Methods and apparatus for multi-characteristic medical imaging and image reconstruction

Abstract

Systems and methods for capturing scans using varying detection characteristics, and for reconstructing medical images based on the captured scans, are disclosed. In some examples, an image scanning system includes a first detector ring with first detection characteristics, and a second detector ring with second detection characteristics that differ from the first detection characteristics. For example, the first detector ring may be configured to capture scans with high spatial resolution, and the second detector ring may be configured to capture scans with high sensitivity. The image scanning system can capture a first scan with the first detector ring, and a second scan with the second detector ring. Further, the image scanning system generates first detection data based on the first scan, and second detection data based on the second scan. The image scanning system transmits the first detection data and the second detection data for image reconstruction.

Description

Docket No.: 2024P21678WOMETHODS AND APPARATUS FOR MULTI-CHARACTERISTIC MEDICAL IMAGING AND IMAGE RECONSTRUCTION FIELD

[0001] Aspects of the present disclosure relate in general to medical diagnostic systems and, more particularly, to capturing and reconstructing images from nuclear imaging systems for diagnostic and reporting purposes.BACKGROUND

[0002] Nuclear imaging systems can employ various technologies to capture images. For example, some nuclear imaging systems employ positron emission tomography (PET) or single-photon emission computed tomography (SPECT) to capture anatomical images. PET is a nuclear medicine imaging technique that produces tomographic images representing the distribution of positron emitting isotopes within a body, while SPECT relies on the detection of gamma rays to produce tomographic images representing the distribution of radioactive tracer molecules within a body. Some nuclear imaging systems combine images from PET and a co-modality, such as computed tomography (CT) or Magnetic Resonance Imaging (MRI). CT is an imaging technique that uses x-rays to produce anatomical images. Magnetic Resonance Imaging (MRI) is an imaging technique that uses magnetic fields and radio waves to generate anatomical and functional images, and may also be used as a comodality. These nuclear imaging systems can combine images from PET and comodality scanners during an image fusion process to produce images that show information from both the PET scan and the co-modality scan (e.g., PET / CT systems). Moreover, the nuclear imaging systems may generate an attenuation map that can be used to correct the PET measurement data during image reconstruction.

[0003] More recently, long-axial field-of-view (LAFOV) systems, such as LAFOV PET / CT systems, allow for wider scans, such as full body scans, in a single field of view. These LAFOV systems include similar rings of detectors that can capture a scan of a patient. Each detector ring is essentially a replica of each other. For example, each detector ring includes the same hardware, such as the same number of similarly configured detector elements (e.g., PET scintillation detectors). In other words, theDocket No.: 2024P21678WOdetector rings share the same detection characteristics (e.g., time-of-flight, spatial resolution, sensitivity, etc.). There are opportunities to address deficiencies in nuclear imaging systems.SUMMARY

[0004] Systems and methods for capturing scans using detector rings of an image scanning system with varying detection characteristics, and for reconstructing medical images based on the captured scans, are disclosed.

[0005] In some embodiments, a method by an image scanning system includes capturing a first scan by a first detector ring, the first detector ring configured to have first detection characteristics. The method also includes capturing a second scan by a second detector ring, the second detector ring configured to have second detection characteristics, wherein the second detection characteristics differ from the first detection characteristics.

[0006] In some embodiments, an image scanning system includes a first detector ring configured to have first detection characteristics, and a second detector ring configured to have second detection characteristics, wherein the second detection characteristics differ from the first detection characteristics.

[0007] In some embodiments, a method by an image scanning system includes capturing a scan by a detector ring that includes at least a first detector element and a second detector element. The first detector element has first detection characteristics, and the second detector element has second detection characteristics, wherein the second detection characteristics differ from the first detection characteristics.

[0008] In some embodiments, an image scanning system includes a detector ring including at least a first detector element and a second detector element. The first detector element has first detection characteristics, and the second detector element has second detection characteristics, wherein the second detection characteristics differ from the first detection characteristics.Docket No.: 2024P21678WO

[0009] In some embodiments, a computer-implemented method includes receiving a first scan captured by a first detector ring of an image scanning system, the first detector ring having first detection characteristics. The method also includes receiving a second scan captured by a second detector ring of the image scanning system, the second detector ring having second detection characteristics, wherein the second detection characteristics differ from the first detection characteristics. Further, the method includes generating first detection data based on the first scan and second detection data based on the second scan. The method also includes storing the first detection data and the second detection data within a memory device.

