floor; or in a first hospital building), and the medical imaging scanner 104(n) may be located, positioned, or deployed at an nth physical location (e.g., in an nth hospital room; on an nth hospital floor; or in an nth hospital building). Note that in various instances, different ones of the plurality of medical imaging scanners 104 may be located, positioned, or deployed in or at the same physical location or physical locations as one another or different physical locations.

[0066] Now, consider Figure 3 . Figure 3 An example non-limiting block diagram 300 of the set of medical imaging interventions 106 is illustrated in accordance with one or more embodiments described herein.

[0067] In various embodiments, the set of medical imaging interventions 106 can include s interventions, where s is any suitable positive integer: medical imaging intervention 106(1) through medical imaging intervention 106(s). In various aspects, each medical imaging intervention in the set of medical imaging interventions 106 can be considered to be a respective scanning protocol requested or ordered for or for a respective medical patient.

[0068] As a non-limiting example, the medical imaging intervention 106(1) may include a scan protocol 106(1)(1) and patient metadata 106(1)(2). In various instances, the scan protocol 106(1)(1) may be any suitable electronic information (e.g., may be one or more scalars, one or more vectors, one or more matrices, one or more tensors, one or more strings, or any suitable combination thereof) that specifies, indicates, communicates, represents, or otherwise defines a scanning or imaging routine or procedure that may be implemented by any of the plurality of medical imaging scanners 104. As some non-limiting examples, the scan protocol 106(1)(1) may specify: a gantry speed or gantry range to be implemented by the medical imaging scanner; a table height to be implemented by the medical imaging scanner; a patient position or orientation on a table of the medical imaging scanner; or a tube current or tube voltage to be implemented by the medical imaging scanner. In various embodiments, the patient metadata 106(1)(2) may be any suitable electronic information (e.g., may be one or more scalars, one or more vectors, one or more matrices, one or more tensors, one or more strings, or any suitable combination thereof) that specifies, indicates, communicates, or otherwise represents physical characteristics of any medical patient for whom the scanning protocol 106(1)(1) is desired to be performed. As some non-limiting examples, the patient metadata 106(1)(2) may indicate: the age of the medical patient; the height of the medical patient; the gender of the medical patient; the mass, weight, or body mass index of the medical patient; the pathology of the medical patient; the medications of the medical patient; the prosthesis or implant of the medical patient. In other words, the patient metadata 106(1)(2) may be considered to be medical information related to a particular medical patient for whom a particular medical image is desired, and the scanning protocol 106(1)(1) may be considered to be scanner-implementable instructions according to which the particular medical image should be generated.

[0069] As another non-limiting example, the medical imaging intervention 106(s) may include a scan protocol 106(s)(1) and patient metadata 106(s)(2). In various instances, the scan protocol 106(s)(1) may be any suitable electronic information (e.g., may be one or more scalars, one or more vectors, one or more matrices, one or more tensors, one or more strings, or any suitable combination thereof) that specifies, indicates, communicates, represents, or otherwise defines a scanning or imaging routine or procedure that may be implemented by any of the plurality of medical imaging scanners 104. In various instances, the patient metadata 106(s)(2) may be any suitable electronic information (e.g., may be one or more scalars, one or more vectors, one or more matrices, one or more tensors, one or more strings, or any suitable combination thereof) that specifies, indicates, communicates, or otherwise represents the physical characteristics of any medical patient for whom the scan protocol 106(s)(1) is desired to be performed. In other words, the patient metadata 106(s)(2) can be considered as medical information relating to a particular medical patient for whom a particular medical image is desired, and the scanning protocol 106(s)(1) can be considered as scanner-implementable instructions according to which the particular medical image should be generated.

[0070] Back to Figure 1 , it may be desirable to determine how the set of medical imaging interventions 106 should be distributed among the plurality of medical imaging scanners 104. In other words, it may be desirable to determine which particular medical imaging interventions in the set of medical imaging interventions 106 should be performed or implemented by which particular medical imaging scanners in the plurality of medical imaging scanners 104. As described herein, the scanner wear system 102 can facilitate such a determination.

[0071] In various embodiments, the scanner wear system 102 may include a processor 108 (e.g., a computer processing unit, a microprocessor), and a non-transitory computer readable memory 110 operatively or operatively or communicatively connected or coupled to the processor 108. The non-transitory computer readable memory 110 may store computer executable instructions that, when executed by the processor 108, may cause the processor 108 or other components of the scanner wear system 102 (e.g., an access component 112, an assignment component 114, a result component 116) to perform one or more actions. In various embodiments, the non-transitory computer readable memory 110 may store computer executable components (e.g., an access component 112, an assignment component 114, a result component 116), and the processor 108 may execute the computer executable components.

[0072] In various embodiments, the scanner wear system 102 may include an access component 112. In various aspects, the access component 112 may electronically access the plurality of medical imaging scanners 104 or the set of medical imaging interventions 106. For example, the access component 112 may electronically communicate with the plurality of medical imaging scanners 104 such that the access component 112 may electronically send data to or electronically receive data from any of the plurality of medical imaging scanners 104. As another example, the access component 112 may electronically retrieve, electronically receive, or otherwise electronically obtain the set of medical imaging interventions 106 from any suitable centralized or decentralized data structure or from any suitable centralized or decentralized computing device (e.g., from any computing device used to electronically create or electronically invoke the set of medical imaging interventions 106). In any case, the access component 112 can electronically access the plurality of medical imaging scanners 104 or the set of medical imaging interventions 106 such that other components of the scanner wear system 102 can electronically interact with the plurality of medical imaging scanners 104 or the set of medical imaging interventions 106 (e.g., via an agent).

[0073] In various embodiments, the scanner wear system 102 can include an allocation component 114. In various aspects, the allocation component 114 can determine a recommended manner to allocate the set of medical imaging interventions 106 among the plurality of medical imaging scanners 104 by utilizing a plurality of digital scanner twins, as described herein.

[0074] In various embodiments, scanner wear system 102 can include a results component 116. In various examples, results component 116 can present or send any suitable electronic notification indicative of an allocation determination generated by allocation component 114, as described herein.

[0075] Figure 4 A block diagram of an example non-limiting system 400 is illustrated, the system including multiple digital scanner twins and recommended intervention assignments that can facilitate intelligent intervention assignments for a medical imaging scanner, according to one or more embodiments described herein. As shown, in some cases, the system 400 can include the same components as the system 100, and can also include multiple digital scanner twins 402 and recommended intervention assignments 404.

[0076] In various embodiments, the allocation component 114 may electronically store, electronically maintain, electronically control, or otherwise electronically access the plurality of digital scanner twins 402. In various aspects, the allocation component 114 may electronically utilize or use the plurality of digital scanner twins 402 in order to determine a recommended intervention allocation 404. In various instances, the recommended intervention allocation 404 may be any suitable electronic data (e.g., may be one or more scalars, one or more vectors, one or more matrices, one or more tensors, one or more strings, or any suitable combination thereof) that specifies, indicates, communicates, or otherwise represents a particular manner in which the set of medical imaging interventions 106 are allocated or distributed among the plurality of medical imaging scanners 104. In particular, the recommended intervention allocation 404 may be considered to assign a particular medical imaging intervention in the set of medical imaging interventions 106 to a particular medical imaging scanner in the plurality of medical imaging scanners 104, wherein such a particular assignment helps reduce cathode filament waste associated with the plurality of medical imaging scanners 104 or otherwise helps increase ease of maintenance associated with the plurality of medical imaging scanners 104. About Figures 5 to 9 Non-limiting aspects are described.

[0077] Figure 5 An example non-limiting block diagram 500 of a plurality of digital scanner twins 402 is illustrated in accordance with one or more embodiments described herein.

[0078] In various embodiments, the plurality of digital scanner twins 402 may correspond respectively (e.g., in a one-to-one manner) to the plurality of medical imaging scanners 104. Thus, since the plurality of medical imaging scanners 104 may include n scanners, the plurality of digital scanner twins 402 may include n twins: digital scanner twin 402(1) to digital scanner twin 402(n). In various aspects, each digital scanner twin in the plurality of digital scanner twins 402 may be considered a virtualization that electronically models or predicts wear accumulated by a cathode filament of a corresponding one of the plurality of medical imaging scanners 104.

[0079] As a non-limiting example, the digital scanner twin 402(1) may correspond to the medical imaging scanner 104(1). Thus, the digital scanner twin 402(1) may be considered a virtualized version of the medical imaging scanner 104(1) that may model or predict how the cathode filaments of the medical imaging scanner 104(1) accumulate wear over time. In particular, the digital scanner twin 402(1) may include a set of cathode filament wear models 402(1)(1). In various cases, the set of cathode filament wear models 402(1)(1) may respectively correspond to the cathode filaments of the x-ray tube 104(1)(1). Thus, since the x-ray tube 104(1)(1) has p cathode filaments, the set of cathode filament wear models 402(1)(1) may also include p models: cathode filament wear model 402(1)(1)(1) to cathode filament wear model 402(1)(1)(p). In various aspects, each cathode filament wear model in the set of cathode filament wear models 402(1)(1) can be any suitable computational, numerical or mathematical model capable of simulating, quantifying or otherwise estimating the wear or degradation accumulated by the corresponding cathode filament of the X-ray tube 104(1)(1).

[0080] For example, the cathode filament wear model 402(1)(1)(1) may correspond to the cathode filament 104(1)(1)(1). Thus, the cathode filament wear model 402(1)(1)(1) may computationally, numerically, or mathematically simulate how wear, degradation, corrosion, or damage develops in the cathode filament 104(1)(1)(1). In some cases, the cathode filament wear model 402(1)(1)(1) may include or otherwise be based on any suitable number of any suitable type of physical equations associated with the cathode filament 104(1)(1)(1). Non-limiting examples of such physical equations may include: energy balance differential or non-differential equations; mass continuity differential or non-differential equations; heat transfer differential or non-differential equations; charge conservation differential or non-differential equations; or corrosion damage differential or non-differential equations.

