Distributed medical image acquisition

Abstract

The invention discloses a framework for distributed medical image acquisition. An optimal configuration of one or more mobile imaging systems to address clinical tasks is determined. One or more mobile imaging systems may be dispatched to perform medical image acquisition of a patient according to an optimal configuration to generate medical image data. Image reconstruction may then be performed based on the medical image data.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims the benefit of U.S. Provisional Application No. 63 / 478,514, filed January 5, 2023, entitled “Collaborative SPECT: Operation and Optimization of Multimodal Workflows Utilizing Mobile SPECT Units,” which is incorporated herein by reference. Technical Field

[0003] The present disclosure relates generally to medical imaging, and more particularly to a mobile imaging system for distributed medical image acquisition. Background Art

[0004] The field of medical imaging has made significant progress since X-rays were first used to identify anatomical abnormalities. Medical imaging hardware has evolved in the form of newer machines such as medical resonance imaging (MRI) scanners and computed axial tomography (CAT) scanners. Digital medical images are constructed using the raw image data obtained from such scanners.

[0005] Typically, only one imaging system is assigned to an imaging suite, used to image one patient at a time. Throughput cannot be increased beyond the imaging performance of a specific imaging system. Furthermore, technology updates to address imaging performance issues (i.e., "forklift" upgrades) require replacing the imaging system, which often incurs construction costs and delays. This approach requires an inflexible business model and is suboptimal from an operational perspective, as most imaging systems are idle, with utilization rates of less than 50% on any given day.

[0006] The patient's injection is performed in the injection room, and the patient then needs to go to the imaging suite. The patient can be moved or transported to the imaging suite, imaged, and released or transferred back to the workstation. This imaging process is performed for each imaging modality. This is a costly logistical problem because the patient must be arranged and transported from one room to another. In addition, the design of the imaging system is not optimal for all imaging tasks. It is often designed as a general purpose or dedicated system, rather than designed to work with other imaging systems. When the imaging system needs to be shut down for maintenance service, this service usually occurs in the "patient" space and therefore interrupts the workflow.

[0007] Currently, there is no solution other than taking the patient to a separate imaging system, even if that means going to another hospital. Small footprint imaging system designs can address the footprint issue, but these imaging systems with smaller footprints typically include dedicated organ cameras or specialized cameras (e.g., thyroid scintigraphy) and are not designed for tomography. Other designs with ultra-small footprints and portability are designed as gamma probes with very small imaging areas to assist in procedures such as sentinel lymph node biopsy. Summary of the Invention

[0008] This paper describes a framework for distributed medical image acquisition. An optimal configuration of one or more mobile imaging systems is determined to solve a clinical task. Based on the optimal configuration, the one or more mobile imaging systems can be dispatched to perform medical image acquisition on a patient to generate medical image data. Image reconstruction can then be performed based on the medical image data. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] A more complete understanding of the present disclosure and its many attendant aspects will be readily obtained as the present disclosure and its many attendant aspects become better understood by reference to the following detailed description when considered in conjunction with the accompanying drawings.

[0010] Figure 1 is a block diagram illustrating an exemplary mobile imaging system;

[0011] Figure 2 An exemplary method of distributed image acquisition is shown;

[0012] Figure 3 An exemplary clinical use case is shown;

[0013] Figure 4 Depicts an exemplary situation in which a mobile imaging system is moved to a location where typical single photon emission computed tomography (SPECT) imaging is not possible; and

[0014] Figure 5 An exemplary use case is depicted in which a mobile imaging system assembles itself for optimal axial coverage. DETAILED DESCRIPTION

[0015] In the following description, many specific details are set forth, such as examples of specific components, devices, methods, etc., in order to provide a thorough understanding of the embodiments of the present framework. However, it will be apparent to those skilled in the art that these specific details are not required to practice the embodiments of the present framework. In other instances, in order to avoid unnecessarily obscuring the embodiments of the present framework, well-known materials or methods are not described in detail. Although the present framework is susceptible to various modifications and alternative forms, specific embodiments thereof are shown by way of example in the accompanying drawings and will be described in detail herein. However, it should be understood that there is no intention to limit the present invention to the specific forms disclosed, but rather, the intention is to cover all modifications, equivalents, and alternatives that fall within the spirit and scope of the present invention. In addition, for ease of understanding, certain method steps are described as separate steps; however, these separately described steps should not be construed as necessarily dependent on the order in which they are performed. Nouns and pronouns relating to people in this patent application generally do not limit specific genders.