[0010] In some embodiments, a non-transitory computer readable medium stores instructions that, when executed by at least one processor, cause the at least one processor to perform operations including receiving a first scan captured by a first detector ring of an image scanning system, the first detector ring having first detection characteristics. The operations also include receiving a second scan captured by a second detector ring of the image scanning system, the second detector ring having second detection characteristics, wherein the second detection characteristics differ from the detection characteristics. Further, the operations include generating first detection data based on the first scan and second detection data based on the second scan. The operations also include storing the first detection data and the second detection data within a memory device.

[0011] In some embodiments, an image scanning system includes a first detector ring, a second detector ring, a memory device, and at least one processor communicatively coupled the memory device. The at least one processor is configured to receive a first scan captured by the first detector ring, the first detector ring having first detection characteristics. The at least one processor is also configured to receive a second scan captured by the second detector ring, the second detector ring having second detection characteristics, wherein the second detection characteristics differ from the first detection characteristics. Further, the at least one processor is configured to generate first detection data based on the first scan and second detection data basedDocket No.: 2024P21678WOon the second scan. The at least one processor is configured to store the first detection data and the second detection data within the memory device.BRIEF DESCRIPTION OF THE DRAWINGS

[0012] The following will be apparent from elements of the figures, which are provided for illustrative purposes and are not necessarily drawn to scale.

[0013] FIG. 1 illustrates a nuclear imaging system, in accordance with some embodiments.

[0014] FIG. 2 illustrates a block diagram of an example computing device that can perform one or more of the functions described herein, in accordance with some embodiments.

[0015] FIG. 3 illustrates a nuclear imaging system, in accordance with some embodiments.

[0016] FIGS. 4A and 4B illustrate exemplary portions of a nuclear image scanning system, in accordance with some embodiments.

[0017] FIG. 5 is a flowchart of an example method of generating detection data based on varying characteristics of an image scanning system, and reconstructing a corresponding image, in accordance with some embodiments.

[0018] FIG. 6 is a flowchart of an example method of capturing scans using an image scanning system with detector rings of varying characteristics, in accordance with some embodiments.DETAILED DESCRIPTION

[0019] This description of the exemplary embodiments is intended to be read in connection with the accompanying drawings, which are to be considered part of the entire written description. Independent of the grammatical term usage, individuals with male, female, or other gender identities are included within the term.Docket No.: 2024P21678WO

[0020] The exemplary embodiments are described with respect to the claimed systems as well as with respect to the claimed methods. Furthermore, the exemplary embodiments are described with respect to methods and systems for image reconstruction, as well as with respect to methods and systems for training functions used for image reconstruction. Features, advantages, or alternative embodiments herein can be assigned to the other claimed objects and vice versa. For example, claims for the providing systems can be improved with features described or claimed in the context of the methods, and vice versa. In addition, the functional features of described or claimed methods are embodied by objective units of a providing system. Similarly, claims for methods and systems for training image reconstruction functions can be improved with features described or claimed in context of the methods and systems for image reconstruction, and vice versa.

[0021] Various embodiments of the present disclosure can employ machine learning methods or processes to provide clinical information from nuclear imaging systems. For example, the embodiments can employ machine learning methods or processes to reconstruct images based on captured measurement data, and provide the reconstructed images for clinical diagnosis. In some embodiments, machine learning methods or processes are trained to improve the reconstruction of images.

[0022] Long-axial field-of-view (LAFOV) positron emission tomography (PET) imaging systems can include a scanner with multiple detector rings (e.g., in the axial direction). Each detector ring can include multiple detector electronic assemblies (DEAs), with each DEA (e.g., detector module) including various detection elements. Each detection element may house scintillation crystals that detect emitted gamma rays from a scanned subject. When a crystal detects an event, the PET imaging system generates one or more signals characterizing the detected event. For example, the PET imaging system can generate an energy signal characterizing a detected energy level (e.g., energy deposition), a first position signal characterizing a first dimension position (e.g., X coordinate) of the detecting crystal, and a second position signal characterizing a second dimension position (e.g., Y coordinate) of the detected crystal.Docket No.: 2024P21678WOThe PET imaging system may also generate a time signal characterizing a detection time of the detected event.