[0081] In various aspects, the cathode filament wear model 402(1)(1)(1) may include or utilize as a constant any suitable physical property, attribute, or characteristic of the cathode filament 104(1)(1)(1). Non-limiting examples of such physical properties, attributes, or characteristics may include: material composition of the cathode filament 104(1)(1)(1); coil length of the cathode filament 104(1)(1)(1); thickness, diameter, or radius of the cathode filament 104(1)(1)(1); resistance of the cathode filament 104(1)(1)(1); thermal conductivity of the cathode filament 104(1)(1)(1); heat capacity of the cathode filament 104(1)(1)(1); or specific emissivity of the cathode filament 104(1)(1)(1).

[0082] In various instances, the cathode filament wear model 402(1)(1)(1) may include or utilize as independent variables any suitable operational input parameters that describe or define how the cathode filament 104(1)(1)(1) has been used, is being used, or may be used. Non-limiting examples of such operational input parameters may include: a current that has been applied, is being applied, or may be applied to the cathode filament 104((1)(1)(1); a time span over which the current has been applied, is being applied, or may be applied to the cathode filament 104(1)(1)(1); a voltage that has been applied, is being applied, or may be applied to the cathode filament 104(1)(1)(1); a time span over which the voltage has been applied, is being applied, or may be applied to the cathode filament 104(1)(1)(1); an ambient temperature that has been applied, is being applied, or may be applied to the cathode filament 104(1)(1)(1); or an ambient temperature that has been applied, is being applied, or may be applied to the cathode filament 104(1)(1)(1); The time span over which temperature has been applied, is being applied, or may be applied to the cathode filament 104(1)(1)(1). In various cases, the cathode filament wear model 402(1)(1)(1) may treat such operational input parameters as independent variables, and the cathode filament wear model 402(1)(1)(1) may calculate or account for the total amount of wear or degree of wear that has or will accumulate by the cathode filament 104(1)(1)(1) since the cathode filament 104(1)(1)(1) was new (e.g., since the x-ray tube 104(1)(1) was recently installed in the medical imaging scanner 104(1)) as a dependent variable.

[0083] It is noted that the current, voltage, ambient temperature, or duration of exposure to such current, voltage, or temperature may vary with any medical imaging intervention performed using the cathode filament 104(1)(1)(1). Indeed, different scanning protocols applied to different medical patients may require or require that the cathode filament 104(1)(1)(1) be subjected to different currents, voltages, or temperatures for different time spans (e.g., a head scan protocol may use a different current or voltage than a foot scan protocol; a larger or heavier patient may require a higher current or voltage than a smaller or lighter patient; a pregnant patient may require a lower current or voltage than a non-pregnant patient; a patient with a pacemaker may require a lower current or voltage than a patient without a pacemaker).

[0084] Thus, in various aspects, the cathode filament wear model 402(1)(1)(1) can be considered to be indicative of how much wear the cathode filament 104(1)(1)(1) has actually experienced at the present or current time (e.g., due to actual prior use of the medical imaging scanner 104(1)), and the cathode filament wear model 402(1)(1)(1) can be considered to be capable of simulating, estimating, or predicting how much wear the cathode filament 104(1)(1)(1) would or would have accumulated if any given medical imaging intervention were to be performed by the medical imaging scanner 104(1).

[0085] Similarly, cathode filament wear model 402(1)(1)(p) may correspond to cathode filament 104(1)(1)(p). Thus, cathode filament wear model 402(1)(1)(p) may computationally, numerically, or mathematically simulate, estimate, or predict (e.g., based on any suitable differentiable or non-differential equation that treats any suitable physical property of cathode filament 104(1)(1)(p) as a constant or any suitable current, voltage, temperature, or time of use experienced by cathode filament 104(1)(1)(p) as an independent variable) how wear will gradually develop or accumulate in cathode filament 104(1)(1)(p). Thus, in various aspects, the cathode filament wear model 402(1)(1)(p) can be considered to be indicative of how much wear the cathode filament 104(1)(1)(p) has actually experienced at the present or current time, and the cathode filament wear model 402(1)(1)(p) can be considered to be capable of simulating, estimating, or predicting how much wear the cathode filament 104(1)(1)(p) will or will have accumulated if any given medical imaging intervention were to be performed by the medical imaging scanner 104(1).

[0086] In various embodiments, the digital scanner twin 402(1) may also include a set of cathode filament wear models 402(1)(m). In various cases, the set of cathode filament wear models 402(1)(m) may correspond to the cathode filaments of the X-ray tube 104(1)(m), respectively. Therefore, since the X-ray tube 104(1)(m) has p cathode filaments, the set of cathode filament wear models 402(1)(m) may also include p models: cathode filament wear model 402(1)(m)(1) to cathode filament wear model 402(1)(m)(p). In various aspects, each cathode filament wear model in the set of cathode filament wear models 402(1)(m) can be any suitable computational, numerical or mathematical model capable of simulating, quantifying or otherwise estimating the wear accumulated by the corresponding cathode filament of the X-ray tube 104(1)(m).

[0087] For example, cathode filament wear model 402(1)(m)(1) may correspond to cathode filament 104(1)(m)(1). Thus, cathode filament wear model 402(1)(m)(1) may be considered to be indicative of how much wear cathode filament 104(1)(m)(1) has actually experienced at the present or current time, and cathode filament wear model 402(1)(m)(1) may be considered to be able to simulate, estimate, or predict how much wear cathode filament 104(1)(m)(1) would or would have accumulated if any given medical imaging intervention were to be performed by medical imaging scanner 104(1).

[0088] Likewise, the cathode filament wear model 402(1)(m)(p) may correspond to the cathode filament 104(1)(m)(p). Thus, the cathode filament wear model 402(1)(m)(p) may be considered to be indicative of how much wear the cathode filament 104(1)(m)(p) has actually experienced at the present or current time, and the cathode filament wear model 402(1)(m)(p) may be considered to be capable of simulating, estimating, or predicting how much wear the cathode filament 104(1)(m)(p) would or would have accumulated if any given medical imaging intervention were to be performed by the medical imaging scanner 104(1).

[0089] As another non-limiting example, the digital scanner twin 402(n) may correspond to the medical imaging scanner 104(n). Thus, the digital scanner twin 402(n) may be considered a virtualized version of the medical imaging scanner 104(n) that may model or predict how the cathode filaments of the medical imaging scanner 104(n) accumulate wear, degradation, damage, or corrosion over time. In particular, the digital scanner twin 402(n) may include a set of cathode filament wear models 402(n)(1). In various cases, the set of cathode filament wear models 402(n)(1) may respectively correspond to the cathode filaments of the x-ray tube 104(n)(1). Thus, since the x-ray tube 104(n)(1) has p cathode filaments, the set of cathode filament wear models 402(n)(1) may also include p models: cathode filament wear model 402(n)(1)(1) to cathode filament wear model 402(n)(1)(p). In various aspects, each cathode filament wear model in the set of cathode filament wear models 402(n)(1) can be any suitable computational, numerical, or mathematical model capable of simulating, quantifying, or otherwise estimating the wear accumulated by the corresponding cathode filament of the X-ray tube 104(n)(1).

[0090] For example, cathode filament wear model 402(n)(1)(1) may correspond to cathode filament 104(n)(1)(1). Thus, cathode filament wear model 402(n)(1)(1) may be considered to be indicative of how much wear cathode filament 104(n)(1)(1) has actually experienced at the present or current time, and cathode filament wear model 402(n)(1)(1) may be considered to be able to simulate, estimate, or predict how much wear cathode filament 104(n)(1)(1) will or will have accumulated if any given medical imaging intervention were to be performed by medical imaging scanner 104(n).

[0091] Similarly, the cathode filament wear model 402(n)(1)(p) may correspond to the cathode filament 104(n)(1)(p). Thus, the cathode filament wear model 402(n)(1)(p) may be considered to be indicative of how much wear the cathode filament 104(n)(1)(p) has actually experienced at the present or current time, and the cathode filament wear model 402(n)(1)(p) may be considered to be able to simulate, estimate, or predict how much wear the cathode filament 104(n)(1)(p) will or will have accumulated if any given medical imaging intervention were to be performed by the medical imaging scanner 104(n).

[0092] In various embodiments, the digital scanner twin 402(n) may also include a set of cathode filament wear models 402(n)(m). In various cases, the set of cathode filament wear models 402(n)(m) may respectively correspond to the cathode filaments of the X-ray tube 104(n)(m). Therefore, since the X-ray tube 104(n)(m) has p cathode filaments, the set of cathode filament wear models 402(n)(m) may also include p models: cathode filament wear model 402(n)(m)(1) to cathode filament wear model 402(n)(m)(p). In various aspects, each cathode filament wear model in the set of cathode filament wear models 402(n)(m) can be any suitable computational, numerical, or mathematical model capable of simulating, quantifying, or otherwise estimating the wear accumulated by the corresponding cathode filament of the X-ray tube 104(n)(m).

[0093] For example, the cathode filament wear model 402(n)(m)(1) may correspond to the cathode filament 104(n)(m)(1). Thus, the cathode filament wear model 402(n)(m)(1) may be considered to be indicative of how much wear the cathode filament 104(n)(m)(1) has actually experienced at the present or current time, and the cathode filament wear model 402(n)(m)(1) may be considered to be able to simulate, estimate, or predict how much wear the cathode filament 104(n)(m)(1) will or will have accumulated if any given medical imaging intervention is to be performed by the medical imaging scanner 104(n).

[0094] Likewise, the cathode filament wear model 402(n)(m)(p) may correspond to the cathode filament 104(n)(m)(p). Thus, the cathode filament wear model 402(n)(m)(p) may be considered to indicate how much wear the cathode filament 104(n)(m)(p) has actually experienced at the present or current time, and the cathode filament wear model 402(n)(m)(p) may be considered to be able to simulate, estimate, or predict how much wear the cathode filament 104(n)(m)(p) will or has accumulated if any given medical imaging intervention were to be performed by the medical imaging scanner 104(n).

[0095] In any case, the plurality of digital scanner twins 402 can be considered to model or simulate the accumulated cathode filament wear of the plurality of medical imaging scanners 104, respectively, and the allocation component 114 can utilize the plurality of digital scanner twins 402 to determine the recommended intervention allocation 404. In some embodiments, the allocation component 114 can facilitate such determination via an optimizer engine, such as with respect to Figure 6 to Figure 7 In other embodiments, the allocation component 114 may alternatively facilitate such determination via a deep learning neural network, such as with respect to Figures 8 to 9 described.