[0016] Unless otherwise indicated, it will be apparent from the following discussion that it will be understood that terms such as "segment," "generate," "register," "determine," "align," "locate," "process," "calculate," "select," "estimate," "detect," "track," and the like may refer to actions and processes of a computer system or similar electronic computing device that manipulate and transform data represented as physical (e.g., electronic) quantities within the computer system's registers and memories into other data similarly represented as physical quantities within the computer system's memories or registers or other such information storage, transmission, or display devices. The embodiments of the methods described herein may be implemented using computer software. If written in a programming language conforming to a recognized standard, the sequences of instructions designed to implement the methods may be compiled for execution on a variety of hardware platforms and interfaced with a variety of operating systems. Furthermore, the embodiments of the present framework are not described with reference to any particular programming language. It will be understood that a variety of programming languages ​​may be used.

[0017] A framework for distributed medical image acquisition is presented herein. According to one aspect, a mobile imaging system with specific imaging parameters is provided for facilitating distributed image acquisition. In some embodiments, one or more of the mobile imaging systems are autonomously moved to a patient's bedside for performing medical image acquisition of the patient. In addition, the mobile imaging systems can collaborate with each other and be assembled to optimally handle clinical tasks. "Collaboration" as used herein generally refers to mobile imaging systems working together and sharing data (e.g., sensor data, medical image data) to achieve a common goal of optimizing the completion of clinical tasks. The mobile imaging systems can be assembled using a common communication protocol. One of the mobile imaging systems can be designated as a reference system to align spatial and temporal markers, and to acquire and reconstruct image data as an image volume.

[0018] The framework advantageously provides distributed image acquisition that allows for extra-modal and / or intra-modal information to optimally assess the patient. The framework brings clinical workflow and operational changes to clinical workflow to optimize utilization and use of medical imaging equipment. Distributed emission acquisition enables optimal evaluation and utilization by simultaneously evaluating the gamma ray spectrum from the patient with dedicated specialized units. In addition, when one mobile imaging system is down for maintenance, other alternative systems can be dispatched to provide medical imaging services. Maintenance of the mobile imaging system can occur outside of the "patient" space (e.g., in a storage area), thereby minimizing disruption to the workflow. These and other exemplary advantages and features will be described in more detail in the description that follows.

[0019] Figure 1 1 is a block diagram illustrating an exemplary mobile imaging system 101 for implementing the framework as described herein. In some embodiments, the mobile imaging system 101 operates as a standalone device. In other embodiments, the mobile imaging system 101 can be connected to other machines, such as other mobile imaging systems 101, via the communication module 114. In a networked deployment, the mobile imaging system 101 can operate as a peer machine in a peer-to-peer (or distributed) network environment. Any number of mobile imaging systems 101 can be provided (e.g., two, three, or more). Each mobile imaging system 101 can move and / or operate independently of the other mobile imaging systems 101.

[0020] The mobile imaging system 101 may include a processor device or central processing unit (CPU) 104 coupled to one or more non-transitory computer-readable media 105 (e.g., computer storage or memory devices), an input-output device 108 (e.g., a monitor, mouse, touchpad, or keyboard), a medical imaging unit 110, a transport unit 112, and a communication unit 114 via an input-output interface 121. The mobile imaging system 101 may also include support circuits, such as cache, a power supply or battery, a clock circuit, and a communication bus (not shown). The mobile imaging system 101 may be charged at a docking station, such as in a storage location and / or at charging docks located throughout the hospital. Wired power may also be provided for operation during scanning. Various other peripheral devices, such as additional data storage devices and printing devices, may also be connected to the mobile imaging system 101.