[0023] In contrast to conventional imaging systems (e.g., LAFOV imaging systems), the image scanning systems described herein can include detector rings that have varying detection characteristics. For example, detector rings include DEAs and other electronics that determine the performance of scans, such as time-of-flight (TOF), spatial resolution, and detector sensitivity, among other detection characteristics. The image scanning systems described herein may provide at least one detector ring with a corresponding set of detection characteristics, and at least another detector ring with a differing set of detection characteristics. For instance, at least one detector ring (e.g., a high spatial resolution (HR) detector ring) may be optimized to deliver high spatial resolution detection data. These detector rings can include, for example, smaller crystals, additional detector elements (e.g., within the DEAs), additional DEAs, depth-of-interaction (DOI) information and processes, and / or dedicated scintillator material. In contrast, one or more other detector rings may be optimized to exhibit high sensitivity. These other detector rings can include, for example, larger crystals, lower noise electronics, and a lower number of detector elements and / or DEAs. As such, the image scanning systems described herein may spatially vary certain detection characteristics among detector rings. In some examples, the image scanning systems described herein vary the detection characteristics with a detector ring. For instance, a detector ring may have DEAs that have varying detection characteristics. In other examples, detection elements of a DEA may have varying detection characteristics. Table 1 below illustrates various detection characteristics, along with possible detector ring modifications to achieve the various detection characteristics.Distinguishing Characteristics of a Detector Ring Possible Detector Ring ModificationsDocket No.: 2024P21678WO

[0024] As an example, a detection element may include scintillation crystals of a different size (e.g., height, width) from scintillation crystals of another detection element. Based on the size of the crystals, a detection element can provide detections at a corresponding resolution. For example, a high sensitivity DEA may include detection elements with crystals that are 3.2 by 3.2 mm. These detector elements may be expected to have a detection resolution of 3.6 mm. A high spatial resolution DEA may include detector elements with crystals measuring 1.6 mm by 1.6 mm. These detector elements may be expected to have a detection resolution of 2.8 mm.

[0025] In some examples, the DEAs of a detector ring are configured to scan with a high spatial resolution (e.g., as described in Table 1), while the DEAs of another detector ring (e.g., an adjacent detector ring) are configured to scan with high sensitivity (e.g., as described in Table 1 ). For instance, in some examples, a portion of the DEAs of a detector ring are configured to scan with a high spatial resolution, while another portion of the DEAs of the detector ring are configured to scan with high sensitivity. In some examples, a portion of detector elements within a DEA of a detector ring are configured to scan with a high spatial resolution, while another portion of the detector elements within the DEA are configured to scan with high sensitivity. In some examples, the various portions of the detector elements, additionally or alternatively, vary in material.

[0026] In some examples, the image scanning systems described herein may allow for a patient table to be moved during scanning. The systems allow for the scanning of regions of interest, such as whole body scans, that are larger than the scanner’s axial field-of-view, for instance. Because the systems may include detector rings with varying characteristics, the region of interest can be scanned by the same image scanning system in various ways. For example, the image scanning system can move a patient such that a region of interest is under a detector ring that is configured to scan with high spatial resolution. For instance, a scan of a brain may require a short field-of-view (FOV) but high spatial resolution. After scanning, the image scanning system can move the patient such that the region of interest is under another detector ring that is configured to scan with high sensitivity. Once scanned, at least two imagesDocket No.: 2024P21678WOcan be reconstructed based on the high spatial resolution scan and the high sensitivity scan, respectively.

[0027] In some examples, reconstruction processes can be applied to the scans of varying characteristics to reconstruct an image. For instance, in one example, the reconstruction problem is constrained. For example, in a low count scenario, the high sensitivity scan detection data can be utilized to reconstruct low count images of sufficient count statistics. The high-resolution scan detection data can be used as a penalty term to improve the spatial resolution of the low count images. For instance, algorithms that use prior information (e.g., segmentation-free anatomical priors), such as “Bowsher prior,” can use anatomical information (e.g., MR image) to guide a functional image reconstruction (e.g., a PET image reconstruction).

[0028] Among other advantages, the embodiments described herein can provide imaging systems (e.g., LAFOV imaging systems) that can capture scans with varying detection characteristics, thereby allowing for flexibility in axial FOV, performance, and price. The embodiments may allow for the simultaneous acquisition of images with varying characteristics, such as high / low spatial resolution and high / low sensitivity based images. Moreover, the embodiments may provide the ability to combine imaging data from detection elements and / or DEAs with varying detection characteristics to generate reconstructed images. Persons of ordinary skill in the art would recognize these and other benefits as well.