[0096] Figure 6 to Figure 7 An example non-limiting block diagram showing how an optimizer engine may be utilized to generate a recommended intervention allocation in accordance with one or more embodiments described herein is illustrated.

[0097] First, consider Figure 6 In various embodiments, the allocation component 114 may electronically store, electronically maintain, electronically control, and / or otherwise electronically access the optimizer engine 602. In various aspects, the optimizer engine 602 may be any suitable optimization platform or optimization software. As a non-limiting example, the optimizer engine 602 may be As another non-limiting example, the optimizer engine 602 may be Optimizer. In any case, the optimizer engine 602 may electronically adjust or manipulate operational input parameters (e.g., applied current values; applied voltage values; applied ambient temperature values; applied time of use values) of the plurality of digital scanner twins 402 in order to simulate how various different intervention allocations will affect the cumulative cathode filament wear profiles of the plurality of medical imaging scanners 104.

[0098] More specifically, the optimizer engine 602 can electronically identify a set of possible intervention allocations 606. In various aspects, the set of possible intervention allocations 606 can include t allocations, where t is any suitable positive integer: possible intervention allocation 606(1) to possible intervention allocation 606(t). In various instances, each intervention allocation in the set of possible intervention allocations 606 can be a unique or different way of allocating or distributing the set of medical imaging interventions 106 among the plurality of medical imaging scanners 104 (e.g., can be a unique or different determination of which particular medical imaging interventions in the set of medical imaging interventions 106 should be performed or implemented by particular medical imaging scanners in the plurality of medical imaging scanners 104). Note that in some cases, the possible intervention allocations can assign more than one medical imaging intervention to any given medical imaging scanner. Conversely, note that in some cases, the possible intervention allocations can assign zero medical imaging interventions to any given medical imaging scanner. In various aspects, the set of possible intervention allocations 606 can encompass or otherwise include all possible permutations or combinations of intervention-to-scanner assignments. However, in other aspects, the set of possible intervention assignments 606 may include fewer than all possible permutations or combinations of intervention-to-scanner assignments.

[0099] In any case, the optimizer engine 602 may simulate each intervention allocation in the set of possible intervention allocations 606 on multiple digital scanner twins 402 in order to determine how each intervention allocation in the set of possible intervention allocations 606 will affect the cumulative cathode filament wear of multiple medical imaging scanners 104. In various aspects, the optimizer engine 602 may then select that intervention allocation in the set of possible intervention allocations 606 that best satisfies the optimization criterion 604 as the recommended intervention allocation 404. In some cases, the optimization criterion 604 may be the minimization of the total intra-scanner cathode filament wear difference exhibited by multiple digital scanner twins 402 after simulation. In other cases, the optimization criterion 604 may be the maximization of the total inter-scanner cathode filament wear difference exhibited by multiple digital scanner twins 402 after simulation. In still other cases, the optimization criterion 604 may be the minimization of the overall location cathode filament wear difference exhibited by multiple digital scanner twins 402 after simulation. For non-limiting illustration, consider Figure 7 .

[0100] Figure 7 Various different quantities that may be represented by, output by, or derived from the plurality of digital scanner twins 402 are illustrated.

[0101] In various aspects, there can be a cathode filament wear model 702. In various instances, the cathode filament wear model 702 can be any cathode filament wear model in the plurality of digital scanner twins 402 (e.g., any of 402(1)(1); any of 402(1)(m); any of 402(n)(1); any of 402(n)(m)). Thus, the cathode filament wear model 702 can be considered to correspond to a given cathode filament for a given medical imaging scanner (e.g., any of the medical imaging scanners 104).

[0102] In various cases, the cathode filament wear model 702 may indicate a present time wear state of a given cathode filament before its operational input parameters are manipulated or adjusted by the optimizer engine 602. In various cases, the present time wear state may be any suitable electronic data (e.g., one or more scalars, one or more vectors, one or more matrices, one or more tensors, or any suitable combination thereof) that indicates how much total wear a given cathode filament has accumulated or has accumulated since the given cathode filament was new or recently installed.

[0103] In various aspects, possible intervention allocations (e.g., any of 606) are considered to allocate or assign a given medical imaging intervention (e.g., any of 106) to a given medical imaging scanner. In various examples, the optimizer engine 602 may manipulate or adjust operational input parameters of the cathode filament wear model 702 based on, as specified by, or otherwise in accordance with the given medical imaging intervention. Such manipulation or adjustment may cause the cathode filament wear model 702 to calculate or account for a simulated cathode filament wear state 704. In various cases, the simulated cathode filament wear state 704 may be any suitable electronic data (e.g., one or more scalars, one or more vectors, one or more matrices, one or more tensors, or any suitable combination thereof) that indicates how much total wear a given cathode filament would accumulate if the given medical imaging intervention were actually to be implemented or performed by a given medical imaging scanner.

[0104] In various aspects, the optimizer engine 602 may calculate an intra-scanner cathode filament wear difference based on any two simulated cathode filament wear states corresponding to the same medical imaging scanner. As a non-limiting example, assume that a particular medical imaging scanner includes a first cathode filament and a second cathode filament. In various instances, as described above, the optimizer engine 602 may calculate: a simulated cathode filament wear state 706 for the first cathode filament; and a simulated cathode filament wear state 708 for the second cathode filament. Because both the first cathode filament and the second cathode filament may belong to a particular medical imaging scanner (e.g., to the same scanner as each other), the optimizer engine 602 may calculate an intra-scanner cathode filament wear difference 710 based on the simulated cathode filament wear state 706 and based on the simulated cathode filament wear state 708. More specifically, the intra-scanner cathode filament wear difference 710 may be equal to or otherwise based on the absolute value of the difference between the simulated cathode filament wear state 706 and the simulated cathode filament wear state 708.

[0105] Recall that in the non-limiting example shown in the figures, the plurality of medical imaging scanners 104 may include n scanners, and each medical imaging scanner may include mp cathode filaments. Thus, in the non-limiting example shown in the figures, the optimizer engine 602 may calculate the sum of the differences in cathode filament wear within the scanner for any given intervention allocation. (For example, where In various examples, the optimizer engine 602 may select such The intra-scanner cathode filament wear differences are totaled, such as via averaging, summing, multiplication, or any suitable combination thereof. Such totaling may be considered to produce a total intra-scanner cathode filament wear difference that would be produced or achieved if the given intervention allocation were actually implemented. In various cases, as described above, the optimization criterion 604 may be minimization of such total intra-scanner cathode filament wear differences. In other words, the optimizer engine 602 may simulate each of the set of possible intervention allocations 606 on multiple digital scanner twins 402, thereby producing a corresponding total intra-scanner cathode filament wear difference for each of the set of possible intervention allocations 606, and the optimizer engine 602 may select that intervention allocation in the set of possible intervention allocations 606 that has the lowest or minimum total intra-scanner cathode filament wear difference as the recommended intervention allocation 404. In such cases, the recommended intervention allocation 404 can be considered to best even out the wear of the cathode filaments of each individual medical imaging scanner in the plurality of medical imaging scanners, so as to minimize situations where an X-ray tube may reach the end of its useful life while still having one or more significantly unconsumed cathode filaments, and so as to minimize situations where one or more X-ray tubes of a medical imaging scanner are at the end of their useful life while one or more other X-ray tubes of the same medical imaging scanner still have significant remaining useful life. In other words, in such cases, the recommended intervention allocation 404 can be considered to reduce cathode filament waste and to increase ease of scanner maintenance.

[0106] Now, in the non-limiting example of the accompanying drawings, any given medical imaging scanner may include mp cathode filaments. In various aspects, as described above, the optimizer engine 602 may calculate or account for a corresponding simulated cathode filament wear state for each of those mp cathode filaments. For ease of explanation, these may be referred to as simulated cathode filament wear states 712(1) to simulated cathode filament wear states 712(mp). In various instances, the optimizer engine 602 may aggregate such mp simulated cathode filament wear states, such as via averaging, summing, multiplication, or any suitable combination thereof. Such aggregation may be considered to produce a scanner-by-scanner cathode filament wear state 714 for a given medical imaging scanner.

[0107] In various aspects, the optimizer engine 602 may calculate an inter-scanner cathode filament wear difference based on any two scanner-by-scanner cathode filament wear states corresponding to any two medical imaging scanners, respectively. As a non-limiting example, consider a first medical imaging scanner (e.g., one of the medical imaging scanners 104) and a second medical imaging scanner (e.g., another of the medical imaging scanners 104). In various instances, as described above, the optimizer engine 602 may calculate: a scanner-by-scanner cathode filament wear state 716 for the first medical imaging scanner; and a scanner-by-scanner cathode filament wear state 718 for the second medical imaging scanner. In various cases, the optimizer engine 602 may calculate an intra-scanner cathode filament wear difference 720 based on the scanner-by-scanner cathode filament wear state 716 and based on the scanner-by-scanner cathode filament wear state 718. More specifically, the inter-scanner cathode filament wear difference 720 may be equal to or otherwise based on an absolute value of a difference between the scanner-by-scanner cathode filament wear state 716 and the scanner-by-scanner cathode filament wear state 718.

[0108] Recall that in the non-limiting example shown in the figures, the plurality of medical imaging scanners 104 may include n scanners. Thus, in the non-limiting example shown in the figures, the optimizer engine 602 may calculate for any given intervention allocation The sum of the differences in cathode filament wear between the scanners. In various examples, the optimizer engine 602 may The cathode filament wear differences between the scanners are summed up, such as by averaging, summing, multiplication, or any suitable combination thereof. Such summing can be considered to produce a total scanner-to-scanner cathode filament wear difference that would be achieved if the given intervention allocation was actually implemented. In various cases, as described above, the optimization criterion 604 can be the maximization of such total scanner-to-scanner cathode filament wear difference. In other words, the optimizer engine 602 can simulate each intervention allocation in the set of possible intervention allocations 606 on multiple digital scanner twins 402, thereby producing a corresponding total scanner-to-scanner cathode filament wear difference for each intervention allocation in the set of possible intervention allocations 606, and the optimizer engine 602 can select one of the possible intervention allocations in the set of possible intervention allocations 606 that has the highest or maximum total scanner-to-scanner cathode filament wear difference as the recommended intervention allocation 404. In such cases, the recommended intervention allocation 404 can be considered to best cause the cathode filaments of the multiple medical imaging scanners 104 to wear in a staggered synchronous manner so as to minimize the situation where many of the multiple medical imaging scanners 104 may be shut down or offline for maintenance at the same time as each other. In other words, recommending intervention allocations 404 in such situations may be considered helpful in preventing large-scale or widespread downtime of multiple medical imaging scanners 104 .