[0021] The present technology can be implemented in various forms of hardware, software, firmware, dedicated processors, or a combination thereof, or as part of microinstruction code, or as part of an application or software product, or a combination thereof, which is executed via an operating system. In some embodiments, the technology described herein is implemented as computer-readable program code tangibly contained in one or more non-transitory computer-readable media 105. Specifically, the technology can be implemented by a processing module 107. The non-transitory computer-readable medium 105 may include random access memory (RAM), read-only memory (ROM), magnetic floppy disk, flash memory, and other types of memory, or a combination thereof. The computer-readable program code is executed by a processor device 104 to process data collected by, for example, a medical imaging unit 110. The computer-readable program code is not intended to be limited to any particular programming language and its implementation. It should be understood that the teachings of the disclosure contained herein can be implemented using various programming languages ​​and their encodings. The same or different computer-readable media 105 can be used to store databases, including but not limited to image datasets of subjects, knowledge bases, individual subject data, medical records, diagnostic reports (or documents), or a combination thereof.

[0022] The medical imaging unit 110 acquires medical image data. The medical imaging unit 110 may be a tomography scanner (e.g., a nuclear medicine scanner) for acquiring, collecting, and / or storing such medical image data. The medical imaging unit 110 may be a single photon emission computed tomography (SPECT), positron emission tomography (PET), computed tomography (CT), or other tomography (e.g., ultrasound or surgical) imaging system. It may provide diagnostic, theranostic, dosimetric, or surgical support imaging.

[0023] In some embodiments, the medical imaging unit 110 is a SPECT imaging system that includes a detector connected to, for example, an arm via an articulated coupling. The detector can be a gamma ray detector that produces tomographic images from a fixed position. The detector can include a flat or curved plate. Current examples of such systems include parallel hole collimators, rotating acceptance angle collimators, and rotating slit / slat collimators, in which the aperture channels in the collimator are arranged so that the viewing angle of each row varies relative to the radiation source, and rotating slit / slat collimators used in conjunction with a gamma camera having a scintillation detector formed by a stack of scintillation rod detectors. Other types of imaging systems are also useful. For example, attenuation mode coded or non-attenuation mode coded, multiplexed or non-multiplexed, time coded or non-time coded imaging systems are also useful.

[0024] When the mobile imaging system 101 is far from the patient, medical image data with a larger field of view of the patient and lower resolution can be acquired. As the mobile imaging system 101 approaches the patient, the medical imaging unit 110 can acquire medical image data of the patient with increased resolution but a reduced field of view. As the mobile imaging system 101 approaches the patient, image processing and / or reconstruction can be performed based on the acquired medical image data, thereby allowing the mobile imaging system 101 to display a tomographic image that is "zoomed in" to the volume of interest.

[0025] The transport unit 112 is used to automatically or semi-automatically position the mobile imaging system 101 near the patient to collect medical image data. In some embodiments, the transport unit 112 includes at least one mobile structure (e.g., wheels, cylinders, rollers, legs) for freely moving the mobile imaging system 101, a drive module for driving at least one mobile structure, and a sensor module. The drive module may include a motor (e.g., an electric motor) that can be driven by an operator or self-driven in response to a signal initiated by the processing module 107. The sensor module includes one or more sensors for determining the position and / or direction of the mobile imaging system 101, detecting obstacles to avoid collisions, and / or detecting the position of the patient. The one or more sensors may include, for example, a camera, a distance sensor, an ultrasonic sensor, an infrared sensor, a global positioning sensor, or a combination thereof. See, for example, U.S. Patent Publication No. US20210219927A1 and U.S. Patent Publication No. US20220015726A1, which are incorporated herein by reference.