[0029] Referring now to the figures, FIG. 1 illustrates a nuclear imaging system 100 that includes multi-detection characteristic image scanning system 102 and image reconstruction system 104. The multi-detection characteristic image scanning system 102 can be PET scanner (e.g., LAFOV PET scanner) that can capture PET images, a PET / MR scanner that can capture PET and MR images, a PET / CT scanner that can capture PET and CT images, a SPECT scanner, or any other suitable image scanner. For example, as illustrated, multi-detection characteristic image scanning system 102 can capture PET images (e.g., of a person), and can generate PET measurement data based on captured PET images. The PET measurement data (e.g., sinogram data,Docket No.: 2024P21678WOlist-mode data) can represent anything imaged in the scanner's field-of-view (FOV) containing positron emitting isotopes. Moreover, the PET measurement data may identify detection events (e.g. , time-coincident data pairs) and related timing information.

[0030] The multi-detection characteristic image scanning system 102 includes various detector rings, including a first characteristic detector ring 110 and multiple second characteristic detector rings 112A, 112B, 112C, 112D. In this example, the first characteristic detector ring 110 is axially adjacent to each of the second characteristic detector ring 112B and the second characteristic detector ring 112C. In other examples, the first characteristic detector ring 110 may be axially adjacent to one or more of the second characteristic detector rings 112A, 112B, 112C, 112D. Each of the first characteristic detector ring 110 and multiple second characteristic detector rings 112A, 112B, 112C, 112D can include detector elements (e.g., detector blocks), where the detector elements include scintillation crystals.

[0031] Further, each of the first characteristic detector ring 110 and multiple second characteristic detector rings 112A, 112B, 112C can scan a patient 103 and, based on the scans, generate corresponding detection data (e.g., sinograms). The characteristics of the detection data generated by the first characteristic detector ring 110 may differ from the characteristics of the detection data generated by each of the second characteristic detector rings 112A, 112B, 112C, 112D. For instance, in some examples, the first characteristic detector ring 110 may be configured (e.g., optimized) for high spatial resolution image captures, while the second characteristic detector rings 112A, 112B, 112C, 112D may be configured for high sensitivity image captures.

[0032] As an example, to generate high spatial resolution images, one or more of the detector elements of the first characteristic detector ring 110 can include smaller crystals than the detector elements of the second characteristic detector rings 112A, 112B, 112C, 112D. For example, the first characteristic detector ring 110 can include detector elements that include crystals of a first size (e.g., 1.6 x 1.6 x 20 mm), whereas the detector elements of the second characteristic detector rings 112A, 112B, 112C,Docket No.: 2024P21678WO112D can include crystals of a second size (e.g., 2.7 x 2.7 x 20 mm). Additionally or alternatively, the first characteristic detector ring 110 may include a higher number of detector elements than any of the second characteristic detector rings 112A, 112B, 112C, 112D.

[0033] In some examples, the first characteristic detector ring 110 may be configured to have higher DOI and / or TOF performance than the second characteristic detector rings 112A, 112B, 112C, 112D, thereby allowing the first characteristic detector ring 110 to capture higher spatial resolution images than the second characteristic detector rings 112A, 112B, 112 C , 112D. In some examples, the detector elements of the first characteristic detector ring 110 can include lutetium oxyorthosilicate (LSO) crystals, while the detection elements of the second characteristic detector rings 112A, 112B, 112C, 112D may include bismuth germanate (BSO) or plastic scintillation crystals. LSO crystals have relatively high light output which can improve spatial resolution, and can have relatively faster decay times which can allow for better count rate performance. LSO crystals are generally more expensive than BGO or plastic crystals. As a result, the cost of the second characteristic detector rings 112A, 112B, 112C, 112D may be less than the cost of the first characteristic detector ring 110.

[0034] To generate high sensitivity images, the DEAs of the second characteristic detector rings 112A, 112B, 112C, 112D may include lower noise electronics, larger (e.g., longer) crystals, and / or higher stopping power material (e.g., to absorb radiation particles), when compared to the DEAs of the first characteristic detector ring 110. In some examples, a portion of the DEAs of any one of the second characteristic detector rings 112A, 112B, 112C, 112D may include one or more of the lower noise electronics, larger crystals, and / or higher stopping power material, while another portion of the DEAs of the second characteristic detector ring 112A, 112B, 112C, 112D does not.