[0109] Now, as described above, any of the plurality of medical imaging scanners 104 may be located at the same or different physical locations as each other. In various aspects, the optimizer engine 602 may calculate the intra-location cathode filament wear difference based on any two scanner-by-scanner cathode filament wear states corresponding to any two medical imaging scanners located or deployed at the same physical location as each other. As a non-limiting example, consider a first medical imaging scanner (e.g., one of the medical imaging scanners 104) and a second medical imaging scanner (e.g., another of the medical imaging scanners 104), both of which are deployed at a particular physical location. In various instances, as described above, the optimizer engine 602 may calculate: a scanner-by-scanner cathode filament wear state 722 of the first medical imaging scanner; and a scanner-by-scanner cathode filament wear state 724 of the first medical imaging scanner. In various cases, because both the first medical imaging scanner and the second medical imaging scanner are located at a particular physical location, the optimizer engine 602 may calculate the intra-location cathode filament wear difference 726 based on the scanner-by-scanner cathode filament wear state 722 and based on the scanner-by-scanner cathode filament wear state 724. More specifically, the intra-scanner cathode filament wear difference 726 may be equal to or otherwise based on the absolute value of the difference between the scanner-by-scanner cathode filament wear status 722 and the scanner-by-scanner cathode filament wear status 724 .

[0110] Thus, as described above, for any given intervention allocation, the optimizer engine 602 may calculate or account for the respective intra-position cathode filament wear differences for each distinct or unique pair of medical imaging scanners located at a common physical location relative to one another. In various instances, the optimizer engine 602 may aggregate such various intra-position cathode filament wear differences, such as via averaging, summing, multiplication, or any suitable combination thereof. Such aggregation may be considered to produce an aggregate intra-position cathode filament wear difference that would be achieved if the given intervention allocation were actually achieved. In various cases, as described above, the optimization criterion 604 may be minimization of such aggregate intra-position cathode filament wear differences. In other words, the optimizer engine 602 may simulate each intervention allocation in the set of possible intervention allocations 606 on a plurality of digital scanner twins 402, thereby producing a respective aggregate intra-position cathode filament wear difference for each intervention allocation in the set of possible intervention allocations 606, and the optimizer engine 602 may select one of the possible intervention allocations 606 having the lowest or minimum aggregate intra-position cathode filament wear difference as the recommended intervention allocation 404. In such cases, the recommended intervention allocation 404 can be considered to best enable the plurality of medical imaging scanners 104 to be worn in a location-synchronized manner so as to minimize situations where many of the plurality of medical imaging scanners 104 deployed at the same physical location as one another may need to be serviced at significantly different or staggered times from one another. In other words, in such cases, the recommended intervention allocation 404 can be considered to help increase scanner serviceability.

[0111] In various embodiments, the above specific implementations of optimization criteria 604 are merely non-limiting examples. In various aspects, optimization criteria 604 may involve minimization or maximization of any other suitable value. In some cases, optimization criteria 604 may even involve any suitable combination of the foregoing (e.g., may involve simultaneous optimization of intra-scanner cathode filament wear differences, inter-scanner cathode filament wear differences, or intra-location cathode filament wear differences).

[0112] Note that in some cases, the optimizer engine 602 may implement a brute force or exhaustive optimization algorithm or technique. However, this is only a non-limiting example. In other cases, the optimizer engine 602 may implement any other suitable type of optimization algorithm or technique, such as: gradient descent; differential evolution; evolutionary algorithm; genetic algorithm; Nelder-Mead simple heuristic technique; random tunneling; or synchronous perturbation stochastic approximation.

[0113] Now, consider Figures 8 to 9 . Figures 8 to 9An example non-limiting block diagram showing how a deep learning neural network may be utilized to generate a recommended intervention allocation in accordance with one or more embodiments described herein is illustrated.

[0114] First, consider Figure 8 . In various embodiments, the allocation component 114 (e.g., in place of the optimizer engine 602) may electronically store, electronically maintain, electronically control, or otherwise electronically access the deep learning neural network 804. In various aspects, the deep learning neural network 804 may be any suitable artificial neural network that may have or otherwise exhibit any suitable internal architecture. For example, the deep learning neural network 804 may have an input layer, one or more hidden layers, and an output layer. In various instances, any of such layers may be connected together by any suitable inter-neuron connection or inter-layer connection (such as a forward connection, a skip connection, or a recursive connection). In addition, in various cases, any of such layers may be a neural network layer of any suitable type with any suitable learnable or trainable internal parameters. For example, any of such input layer, one or more hidden layers, or output layer may be a convolutional layer, and the learnable or trainable parameters of the convolutional layer may be a convolution kernel. As another example, any of such input layer, one or more hidden layers, or output layer may be a dense layer, and the learnable or trainable parameters of the dense layer may be a weight matrix or a bias value. As even another example, any of such input layer, one or more hidden layers, or output layer may be an LSTM layer, and the learnable or trainable parameters of the LSTM layer may be an input state weight matrix or a hidden state weight matrix. As yet another example, any of such input layer, one or more hidden layers, or output layer may be a batch normalization layer, and the learnable or trainable parameters of the batch normalization layer may be a shift factor or a scale factor. Further, in various cases, any of such layers may be a neural network layer of any suitable type with any suitable fixed or non-trainable internal parameters. For example, any of such input layer, one or more hidden layers, or output layer may be a nonlinear layer, a padding layer, a pooling layer, or a cascade layer.

[0115] In any case, the deep learning neural network 804 may be configured to intelligently or judiciously distribute the input medical imaging interventions among the input medical imaging scanners. In particular, as described above, each cathode filament wear model of the plurality of digital scanner twins 402 may indicate a present-time cathode filament wear state of a corresponding cathode filament of a corresponding medical imaging scanner prior to simulating any new or additional intervention allocations (e.g., prior to simulating any of the possible intervention allocations 606). In the non-limiting example of the accompanying drawings, there may be a total of nmp cathode filament wear models in the plurality of digital scanner twins 402. Thus, there may therefore be a total of nmp present-time cathode filament wear states indicated by the plurality of digital scanner twins 402. In various instances, such nmp present-time cathode filament wear states may be collectively referred to as a plurality of present-time cathode filament wear states 802. In other words, the multiple current time cathode filament wear states 802 can be any suitable electronic data (e.g., can be one or more scalars, one or more vectors, one or more matrices, one or more tensors, or any suitable combination thereof) that indicates how much wear has accumulated or has accumulated for each specific cathode filament of each specific medical imaging scanner in the multiple medical imaging scanners 104.

[0116] In various cases, the assignment component 114 can electronically execute the deep learning neural network 804 on both the plurality of current time cathode filament wear states 802 and the set of medical imaging interventions 106, and such execution can cause the deep learning neural network 804 to generate the recommended intervention assignment 404 as an output. More specifically, the assignment component 114 can cascade the plurality of current time cathode filament wear states 802 and the set of medical imaging interventions 106 together. In various aspects, the assignment component 114 can feed such cascade to an input layer of the deep learning neural network 804. In various instances, such cascade can complete a forward pass through one or more hidden layers of the deep learning neural network 804. In various cases, the output layer of the deep learning neural network 804 can calculate or otherwise account for the recommended intervention assignment 404 based on the activation map or feature map generated by the one or more hidden layers of the deep learning neural network 804.

[0117] In embodiments where the recommended intervention assignment 404 is determined by the deep learning neural network 804, the deep learning neural network 804 may first be trained to help make the recommended intervention assignment 404 reliable (e.g., actually help reduce cathode filament waste or increase scanner repair ease). In some cases, such training may be facilitated in a supervised manner, such as with respect to Fig. 9 described.

[0118] Fig. 9An example non-limiting block diagram showing how a deep learning neural network 804 may be trained in a supervised manner according to one or more embodiments described herein is illustrated.

[0119] Before starting training, the trainable internal parameters of the deep learning neural network 804 (e.g., weight matrices, bias values, convolution kernels) can be initialized in any suitable manner (e.g., via random initialization).

[0120] In various aspects, there may be a plurality of training current-time cathode filament wear states 902, and there may also be a set of training medical imaging interventions 904. In various instances, the plurality of training current-time cathode filament wear states 902 and the set of training medical imaging interventions 904 may be considered to correspond to a reference truth recommended intervention allocation 906. In various cases, the reference truth recommended intervention allocation 906 may be considered to be any allocation known to the set of training medical imaging interventions 904 or to be the best allocation for the set of training medical imaging interventions (e.g., in terms of reducing cathode filament waste or increasing scanner maintenance ease) given the plurality of training current-time cathode filament wear states 902. In some aspects, the reference truth recommended intervention allocation 906 may be manually formulated by a subject matter expert. In other aspects, the reference truth recommended intervention allocation 906 may be generated or determined by the optimizer engine 602.

[0121] In any case, the deep learning neural network 804 may be executed on both the plurality of training current-time cathode filament wear states 902 and the set of training medical imaging interventions 904, and such execution may cause the deep learning neural network 804 to produce an output 908. More specifically, the plurality of training current-time cathode filament wear states 902 and the set of training medical imaging interventions 904 may be cascaded together, such cascade may be fed to an input layer of the deep learning neural network 804, such cascade may complete a forward pass through one or more hidden layers of the deep learning neural network 804, and the output layer of the deep learning neural network 804 may calculate or compute the output 908 based on activation maps or feature maps produced by one or more hidden layers of the deep learning neural network 804.