[0026] The communication unit 114 enables the mobile imaging system 101 to communicate with other mobile imaging systems and / or other systems. The mobile imaging system 101 can communicate with other mobile imaging systems and / or other systems to, for example, cooperate and / or arrange itself to obtain optimal coverage. The communication unit 114 may include a wireless signal transceiver that transmits signals using a common communication protocol, such as Global System for Mobile Communications (GSM), WIFI, Bluetooth, Zigbee, LoRa, and TCP / IP. Other types of communication protocols are also useful. Wireless communication is used to assemble the mobile imaging system 101 in an optimal configuration to solve the clinical task. One of the mobile imaging systems 101 can be designated as a reference system to provide a reference position and / or clock, with which the other mobile imaging systems 101 are aligned in space and / or time.

[0027] It should also be understood that because some of the constituent system components and method steps depicted in the figures can be implemented in software, the actual connections between the system components (or process steps) can vary depending on how the present framework is programmed. Given the teachings provided herein, one of ordinary skill in the relevant art will be able to envision these and similar implementations or configurations of the present framework.

[0028] Figure 2 An exemplary method 200 for distributed image acquisition is shown. It should be understood that the steps of method 200 may be performed in the order shown or in a different order. Additional, different, or fewer steps may also be provided. Furthermore, method 200 may be used Figure 1 The processing module 107 may be implemented in at least one mobile imaging system 101, a different system, or a combination thereof. For example, the processing module 107 may be located in the mobile imaging system 101 or in another computer system used as a control system.

[0029] At 202, the processing module 107 receives current position data of a patient. The current position data of the patient represents the patient's current position. The current position data of the patient may be acquired by one or more sensors in a sensor module of a transport unit 112 in one or more mobile imaging systems 101. The current position data of the patient may be provided relative to a floor plan of the medical facility. The floor plan may be predefined or adaptively learned based on the sensor data from the sensor module.

[0030] At 204, the processing module 107 receives current position data and imaging parameters of the one or more mobile imaging systems 101. The current position data of the one or more mobile imaging systems 101 represents the current position(s) of the one or more mobile imaging systems 101. The current position data of the one or more mobile imaging systems 101 may be acquired by the sensor module and transmitted via the communication unit 114 of each of the one or more mobile imaging systems 101. The current position data of the one or more mobile imaging systems 101 may be provided relative to a floor plan of the medical facility.

[0031] The imaging parameters describe properties of the medical imaging unit 110. The imaging parameters of the one or more mobile imaging systems 101 can be retrieved from the non-transitory computer-readable medium 105. Alternatively, the imaging parameters can be transmitted to the processing module 107 via the communication unit 114 of each of the one or more mobile imaging systems 101. The imaging parameters include, but are not limited to, field of view (FOV) (e.g., axial FOV), energy resolution, energy range, spatial resolution, detection efficiency, collimator characteristics, or a combination thereof.

[0032] At 206, the processing module 107 determines an optimal configuration of the one or more mobile imaging systems 101 to address the clinical task. The optimal configuration can be determined, for example, to optimally capture emission data emitted by a radioisotope located within a specific location on the patient. The optimal configuration can be determined based on the patient's current position data, the current position data of the one or more mobile imaging systems 101, imaging parameters of the one or more mobile imaging systems 101, or a combination thereof. Other extra-modality or intra-modality information can also be used.

[0033] The optimal configuration may specify the number of mobile imaging systems 101 to be dispatched. The optimal configuration may also specify the positions, imaging parameter values, and / or scan times of the one or more mobile imaging systems 101 to be assembled in order to optimally complete the clinical task. The patient's current position data may be used to guide the one or more mobile imaging systems 101 to move toward the patient. Once the one or more mobile imaging systems 101 are moved next to the patient, medical image acquisition of the patient may be optimized. Patient motion during image acquisition may also be compensated for. The optimal configuration advantageously enables the one or more mobile imaging systems 101 to be assembled at the required location when needed and to operate as a single unit or jointly in a fleet to allow more efficient body coverage when needed. The optimal configuration may be generated, for example, by machine learning techniques (e.g., deep convolutional networks).