[0035] In some examples, the first characteristic detector ring 110 and the second characteristic detector rings 112A, 112B, 112C, 112D may include DEAs in various patterns, such as rings, partial rings, or checker board rings. A partial ring is a detector ring that is sparsely populated (e.g., compared to a conventional detector ring),Docket No.: 2024P21678WOand a checkered board ring includes populating a detector in every other detector position. In some examples, detector elements of a DEA may vary, for instance, in crystal size (e.g., dimensions), any patterns, or have a randomized mix of characteristics, such as varying characteristics to realize a continuous transition of the characteristics along a FOV of the detector element. In some examples, the crystal size pattern along the axial field-of-view is varied by changing the x,y dimension and / or the z dimension. Additionally or alternatively, in some examples, the type of scintillation material of the crystals can vary.

[0036] In some examples, the first characteristic detector ring 110 may include DEA’s, detection elements, and / or crystals with varying detection characteristics, regardless of whether any of the multiple second characteristic detector rings 112A, 112B, 112C include varying detection characteristics. For instance, in some examples, the second characteristic detector rings 112A, 112B, 112C may all include DEA’s (e.g., and a same number of DEA’s) with similar detection characteristics. In addition, the first characteristic detector ring 110 may include at least one DEA with the same detection characteristics as those for the second characteristic detector rings 112A, 112B, 1120, and at least one DEA with detection characteristics that vary from those for the second characteristic detector rings 112A, 112B, 112C.

[0037] Although in this example the first characteristic detector ring 110 is placed between the second characteristic detector rings 112B, 1120, in other examples the first characteristic detector ring 110 can be placed in any other position relative to any of the multiple second characteristic detector rings 112A, 112B, 1120, 112D. For instance, the first characteristic detector ring 110 can be placed in the first, second, fourth, or fifth, detector ring position (i.e. , after the multiple second characteristic detector rings 112A, 112B, 1120, 112D). In addition, in some examples, rather than a single first characteristic detector ring 110, the multi-detection characteristic image scanning system 102 can include multiple first resolution detector rings 110. Similarly, rather than multiple second characteristic detector rings 112A, 112B, 112C, 112D, in some examples the multi-detection characteristic image scanning system 102 canDocket No.: 2024P21678WOinclude a less number of second characteristic detector rings 112A, 112B, 112C, 112D, such as a single second characteristic detector ring 112A, 112B, 112C, 112D.

[0038] As illustrated, the multi-detection characteristic image scanning system 102 further includes a patient table 105 as well as a table motion controller 106. The table motion controller 106 is configured to move the patient table 105 (e.g., in the axial direction) so as to place a patient 103 at a desired position for scanning. For example, the table motion controller 106 can move the patient table 105 through the first characteristic detector ring 110 and various second characteristic detector rings 112A, 112B, 112C, 112D. In some examples, the table motion controller 106 can move the patient table 105 such as to position the patient’s 103 head under the first characteristic detector ring 110, while positioning at least a portion of the patient’s 103 body under the second characteristic detector rings 112C, 112D.

[0039] In some examples, the first characteristic detector ring 110 scans a first portion of the patient 103 (e.g., the patient’s head), while one or more of the second characteristic detector rings 112A, 112B, 112C, 112D scan a second portion of the patient 103 (e.g., the patient’s body). The table motion controller 106 may then move the patient table 105 to place the first portion of the patient 103 within the FOV of one or more of the second characteristic detector rings 112A, 112B, 112C, 112D, and / or place the second portion of the patient 103 within the FOV of the first characteristic detector ring 110. The first characteristic detector ring 110 then scans the second portion of the patient 103 (e.g., the patient’s head), while the one or more of the second characteristic detector rings 112A, 112B, 112C, 112D scan the first portion of the patient 103 (e.g., the patient’s body).

[0040] Additionally, the image reconstruction system 104 can receive detection data (e.g., sinograms) generated by one or more of the first characteristic detector ring 110 and various second characteristic detector rings 112A, 112B, 112C. For example, the image reconstruction system 104 can receive from the multi-detection characteristic image scanning system 102 first resolution detection data based on captured scans byDocket No.: 2024P21678WOthe first characteristic detector ring 110, and second resolution detection data based on captured scans by each of the second characteristic detector rings 112A, 112B, 112C.