[0122] Note that in various cases, the format, size, or dimensionality of the output 908 may be controlled or otherwise determined by the number, arrangement, or size of neurons included in or otherwise constituting the output layer (or any other layer) of the deep learning neural network 804, or the number, arrangement, or size of other internal parameters (e.g., convolution kernels). Thus, the output 908 may be forced to have any desired format, size, or dimensionality by adding neurons or other internal parameters to the output layer (or any other layer) of the deep learning neural network 804, removing neurons or other internal parameters from the output layer (or any other layer) of the deep learning neural network, or otherwise adjusting neurons or other internal parameters within the output layer (or any other layer) of the deep learning neural network. Thus, in various aspects, the output 908 may be considered to be the predicted or inferred recommended intervention allocation that the deep learning neural network 804 believes should correspond to the plurality of training current-time cathode filament wear states 902 and the set of training medical imaging interventions 904. In contrast, the ground truth recommended intervention assignment 906 may be a correct or accurate recommended intervention assignment that is known or believed to correspond to the plurality of training current-time cathode filament wear states 902 and the set of training medical imaging interventions 904. Note that if the deep learning neural network 804 has not been trained at all or has only been trained very little to date, the output 908 may be highly inaccurate (e.g., may differ significantly from the ground truth recommended intervention assignment 906).

[0123] In various aspects, an error 910 between the output 908 and the ground truth recommended intervention allocation 906 may be calculated. In various instances, the error 910 may be calculated using any suitable loss or objective function. As some non-limiting examples, the error 910 may be a mean absolute error, a mean squared error, or a cross entropy error. In various cases, as shown, the trainable internal parameters of the deep learning neural network 804 may be incrementally updated by performing back propagation (e.g., stochastic gradient descent) driven by the error 910.

[0124] In various cases, the above training procedure may be repeated any suitable number of times (e.g., for any suitable input annotation tuples). This may ultimately cause the trainable internal parameters of the deep learning neural network 804 to become iteratively optimized for accurately distributing the input medical imaging interventions across multiple medical imaging scanners 104 in order to reduce cathode filament waste or increase scanner maintenance convenience. In various aspects, any suitable training batch size or any suitable training termination criteria may be implemented.

[0125] Although the above discussion mainly involves training the deep learning neural network 804 in a supervised manner, this is only a non-limiting example for ease of explanation. In various instances, any other suitable training paradigm such as unsupervised training or reinforcement learning can be implemented to train the deep learning neural network 804.

[0126] Note that in various embodiments, any other suitable machine learning architecture that implements an artificial intelligence architecture may be substituted for the deep learning neural network 804. Some non-limiting examples may include: a support vector machine; a naive Bayes model; a linear regression model; a logistic regression model; a decision tree model; or a random forest model.

[0127] In any case, the allocation component 114 can electronically determine the recommended intervention allocation 404 by utilizing the plurality of digital scanner twins 402 .

[0128] Note that in some cases, the allocation component 114 may update the present-time cathode filament wear state (e.g., 802) indicated by the plurality of digital scanner twins 402 based on the recommended intervention allocation 404. As a non-limiting example, whatever simulated cathode filament wear state the plurality of digital scanners 402 calculated or predicted for the recommended intervention allocation 404 may be considered as the new present-time cathode filament wear state during future simulations.

[0129] In various embodiments, the results component 116 can electronically perform or initiate any suitable action based on the recommended intervention assignment 404 .

[0130] As a non-limiting example, the results component 116 can electronically send the recommended intervention assignment 404 to any suitable computing device.

[0131] As another non-limiting example, the results component 116 can electronically present the recommended intervention assignment 404 on any suitable electronic display (e.g., on any suitable computer screen, on any suitable computer monitor). In some cases, the results component 116 can electronically present any other suitable information about the recommended intervention assignment 404 on the electronic display, such as: an estimated wear accumulated by any given cathode filament of any given medical imaging scanner; an estimated time for maintenance (e.g., x-ray tube replacement) of any given medical imaging scanner; or an estimated time to perform an assigned medical imaging intervention for any given medical imaging scanner.

[0132] As yet another non-limiting example, the results component 116 can electronically cause the set of medical imaging interventions 106 to be allocated among the plurality of medical imaging scanners 104 in accordance with the recommended allocation 404. For example, the results component 116 can electronically send the recommended allocation 404 to any suitable scheduling system that manages the plurality of medical imaging scanners 104. In such cases, each of the plurality of medical imaging scanners 104 can independently communicate with the scheduling system to identify which of the set of medical imaging interventions 106 to allocate to the medical imaging scanner in accordance with the recommended intervention allocation 404. However, in other cases, the results component 116 can instead directly electronically instruct each of the plurality of medical imaging scanners 104 to commit (e.g., via a local scan protocol commit operation) or otherwise prepare (e.g., via automatic scanner initialization) for assignment or allocation of which of the set of medical imaging interventions 106 to the medical imaging scanner in accordance with the recommended intervention allocation 404.

[0133] Additionally, it is noted that while various embodiments have been primarily described in which the plurality of digital scanner twins 402 simulate or predict wear accumulated by cathode filaments of the plurality of medical imaging scanners 104, this is merely a non-limiting example for ease of explanation and illustration. In various other embodiments, the plurality of digital scanner twins 402 may simulate or predict wear accumulated by any other suitable component or part of any of the plurality of medical imaging scanners 104. As some non-limiting examples, the plurality of digital scanner twins 402 may simulate or predict wear accumulated by: any suitable gantry motor of the plurality of medical imaging scanners 104 (e.g., in which case the digital twin may include a gantry motor wear model); any suitable table actuator of the plurality of medical imaging scanners 104 (e.g., in which case the digital twin may include a table actuator wear model); any suitable signal brush of the plurality of medical imaging scanners 104 (e.g., in which case the digital twin may include a signal brush wear model); or any suitable motorized brush of the plurality of medical imaging scanners 104 (e.g., in which case the digital twin may include a motorized brush wear model).

[0134] Fig.10 A flow chart illustrating an example non-limiting computer-implemented method 1000 that can facilitate intelligent intervention allocation for a medical imaging scanner according to one or more embodiments described herein. In some cases, the scanner wear system 102 can facilitate the computer-implemented method 1000 .

[0135] In various embodiments, act 1002 may include accessing, by a device (e.g., via 112) operatively coupled to a processor (e.g., 108), a set of medical imaging interventions (e.g., 106) to be performed on a plurality of medical imaging scanners (e.g., 104).

[0136] In various aspects, act 1004 may include determining, by the device (e.g., via 114) and based on a plurality of digital scanner twins (e.g., 402), each digital scanner twin comprising one or more cathode filament wear models (e.g., 402(1)(1), 402(1)(m); 402(n)(1); 402(n)(m)) for a respective one of a plurality of medical imaging scanners, a recommended allocation (e.g., 404) indicating how the set of medical imaging interventions should be allocated among the plurality of medical imaging scanners.

[0137] In various instances, action 1006 may include allocating, by the device (e.g., via 116), the set of medical imaging interventions among the plurality of medical imaging scanners according to a recommended allocation, wherein the recommended allocation may indicate that a first medical imaging intervention in the set of medical imaging interventions (e.g., any one of the medical imaging interventions 106) is to be assigned to a first medical imaging scanner in the plurality of medical imaging scanners (e.g., any one of the medical imaging scanners 104), and wherein the allocating may include instructing, by the device (e.g., via 116), the first medical imaging scanner to locally submit or prepare the first medical imaging intervention.

[0138] Despite Fig.10 Not explicitly shown, but the device may simulate on multiple digital scanner twins multiple different allocations of the set of medical imaging interventions among multiple medical imaging scanners (e.g., 606), and the recommended allocation may be that allocation among those multiple different allocations that minimizes the total intra-scanner cathode filament wear difference (e.g., 604) indicated by the multiple digital scanner twins.

[0139] Despite Fig.10 Not explicitly shown, but the device may simulate on multiple digital scanner twins multiple different allocations of the set of medical imaging interventions among multiple medical imaging scanners (e.g., 606), and the recommended allocation may be that allocation among those multiple different allocations that maximizes the total inter-scanner cathode filament wear difference (e.g., 604) indicated by the multiple digital scanner twins.

[0140] Despite Fig.10Not explicitly shown, but the device may simulate on multiple digital scanner twins multiple different allocations of the set of medical imaging interventions among multiple medical imaging scanners (e.g., 606), and the recommended allocation may be that allocation among those multiple different allocations that minimizes the difference in cathode filament wear within the total positions indicated by the multiple digital scanner twins (e.g., 604).

[0141] Despite Fig.10 Not explicitly shown, the device may determine a recommended allocation by executing a machine learning model (e.g., 804) on the set of medical imaging interventions and on the current time cathode filament wear state (e.g., 802) indicated by multiple digital scanner twins.

[0142] Although in Fig.10 Not explicitly shown, but each of the multiple digital scanner twins may also include: one or more gantry motor wear models of a corresponding one of the multiple medical imaging scanners; one or more signal brush wear models of a corresponding one of the multiple medical imaging scanners; or one or more power brush wear models of a corresponding one of the multiple medical imaging scanners.

[0143] Despite Fig.10 Not explicitly shown, each medical imaging intervention may indicate a corresponding medical imaging protocol to be performed on the corresponding medical patient (e.g., 106(1)(1)) and may indicate physical characteristics of the corresponding medical patient (e.g., 106(1)(2)).

[0144] Although the disclosure herein primarily describes each medical imaging intervention in the set of medical imaging interventions 106 as being implementable, performed, or achievable by any one of the plurality of medical imaging scanners 104, this is merely a non-limiting example for ease of explanation and illustration. In various cases, any one of the set of medical imaging interventions 106 may be implementable, performed, or achievable by less than all of the plurality of medical imaging scanners 104. For example, any given medical imaging intervention may be subject to various requirements that are satisfied by less than all of the plurality of medical imaging scanners 104. As a non-limiting example, some specific medical imaging interventions may only be implemented by some specific medical imaging scanners (e.g., a lower extremity scan protocol may only be implemented by a lower extremity CT scanner; a scan to be performed on a patient in a specific intensive care unit may only be performed by a scanner near the specific intensive care unit). As another non-limiting example, some specific patient characteristics may require a specific medical imaging scanner (e.g., a scan protocol to be performed on an obese patient may require a wide-bore CT scanner with a high-capacity cradle). It should be understood that various embodiments described herein may take such additional intervention requirements into account so as to assign any given medical imaging intervention only to corresponding medical imaging scanners that meet such additional intervention requirements (e.g., so as to avoid assigning a given intervention to an incompatible scanner).