[0034] The assembly of multiple mobile imaging systems 101, each with different or similar imaging parameters, facilitates the creation of a loose configuration that is adjusted to suit the clinical task. For example, if the clinical task requires only an expansion of the axial field of view (FOV_y), the processing module 107 can determine an optimal configuration of k additional mobile imaging systems 101 (where k is a positive integer) such that the sum of the axial fields of view of the mobile imaging systems 101 (i.e., (k+1)*FOV_y) is equal to or greater than the patient's desired axial FOV. The multiple mobile imaging systems 101 can be positioned around the patient to minimize overlap between the axial fields of view.

[0035] As another example, if a first group of mobile imaging systems 101 performs imaging at, for example, less than 400 keV, and a second group of mobile imaging systems 101 performs imaging at, for example, greater than 511 keV, the processing module 107 can determine an optimal configuration in which one or more units are selected from each of the first and second groups. The optimal configuration can specify the scanning times and / or positions of the selected mobile imaging systems 101 on a plan view so that they acquire medical image data simultaneously or sequentially close to the patient or at a specified distance from the patient to image the patient's entire body. The imaging plates can optimally "hug" the contours of the patient's body to achieve the best possible resolution, while also encompassing as much as possible in azimuth to achieve high sensitivity in the corresponding FOV.

[0036] As yet another example, one mobile imaging system 101 can cover a very wide energy range, such as 30 keV to 3000 MeV (e.g., greater than 511 keV), using Compton imaging. Utilization of such a mobile imaging system 101 allows only one unit to suffice for the site, while multiple mobile imaging systems 101 covering the energy range within 200-400 keV, and even more mobile imaging systems 101 covering <3000 keV, can also be available at the site given the needs and operations of a particular site. For example, four mobile imaging systems 101 can cover 30-400 keV, while two mobile imaging systems 101 cover 511 keV, and one mobile imaging system 101 covers the 511-3000 keV energy range.

[0037] As yet another example, the mobile imaging system 101 may be used to perform dynamic four-dimensional (4D) image formation such that image formation is performed in a spatio-temporal manner. The medical imaging unit 110 of such a mobile imaging system 101 may use, for example, a non-multiplexed system, a multi-channel collimation system, a parallel hole collimator, a multiplexed pinhole array, a coded aperture, or a time-coded aperture, or a combination thereof.

[0038] At 208, the processing module 107 dispatches one or more mobile imaging systems 101 to perform medical image acquisition of the patient according to the optimal configuration to generate medical image data. The processing module 107 can activate the transport unit 112 to autonomously (or semi-autonomously) move the one or more mobile imaging systems 101 to the corresponding desired locations indicated by the optimal configuration. The processing module 107 can then activate the medical imaging unit 110 to perform medical image acquisition.

[0039] The transport unit 112 may include a drive module and a sensor module. The drive module may include a motor (e.g., an electric motor) that can be driven by an operator or autonomously driven to a position indicated by the optimal configuration. In some embodiments, in response to sensor data from the sensor module, the processing module 107 can activate the drive module to urgently move the mobile imaging system 101 to quickly clear a space while avoiding obstacles. In other embodiments, the processing module 107 can activate a service mode (e.g., maintenance, calibration) when the mobile imaging system 101 is in storage (e.g., a garage space) and / or charging at a power source (e.g., a charging dock). Service can be performed without interfering with patient operations.

[0040] At 210, the processing module 107 performs image reconstruction based on the medical image data. The processing module 107 reconstructs a tomographic image of the internal region of interest from the medical image data acquired by one or more mobile imaging units 101. CT, PET, SPECT, or other types of reconstruction are used. In an iterative optimization process, the location of the emission or the attenuation at that location is determined from the detected signal. For PET or SPECT, the tomographic reconstruction is used to reconstruct the location of the radioisotope. For CT, the tomographic reconstruction is used to reconstruct the attenuation at the location of the entire region of interest.