[0145] In various instances, the machine learning algorithm or model may be implemented in any suitable manner to facilitate any suitable aspect described herein. In order to facilitate some of the above machine learning aspects of various embodiments, consider the following discussion of artificial intelligence (AI). Various embodiments described herein may employ artificial intelligence to facilitate the automation of one or more features or functionalities. These components may employ various AI-based schemes to perform various embodiments / examples disclosed herein. In order to provide or contribute to the numerous determinations described herein (e.g., determination, detection, inference, calculation, prediction, prognosis, estimation, derivation, forecast, detection, estimation), the components described herein may examine the entirety or subset of the data to which they are granted access rights, and may provide reasoning about or determine the state of a system or environment from a set of observations captured via events or data. For example, determination may be used to identify a specific context or action, or a probability distribution of a state may be generated. These determinations may be probabilistic; that is, the calculation of the probability distribution of the state of interest is based on consideration of data and events. Determination may also refer to a technique for composing a higher-level event from a collection of events or data.

[0146] Such determinations may result in the construction of new events or actions from a collection of observed events or stored event data, whether or not the events are closely related in time, and whether or not the events and data come from one or more event and data sources. The components disclosed herein may employ various classification (explicitly trained (e.g., via training data) and implicitly trained (e.g., via observed behavior, preferences, historical information, receiving external information, etc.)) schemes or systems (e.g., support vector machines, neural networks, expert systems, Bayesian belief networks, fuzzy logic, data fusion engines, etc.) in conjunction with performing automatic or determined actions related to the claimed subject matter. Thus, the classification schemes or systems may be used to automatically learn and perform a variety of functions, actions, or determinations.

[0147] The classifier can take the input attribute vector z = (z 1 ,z 2 ,z 3 ,z 4 ,z n ) is mapped to the confidence that the input belongs to a certain category, such as according to f(z) = confidence(category). Such classification can use probability-based or statistical analysis (e.g., analyzing utility and cost considerations) to determine the actions to be automatically performed. Support vector machines (SVMs) may be examples of classifiers that can be used. SVMs operate by finding a hypersurface in the space of possible inputs, where the hypersurface attempts to separate triggering criteria from non-triggering events. Intuitively, this makes the classification correct for testing data that is close to but different from the training data. Other directed and undirected model classification methods include, for example, naive Bayes, Bayesian networks, decision trees, neural networks, fuzzy logic models, or any of the probabilistic classification models that provide different independent patterns. Classification as used herein also includes statistical regression for developing priority models.

[0148] In order to provide additional context for the various embodiments described herein, Fig.11 The following discussion is intended to provide a brief general description of a suitable computing environment 1100 in which various embodiments described herein may be implemented. Although the embodiments have been described above in the general context of computer-executable instructions that may be executed on one or more computers, those skilled in the art will recognize that the embodiments may also be implemented in conjunction with other program modules or as a combination of hardware and software.

[0149] Generally, program modules include routines, programs, components, data structures, etc. that perform specific tasks or implement specific abstract data types. In addition, those skilled in the art will appreciate that the methods of the present invention can be practiced with other computer system configurations, including single-processor or multi-processor computer systems, minicomputers, mainframe computers, Internet of Things (IoT) devices, distributed computing systems, as well as personal computers, handheld computing devices, microprocessor-based or programmable consumer electronics, etc., each of which can be operably connected to one or more associated devices.

[0150] The illustrated embodiments of the embodiments herein may also be practiced in distributed computing environments where certain tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote memory storage devices.

[0151] Computing devices typically include various media, which may include computer-readable storage media, machine-readable storage media, or communication media, where the two terms are used differently in this article, as described below. Computer-readable storage media or machine-readable storage media can be any available storage media that can be accessed by a computer, and include volatile and non-volatile media, removable and non-removable media. By way of example and not limitation, computer-readable storage media or machine-readable storage media can be implemented in conjunction with any method or technology for storing information such as computer-readable or machine-readable instructions, program modules, structured data, or unstructured data.

[0152] Computer-readable storage media may include, but are not limited to, random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD), Blu-ray disk (BD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, solid-state drives or other solid-state storage devices, or other tangible or non-transitory media that can be used to store the desired information. In this regard, the terms "tangible" or "non-transitory" as applied to storage, memory, or computer-readable media herein should be understood to exclude only propagating transient signals themselves as a modifier, and not to disclaim all standard storage, memory, or computer-readable media that are not merely propagating transient signals themselves.

[0153] Computer-readable storage media may be accessed by one or more local or remote computing devices, eg, via access requests, queries, or other data retrieval protocols, to perform various operations relative to the information stored by the media.

[0154] Communication media typically embodies computer readable instructions, data structures, program modules, or other structured or unstructured data in a data signal, which may be, for example, a modulated data signal, such as a carrier wave or other transport mechanism, and includes any information delivery or transmission media. The term "modulated data signal" or "signal" refers to a signal that has one or more of its characteristics set or changed to encode information in one or more signals. By way of example, and not limitation, communication media include wired media (such as a wired network or direct-wired connection) and wireless media (such as acoustic, RF, infrared and other wireless media).

[0155] Reference again Fig.11 , an example environment 1100 for implementing various embodiments of various aspects described herein includes a computer 1102, which includes a processing unit 1104, a system memory 1106, and a system bus 1108. The system bus 1108 couples system components including, but not limited to, the system memory 1106 to the processing unit 1104. The processing unit 1104 can be any of a variety of commercially available processors. Dual microprocessors and other multi-processor architectures can also be used as the processing unit 1104.

[0156] The system bus 1108 may be any of several types of bus structures capable of further interconnecting to a memory bus (with or without a memory controller), a peripheral bus, and a local bus using any of a variety of commercially available bus architectures. The system memory 1106 includes ROM 1110 and RAM 1112. A basic input / output system (BIOS) may be stored in a nonvolatile memory such as ROM, erasable programmable read-only memory (EPROM), EEPROM, wherein the BIOS contains basic routines that help to transfer information between elements within the computer 1102, such as during startup. The RAM 1112 may also include high-speed RAM, such as static RAM for caching data.

[0157] The computer 1102 also includes an internal hard disk drive (HDD) 1114 (e.g., EIDE, SATA), one or more external storage devices 1116 (e.g., a magnetic floppy disk drive (FDD) 1116, a memory stick or flash drive reader, a memory card reader, etc.), and a drive 1120, such as a solid-state drive, an optical drive, which can read or write from a disk 1122 (such as a CD-ROM disk, a DVD, a BD, etc.). Alternatively, in the case of a solid-state drive, the disk 1122 will not be included unless separated. Although the internal HDD 1114 is illustrated as being located within the computer 1102, the internal HDD 1114 can also be configured for use outside of a suitable infrastructure (not shown). In addition, although not shown in the environment 1100, a solid-state drive (SSD) can be used in addition to or instead of the HDD 1114. The HDD 1114, external storage device 1116, and drive 1120 may be connected to the system bus 1108 via a HDD interface 1124, an external storage interface 1126, and a drive interface 1128, respectively. The interface 1124 for an external drive implementation may include at least one or both of a Universal Serial Bus (USB) and an Institute of Electrical and Electronics Engineers (IEEE) 1394 interface technology. Other external drive connection technologies are within the contemplation of the embodiments described herein.

[0158] The drives and their associated computer-readable storage media provide non-volatile storage of data, data structures, computer-executable instructions, etc. For the computer 1102, the drives and storage media accommodate the storage of any data in a suitable digital format. Although the above description of computer-readable storage media refers to corresponding types of storage devices, it should be understood by those skilled in the art that other types of computer-readable storage media (whether currently existing or developed in the future) can also be used in the exemplary operating environment, and further, any such storage media can contain computer-executable instructions for performing the methods described herein.

[0159] A number of program modules may be stored in the drives and RAM 1112, including an operating system 1130, one or more application programs 1132, other program modules 1134, and program data 1136. All or portions of the operating system, application programs, modules, or data may also be cached in RAM 1112. The systems and methods described herein may be implemented using various commercially available operating systems or combinations of operating systems.

[0160] Computer 1102 may optionally include emulation technology. For example, a hypervisor (not shown) or other intermediary may emulate the hardware environment for operating system 1130, and the emulated hardware may optionally be different from the hardware of the operating system 1130. Fig.111102. In such an embodiment, operating system 1130 may include one of a plurality of virtual machines (VMs) hosted at computer 1102. In addition, operating system 1130 may provide a runtime environment, such as a Java runtime environment or a .NET framework, to application 1132. A runtime environment is a consistent execution environment that allows application 1132 to run on any operating system that includes the runtime environment. Similarly, operating system 1130 may support containers, and application 1132 may be in the form of containers that are lightweight, stand-alone, executable software packages that include, for example, the application's code, runtime, system tools, system libraries, and settings.

[0161] In addition, the computer 1102 may be enabled with a security module, such as a trusted processing module (TPM). For example, in the case of a TPM, the boot component is hashed in the next boot component, and the result is waited for to match the security value before loading the next boot component. This process may occur at any layer in the code execution stack of the computer 1102, such as applied to the application execution level or the operating system (OS) kernel level, thereby achieving security at any code execution level.

[0162] A user may enter commands and information into the computer 1102 through one or more wired / wireless input devices, such as a keyboard 1138, a touch screen 1140, and a pointing device such as a mouse 1142. Other input devices (not shown) may include a microphone, an infrared (IR) remote control, a radio frequency (RF) remote control or other remote control, a joystick, a virtual reality controller or virtual reality headset, a game pad, a stylus, an image input device (e.g., a camera), a gesture sensor input device, a visual movement sensor input device, an emotion or facial detection device, a biometric input device (e.g., a fingerprint or iris scanner), and the like. These and other input devices are often connected to the processing unit 1104 through an input device interface 1144, which may be coupled to the system bus 1108, but these and other input devices may be connected through other interfaces, such as a parallel port, an IEEE 1394 serial port, a game port, a USB port, an IR port, a fingerprint or iris scanner, or the like. interface, etc.) connection.

[0163] A monitor 1146 or other type of display device may also be connected to the system bus 1108 via an interface, such as a video adapter 1148. In addition to the monitor 1146, computers typically include other peripheral output devices (not shown), such as speakers, printers, and the like.