[0041] In some embodiments, medical image data from multiple mobile imaging systems 101 are combined to reconstruct a volume of interest that is larger than depicted in each corresponding image. The medical image data can be combined by, for example, aligning (or registering) pixels or voxels of one medical image with corresponding pixels or voxels representing the same volume of interest in another medical image.

[0042] The processing module 107 renders the reconstructed tomographic image for display, for example, on a display screen. Alternatively, the image is printed or projected. The image can be combined with views or other images in an augmented reality display, a virtual display, or a hybrid display. A registration algorithm can be used to integrate previous images (e.g., CT or MR images) from previous scans with the reconstructed tomographic image.

[0043] Figure 3An exemplary clinical use case is shown, in which mobile imaging systems (101, 101a-b) can be configured in a single or multi-unit configuration to handle various types of beds 302a-b. More specifically, in the single configuration 301a, bed 302a supports imaging subject 303 in a flat configuration, while bed 302b supports imaging subject 303 in an inclined configuration. Mobile imaging system 101 can be moved to a desired position relative to imaging subject 303 via its transport unit 112. The desired position can be a position according to an optimal configuration determined by processing module 107. Detectors 310a-b can be moved to a desired height and / or angle position via articulated arms 312 to facilitate selective positioning relative to imaging subject 303. Detectors 310a-b can include multiple cameras that can be changed from a flat-plate configuration (detector 310a) to a curved-plate configuration (detector 310b) to optimize image acquisition. In the multi-unit configuration 301b, two mobile imaging systems 101a-b are dispatched to either side of bed 302a in tandem operation.

[0044] Figure 4 An exemplary scenario 400 is depicted in which a mobile imaging system 101 is moved to a site where typical SPECT imaging is not possible. Typical SPECT imaging requires a large, fixed, gantry-based system to be permanently installed in a large, dedicated space. In this case, the site does not have sufficient space to accommodate such a large, gantry-based system or even integrate it with a conventional patient handling system (PHS) 402. In this case, the PHS 402 is movable in all axes. The mobile imaging system 101 can be dispatched to provide SPECT image acquisition services for any subject of interest 303 without requiring such a large space or permanent installation.

[0045] Figure 5 An exemplary use case is depicted in which mobile imaging systems 101a-d self-assemble to achieve optimal axial coverage of an object of interest on a bed 302. The mobile imaging systems 101a-d can exchange information via, for example, Bluetooth or other wireless communication protocols. In this optimal configuration, two mobile imaging systems 101a and 101c are assigned to one side of the bed 302, and two mobile imaging systems 101b and 101d are assigned to the other side of the bed 302, so that the overlap between their axial coverage is minimized and the desired axial coverage is achieved. It should be understood that other optimal configurations are also possible, such as two mobile imaging systems covering the same axial range to accelerate the acquisition of the field of view (FOV).

[0046] Furthermore, the mobile imaging systems 101a-d can have different complementary imaging parameters (e.g., different image formation characteristics), thereby allowing for an optimal assembly of units to address a desired task. For example, one mobile imaging system can perform imaging at high energies (e.g., greater than 511 keV) using Compton imaging at a distance from the subject of interest, while other mobile imaging systems can be closer to the patient, with specific image quality characteristics that best address clinical needs.

[0047] Although the present framework has been described in detail with reference to exemplary embodiments, it will be understood by those skilled in the art that various modifications and substitutions may be made thereto without departing from the spirit and scope of the invention as set forth in the appended claims. For example, elements and / or features of different exemplary embodiments may be combined with each other and / or substituted for each other within the scope of this disclosure and the appended claims.

Claims

1. An image acquisition method, comprising: receiving first current position data of a patient; receiving second current position data and imaging parameters of one or more mobile imaging systems; determining an optimal configuration of the one or more mobile imaging systems to solve a clinical task based on the first current position data, the second current position data, and the imaging parameters; dispatching the one or more mobile imaging systems to perform medical image acquisition of a patient according to the optimal configuration, thereby generating medical image data; and Image reconstruction is performed based on the medical image data. 2 . The method of claim 1 , further comprising acquiring first and second current position data using one or more sensors in the one or more mobile imaging systems. 3 . The method of claim 1 , further comprising wirelessly transmitting the first and second current position data and the imaging parameters via a communication unit in the one or more mobile imaging systems.

4. The method of claim 1, wherein the imaging parameters include field of view (FOV), energy resolution, energy range, spatial resolution, detection efficiency, collimator characteristics, or a combination thereof.

5. The method of claim 1, wherein determining the optimal configuration of the one or more mobile imaging systems comprises determining at least the number, location, imaging parameter value, scanning time, or a combination thereof of the one or more mobile imaging systems.

6. The method of claim 1 , wherein determining the optimal configuration of the one or more mobile imaging systems comprises determining a configuration of a plurality of mobile imaging systems, wherein a sum of the axial fields of view of the plurality of mobile imaging systems equals or exceeds a desired axial field of view.

7. The method of claim 1, wherein determining an optimal configuration of the one or more mobile imaging systems comprises determining a configuration of multiple mobile imaging systems that perform imaging at different energies.

8. The method of claim 7, wherein at least one of the plurality of mobile imaging systems performs imaging at less than 400 keV and at least another of the plurality of mobile imaging systems performs imaging at greater than 511 keV.

9. The method of claim 1, wherein determining an optimal configuration of the one or more mobile imaging systems comprises determining a configuration of a plurality of mobile imaging systems positioned around a body contour of the patient.

10. The method of claim 1, wherein dispatching the one or more mobile imaging systems according to the optimal configuration comprises activating a transport unit of the one or more mobile imaging systems to autonomously move the one or more mobile imaging systems.

11. The method of claim 1 , wherein performing image reconstruction comprises combining tomographic images from a plurality of mobile imaging systems.

12. A mobile imaging system comprising: Medical Imaging Unit; transport unit; a non-transitory storage device for storing computer-readable program code; and a processor device in communication with the non-transitory memory device, the medical imaging unit, and the transport unit, the processor device operative with the computer-readable program code to perform steps comprising: Determine the best configuration to solve the clinical task, Activate the transport unit to move according to the optimal configuration, and The medical imaging unit is activated to perform medical image acquisition of a patient to generate medical image data.

13. The mobile imaging system of claim 12, wherein the medical imaging unit comprises a single photon emission computed tomography (SPECT) imaging system.

14. The mobile imaging system of claim 12, wherein the transport unit comprises: at least one mobile structure; a driving module for driving the at least one mobile structure; and Sensor module for determining position data.

15. The mobile imaging system of claim 12, further comprising a communication unit in communication with the processor device, the communication unit enabling communication with other mobile imaging systems.

16. The mobile imaging system of claim 15, wherein the processor device is operative with the computer readable program code to assemble other mobile imaging systems according to the optimal configuration via the communication unit.

17. The mobile imaging system of claim 16, wherein the processor device is operative with the computer readable program code to determine an optimal configuration based on imaging parameters of the mobile imaging system and other mobile imaging systems.

18. The mobile imaging system of claim 17, wherein the imaging parameters include field of view (FOV), energy resolution, energy range, spatial resolution, detection efficiency, collimator characteristics, or a combination thereof.

19. The mobile imaging system of claim 12, wherein the processor device is operative with the computer readable program code to determine an optimal configuration by determining at least a number of other mobile imaging systems to dispatch.

20. One or more non-transitory computer-readable media containing instructions executable by a machine to perform operations comprising: determining an optimal configuration of one or more mobile imaging systems to address a clinical task; dispatching the one or more mobile imaging systems to perform medical image acquisition of a patient to generate medical image data according to the optimal configuration; and Image reconstruction is performed based on the medical image data.

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