[0164] The computer 1102 can operate in a networked environment using logical connections to one or more remote computers, such as remote computer 1150, via wired or wireless communications. The remote computer 1150 can be a workstation, server computer, router, personal computer, portable computer, microprocessor-based entertainment appliance, peer device, or other public network node, and typically includes many or all of the elements described with respect to the computer 1102, but only a memory / storage device 1152 is illustrated for simplicity. The depicted logical connections include wired / wireless connections to a local area network (LAN) 1154 or a larger network, such as a wide area network (WAN) 1156. Such LAN and WAN networking environments are common in offices and companies, and facilitate enterprise-wide computer networks, such as intranets, all of which can be connected to a global communication network, such as the Internet.

[0165] When used in a LAN networking environment, the computer 1102 may be connected to a local network 1154 through a wired or wireless communication network interface or adapter 1158. The adapter 1158 may facilitate wired or wireless communication with the LAN 1154, which may also include a wireless access point (AP) disposed thereon to communicate with the adapter 1158 in a wireless mode.

[0166] When used in a WAN networking environment, the computer 1102 may include a modem 1160, or may be connected to a communications server on the WAN 1156 via other means for establishing communications over the WAN 1156, such as through the Internet. The modem 1160, which may be an internal or external device and a wired or wireless device, may be connected to the system bus 1108 via the input device interface 1144. In a networked environment, program modules depicted relative to the computer 1102, or portions thereof, may be stored in the remote memory / storage device 1152. It should be appreciated that the network connections shown are examples and other means of establishing a communications link between the computers may be used.

[0167] When used in a LAN or WAN networking environment, in addition to or as an alternative to the external storage devices 1116 described above, the computer 1102 may access a cloud storage system or other network-based storage system, such as, but not limited to, a network virtual machine that provides one or more aspects of information storage or processing. Generally, the connection between the computer 1102 and the cloud storage system may be established, for example, via the LAN 1154 or WAN 1156 by an adapter 1158 or a modem 1160, respectively. When the computer 1102 is connected to the associated cloud storage system, the external storage interface 1126 may manage the storage provided by the cloud storage system with the aid of the adapter 1158 or the modem 1160, as other types of external storage devices. For example, the external storage interface 1126 may be configured to provide access to cloud storage sources as if those cloud storage sources were physically connected to the computer 1102.

[0168] The computer 1102 may be operable to communicate with any wireless device or entity that is operably configured to communicate wirelessly, such as a printer, scanner, desktop or portable computer, portable data assistant, communication satellite, any piece of equipment or location associated with a wirelessly detectable tag (e.g., a self-service machine, a newsstand, a store shelf, etc.), and a telephone. This may include Wireless Fidelity (Wi-Fi) and Wireless technology. Therefore, the communication can be a predefined structure like a conventional network, or just an ad hoc communication between at least two devices.

[0169] Fig.12 1 is a schematic block diagram of a sample computing environment 1200 with which the disclosed subject matter can interact. The sample computing environment 1200 includes one or more clients 1210. The client 1210 can be hardware or software (e.g., a thread, a process, a computing device). The sample computing environment 1200 also includes one or more servers 1230. The server 1230 can also be hardware or software (e.g., a thread, a process, a computing device). For example, the server 1230 can accommodate threads to be transformed by adopting one or more embodiments as described herein. One possible communication between the client 1210 and the server 1230 can be in the form of data packets suitable for transmission between two or more computer processes. The sample computing environment 1200 includes a communication framework 1250 that can be used to facilitate communication between the client 1210 and the server 1230. The client 1210 is operably connected to one or more client data repositories 1220, which can be used to store information local to the client 1210. Similarly, server 1230 may be operably connected to one or more server data repositories 1240 , which may be used to store information local to server 1230 .

[0170] Various embodiments can be systems, methods, devices or computer program products at any possible level of technical detail of integration. A computer program product may include a computer-readable storage medium (or multiple media) having computer-readable program instructions thereon for various aspects of various embodiments implemented by a processor. A computer-readable storage medium may be a tangible device that can hold and store instructions for use by an instruction execution device. A computer-readable storage medium may be, for example, but not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device or any suitable combination of the foregoing. An incomplete list of more specific examples of computer-readable storage media may also include the following: a portable computer floppy disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device (such as a punch card or a raised structure in a groove with instructions recorded thereon), and any suitable combination of the above items. As used herein, computer-readable storage media should not be understood as transient signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses passing through fiber optic cables), or electrical signals transmitted through wires.

[0171] Computer-readable program instructions as described herein can be downloaded to corresponding computing / processing equipment from computer-readable storage media, or downloaded to external computers or external storage devices via networks (e.g., the Internet, local area networks, wide area networks, or wireless networks). Networks can include copper transmission cables, optical transmission optical fibers, wireless transmissions, routers, firewalls, switches, gateway computers, or edge servers. Network adapter cards or network interfaces in each computing / processing equipment receive computer-readable program instructions from the network, and forward computer-readable program instructions for storage in computer-readable storage media in corresponding computing / processing equipment. Computer-readable program instructions for the operation of various embodiments for implementation can be assembler instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, configuration data of integrated circuits, or source code or object code written in any combination of one or more programming languages ​​(including object-oriented programming languages, such as Smalltalk, C++, etc.) and process programming languages ​​(such as "C" programming languages ​​or similar programming languages). Computer readable program instructions can be executed completely on the user's computer, partially on the user's computer, executed as an independent software package, partially on the user's computer and partially on a remote computer, or completely on a remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network (including a local area network (LAN) or a wide area network (WAN)), or can be connected to an external computer (for example, by using the Internet of an Internet service provider). In some embodiments, electronic circuits including, for example, programmable logic circuits, field programmable gate arrays (FPGAs), or programmable logic arrays (PLAs) can be executed by utilizing the state information of computer readable program instructions to personalize electronic circuits, so as to perform various aspects.

[0172] The various aspects described herein are described by flowchart illustrations or block diagrams of the methods, devices (systems) and computer program products according to various embodiments. It should be understood that each frame of the flowchart illustration or block diagram, and the combination of the frames in the flowchart illustration or block diagram can be implemented by computer-readable program instructions. These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device create a component for implementing the function / action specified in one or more frames of the flowchart or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium, which can instruct a computer, a programmable data processing device or other equipment to act in a particular manner, so that a computer-readable storage medium with instructions stored therein includes a product, and the product includes instructions for implementing various aspects of the function / action specified in one or more frames of the flowchart or block diagram. Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices, so that a series of operational actions are performed on the computer, other programmable apparatus, or other devices to produce a computer-implemented process, so that the instructions executed on the computer, other programmable apparatus, or other devices implement the functions / actions specified in one or more boxes of the flowchart or block diagram.

[0173] The flow chart and block diagram in the accompanying drawings illustrate the possible specific implementation architecture, functionality and operation of the system, method and computer program product according to various embodiments. In this regard, each frame in the flow chart or block diagram can represent a module, fragment or part of an instruction, which includes one or more executable instructions for realizing a specified logical function. In some alternative specific implementations, the function indicated in the frame may not occur in the order indicated in the figure. For example, in fact, the two frames shown in succession can be executed substantially at the same time, or sometimes these frames may be executed in reverse order, depending on the functionality involved. It will also be noted that each frame illustrated in the block diagram or flow chart and the combination of frames in the block diagram or flow chart illustration can be realized by a system based on dedicated hardware that performs a specified function or action or implements a combination of dedicated hardware and computer instructions.

[0174] Although the present subject matter has been described above in the general context of computer executable instructions of a computer program product running on one or more computers, it will be appreciated by those skilled in the art that the present disclosure may also or may be implemented in combination with other program modules. Typically, a program module includes routines, programs, components, data structures, etc. that perform specific tasks or implement specific abstract data types. In addition, it will be appreciated by those skilled in the art that various aspects may be practiced with other computer system configurations, including single-processor computer systems or multi-processor computer systems, small computing devices, large computers, and computers, handheld computing devices (e.g., PDAs, phones), microprocessor-based or programmable consumer or industrial electronics, etc. The illustrated aspects may also be practiced in a distributed computing environment in which tasks are performed by a remote processing device linked by a communication network. However, some (if not all) aspects of the present disclosure may be practiced on a stand-alone computer. In a distributed computing environment, program modules may be located in local and remote memory storage devices.

[0175] As used in this application, the terms "component", "system", "platform", "interface", etc. may refer to or include computer-related entities or entities related to an operating machine having one or more specific functionalities. The entities disclosed herein may be hardware, a combination of hardware and software, software, or software in execution. For example, a component may be, but is not limited to, a program, a processor, an object, an executable file, an execution thread, a program, or a computer running on a processor. By way of illustration, both an application running on a server and a server may be components. One or more components may reside within a process or a thread of execution, and a component may be located on a computer or distributed between two or more computers. As another example, the corresponding component may be executed according to various computer-readable media having various data structures stored thereon. The components may communicate via a local or remote process, such as according to a signal having one or more data packets (e.g., data from a component that interacts with another component in a local system, a distributed system, or a network (such as the Internet with other systems) via a signal). As another example, a component may be a device having a specific functionality provided by a mechanical part operated by an electrical or electronic circuit, which is operated by a software or firmware application executed by a processor. In such cases, the processor may be internal or external to the device and may execute at least a portion of a software or firmware application. As yet another example, a component may be a device that provides specific functionality through electronic components rather than mechanical parts, where the electronic components may include a processor or other means for executing software or firmware that at least partially imparts the functionality to the electronic components. In one aspect, the component may emulate an electronic component, such as via a virtual machine within a cloud computing system.

[0176] In addition, the term "or" is intended to mean an inclusive "or" rather than an exclusive "or". That is, unless otherwise specified or clear from the context, "X employs A or B" is intended to mean any natural inclusive replacement. That is, if X employs A; X employs B; or X employs both A and B, then "X employs A or B" is satisfied in any of the foregoing cases. As used herein, the term "and / or" is intended to have the same meaning as "or". In addition, unless otherwise specified or clear from the context that it is for a singular form, the articles "a" and "an" used in this specification and the drawings should generally be understood to mean "one or more". As used herein, the term "example" or "exemplary" is used to mean serving as an example, instance, or illustration. For the avoidance of doubt, the subject matter disclosed herein is not limited to such examples. In addition, any aspect or design described herein as an "example" or "exemplary" is not necessarily to be understood as being preferred or advantageous over other aspects or designs, nor is it intended to exclude equivalent exemplary structures and techniques known to those of ordinary skill in the art.

[0177] The disclosure herein describes non-limiting examples. For ease of description or explanation, when discussing various examples, the various parts of the disclosure herein utilize the terms "each," "each," or "all." Such usage of the terms "each," "each," or "all" is non-limiting. In other words, when the disclosure herein provides a description of "each," "each," or "all" specific objects or parts that are applied to some specific objects or parts, it should be understood that this is a non-limiting example, and it should also be understood that in various examples, it may be that such description is applicable to less than "each," "each," or "all" specific objects or parts in the specific object or part.

[0178] As used in this specification, the term "processor" may refer to substantially any computing processing unit or device, including but not limited to a single-core processor; a single processor with software multithreaded execution capability; a multi-core processor; a multi-core processor with software multithreaded execution capability; a multi-core processor with hardware multithreading technology; a parallel platform; and a parallel platform with distributed shared memory. In addition, a processor may refer to an integrated circuit, an application-specific integrated circuit (ASIC), a digital signal processor (DSP), a field programmable gate array (FPGA), a programmable logic controller (PLC), a complex programmable logic device (CPLD), discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. In addition, the processor may utilize nanoscale architectures (such as, but not limited to, transistors, switches, and gates based on molecules and quantum dots) in order to optimize space usage or enhance the performance of user equipment. The processor may also be implemented as a combination of computing processing units. In the present disclosure, terms such as "repository", "storage device", "data repository", "data storage device", "database", and substantially any other information storage component related to the operation and functionality of the component are used to refer to a "memory component", an entity embodied in a "memory", or a component including a memory. It should be understood that the memory or memory components described herein may be volatile memory or non-volatile memory, or may include both volatile memory and non-volatile memory. By way of illustration and not limitation, non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable ROM (EEPROM), flash memory, or non-volatile random access memory (RAM) (e.g., ferroelectric RAM (FeRAM)). For example, volatile memory may include RAM that can act as an external cache memory. By way of illustration and not limitation, RAM can be provided in a variety of forms, such as synchronous RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM), direct Rambus RAM (DRRAM), direct Rambus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM). In addition, the disclosed memory components of the system or computer-implemented method herein are intended to include, but are not limited to, including these and any other suitable types of memory.

[0179] What has been described above includes only examples of systems and computer-implemented methods. Of course, it is not possible to describe every conceivable combination of components or computer-implemented methods for the purposes of describing the present disclosure, but many other combinations and permutations of the present disclosure are possible. In addition, to the extent that the terms "including," "having," "having," and the like are used in the detailed description, claims, appendices, and drawings, such terms are intended to be inclusive in a manner similar to the term "comprising," as interpreted when "including" is used as a transitional word in a claim.

[0180] Descriptions of various embodiments have been given for illustrative purposes, but these descriptions are not intended to be exhaustive or limited to the disclosed embodiments. Many modifications and variations will be apparent without departing from the scope and spirit of the described embodiments. The terminology used herein is selected to best illustrate the principles of the embodiments, practical applications or technical improvements over technologies found on the market, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. A system, comprising: A processor (e.g., 108) that executes computer-executable components stored in a non-transitory computer-readable memory (e.g., 110), wherein the computer-executable components include: an access component (e.g., 112) that accesses a set of medical imaging interventions (e.g., 106) to be performed on a plurality of medical imaging scanners (e.g., 104); an allocating component (e.g., 114) that determines a recommended allocation (e.g., 404) based on a plurality of digital scanner twins (e.g., 402), each of the plurality of digital scanner twins comprising one or more cathode filament wear models (e.g., 402(1)(1), 402(1)(m), 402(n)(1), 402(n)(m)), the recommended allocation indicating how to allocate the set of medical imaging interventions among the plurality of medical imaging scanners; and A result component (e.g., 116) that allocates the set of medical imaging interventions among the plurality of medical imaging scanners based on the recommended allocation, wherein the recommended allocation indicates that a first medical imaging intervention in the set of medical imaging interventions is to be allocated to a first medical imaging scanner among the plurality of medical imaging scanners, and wherein the result component instructs the first medical imaging scanner to locally submit or prepare the first medical imaging intervention.

2. A system according to claim 1, wherein the allocation component simulates, on the multiple digital scanner twins, multiple different allocations of the set of medical imaging interventions among the multiple medical imaging scanners (e.g., 606), and wherein the recommended allocation is that allocation among the multiple different allocations that minimizes the total intra-scanner cathode filament wear difference indicated by the multiple digital scanner twins (e.g., based on 710).

3. A system according to claim 1, wherein the allocation component simulates, on the multiple digital scanner twins, multiple different allocations of the set of medical imaging interventions among the multiple medical imaging scanners (e.g., 606), and wherein the recommended allocation is that allocation among the multiple different allocations that maximizes the total inter-scanner cathode filament wear difference indicated by the multiple digital scanner twins (e.g., based on 720).

4. A system according to claim 1, wherein the allocation component simulates, on the multiple digital scanner twins, multiple different allocations of the set of medical imaging interventions among the multiple medical imaging scanners (e.g., 606), and wherein the recommended allocation is that of the multiple different allocations that minimizes the difference in cathode filament wear within the total position indicated by the multiple digital scanner twins (e.g., based on 726).

5. A system according to claim 1, wherein the allocation component determines the recommended allocation by executing a machine learning model (e.g., 804) on the set of medical imaging interventions and on the current time cathode filament wear state (e.g., 802) indicated by the multiple digital scanner twins.

6. The system of claim 1, wherein each of the plurality of digital scanner twins further comprises: one or more gantry motor wear models of a corresponding one of the plurality of medical imaging scanners; one or more signal brush wear models of a corresponding one of the plurality of medical imaging scanners; or One or more powered brush wear models for a corresponding one of the plurality of medical imaging scanners.

7. A system according to claim 1, wherein each medical imaging intervention indicates a corresponding medical imaging protocol to be performed on a corresponding medical patient (e.g., 106(1)(1)) and indicates a physical characteristic of the corresponding medical patient (e.g., 106(1)(2)).

8. A computer-implemented method, the computer-implemented method comprising: accessing, by a device operatively coupled to a processor (e.g., 108) (e.g., via 112), a set of medical imaging interventions (e.g., 106) to be performed on a plurality of medical imaging scanners (e.g., 104); determining, by the device (e.g., via 114) and based on a plurality of digital scanner twins (e.g., 402), a recommended allocation (e.g., 404), each of the plurality of digital scanner twins including one or more cathode filament wear models (e.g., 402(1)(1), 402(1)(m), 402(n)(1), 402(n)(m)) for a respective one of the plurality of medical imaging scanners, the recommended allocation indicating how to allocate the set of medical imaging interventions among the plurality of medical imaging scanners; as well as The set of medical imaging interventions is allocated by the device (e.g., via 116) among the multiple medical imaging scanners according to the recommended allocation, wherein the recommended allocation indicates that a first medical imaging intervention in the set of medical imaging interventions is to be allocated to a first medical imaging scanner among the multiple medical imaging scanners, and wherein the allocation includes instructing the first medical imaging scanner to locally submit or prepare the first medical imaging intervention by the device.

9. A computer-implemented method according to claim 8, wherein the device simulates, on the multiple digital scanner twins, multiple different allocations of the set of medical imaging interventions among the multiple medical imaging scanners (e.g., 606), and wherein the recommended allocation is that allocation among the multiple different allocations that minimizes the total intra-scanner cathode filament wear difference indicated by the multiple digital scanner twins (e.g., based on 710).

10. A computer-implemented method according to claim 8, wherein the device simulates, on the multiple digital scanner twins, multiple different allocations of the set of medical imaging interventions among the multiple medical imaging scanners (e.g., 606), and wherein the recommended allocation is that allocation among the multiple different allocations that maximizes the total inter-scanner cathode filament wear difference indicated by the multiple digital scanner twins (e.g., based on 720).

11. A computer-implemented method according to claim 8, wherein the device simulates, on the multiple digital scanner twins, multiple different allocations of the set of medical imaging interventions among the multiple medical imaging scanners (e.g., 606), and wherein the recommended allocation is that allocation among the multiple different allocations that minimizes the difference in cathode filament wear within the total position indicated by the multiple digital scanner twins (e.g., based on 726).

12. A computer-implemented method according to claim 8, wherein the device determines the recommended allocation by executing a machine learning model (e.g., 804) on the set of medical imaging interventions and on the current time cathode filament wear state (e.g., 802) indicated by the multiple digital scanner twins.

13. The computer-implemented method of claim 8, wherein each of the plurality of digital scanner twins further comprises: one or more gantry motor wear models of a corresponding one of the plurality of medical imaging scanners; one or more signal brush wear models of a corresponding one of the plurality of medical imaging scanners; or One or more powered brush wear models for a corresponding one of the plurality of medical imaging scanners.

14. A computer-implemented method according to claim 8, wherein each medical imaging intervention indicates a corresponding medical imaging protocol to be performed on a corresponding medical patient (e.g., 106(1)(1)) and indicates a physical characteristic of the corresponding medical patient (e.g., 106(1)(2)).

15. A computer program product for facilitating intelligent intervention allocation for a medical imaging scanner, the computer program product comprising a non-transitory computer readable memory (e.g., 110) having program instructions embodied therein, the program instructions executable by a processor (e.g., 108) to cause the processor to: accessing a set of medical imaging interventions (e.g., 106) to be performed on a plurality of medical imaging scanners (e.g., 104); determining a recommended allocation (e.g., 404) based on a plurality of digital scanner twins (e.g., 402), each of the plurality of digital scanner twins comprising one or more cathode filament wear models (e.g., 402(1)(1), 402(1)(m), 402(n)(1), 402(n)(m)) for a corresponding one of the plurality of medical imaging scanners, the recommended allocation indicating how to allocate the set of medical imaging interventions among the plurality of medical imaging scanners; and The set of medical imaging interventions is allocated among the plurality of medical imaging scanners according to the recommended allocation, wherein the recommended allocation indicates that a first medical imaging intervention in the set of medical imaging interventions is to be allocated to a first medical imaging scanner among the plurality of medical imaging scanners, and wherein the processor instructs the first medical imaging scanner to locally submit or prepare the first medical imaging intervention.

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