[0041] In some examples, the image reconstruction system 104 may also receive co-modality data from the multi-detection characteristic image scanning system 102, such as CT or MRI measurement data. For example, the multi-detection characteristic image scanning system 102 may support PET / CT scans, and thus can generate CT measurement data based on the CT scans, and transmit the CT measurement data to the image reconstruction system 104. Alternatively, the multi-detection characteristic image scanning system 102 may support PET / MRI scans, and thus can generate MRI measurement data based on the MRI scans, and transmit the MRI measurement data to the image reconstruction system 104. In some examples, the multi-detection characteristic image scanning system 102 can generate attenuation maps based on comodality scans, and can transmit the attenuation maps to the image reconstruction system 104.

[0042] As described further herein, the image reconstruction system 104 can reconstruct an image based on the detection data received from the multi-detection characteristic image scanning system 102. For instance, in some examples, the image reconstruction system 104 can reconstruct an image based on the detection data (e.g., time-coincident data pairs, sinograms) generated for scans captured by the first characteristic detector ring 110 (e.g., relatively higher spatial resolution detection data). The image reconstruction system 104 can also reconstruct an image based the detection data generated for scans captured by one or more of the second characteristic detector rings 112A, 112B, 112C (e.g., relatively higher sensitivity detection data).

[0043] As an example, FIG. 4A illustrates the generation of first sinogram data 402 based on one or more scans 403 by the first characteristic detector ring 110 of the multi-detection characteristic image scanning system 102. The image reconstruction system 104 can receive the first sinogram data 402, and can reconstruct a first image 412. FIG. 4A further illustrates the generation of second sinogram data 404 based onDocket No.: 2024P21678WOscans 405 by the second characteristic detector rings 112A, 112B. The image reconstruction system 104 can receive the second sinogram data 404, and can reconstruct a second image 414. Further, the multi-detection characteristic image scanning system 102 can generate third sinogram data 406 based on scans 407405 by the second characteristic detector rings 112C, 112D. The image reconstruction system 104 can receive the third sinogram data 406, and can reconstruct a third image 416. As described herein, the first characteristic detector ring 110 and second characteristic detector rings 112A, 112B, 112C, 112D can have varying detection characteristics. As such, in some examples, the first image 412, second image 414, and third image 416 may have varying image characteristics, such differences in resolution, quality, noise, detail, etc.

[0044] In yet other examples, the image reconstruction system 104 can reconstruct an image based on the detection data generated for scans captured by the first characteristic detector ring 110 and the detection data generated for scans captured by one or more of the second characteristic detector rings 112A, 112B, 112C, 112D. For example, as illustrated in FIG. 4B, the image reconstruction system 104 can reconstruct a fourth image 418 based on the first sinogram data 402, the second sinogram data 404, and the third sinogram data 406.

[0045] FIG. 2 illustrates a computing device 200 that can be employed by, for example, the image reconstruction system 104. For instance, computing device 200 can implement one or more of the functions of the image reconstruction system 104 described herein.

[0046] Computing device 200 can include one or more processors 201 , working memory 202, one or more input-output devices 203, instruction memory 207, a transceiver 204, one or more communication ports 209, and a display 206, all operatively coupled to one or more data buses 208. Data buses 208 allow for communication among the various devices. Data buses 208 can include wired, or wireless, communication channels.Docket No.: 2024P21678WO

[0047] Processors 201 can include one or more distinct processors, each having one or more cores. Each of the distinct processors can have the same or different structure. Processors 201 can include one or more central processing units (CPUs), one or more graphics processing units (GPUs), application specific integrated circuits (ASICs), digital signal processors (DSPs), and the like. Further, each of the one or more processors 201 can be configured to perform a certain function or operation by executing code, stored on instruction memory 207, embodying the function or operation. For example, processors 201 can be configured to perform one or more of the functions, methods, or operations disclosed herein.

[0048] Instruction memory 207 can store instructions that can be accessed (e.g., read) and executed by processors 201. For example, instruction memory 207 can be a non-transitory, computer-readable storage medium such as a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), flash memory, a removable disk, CD-ROM, any non-volatile memory, or any other suitable memory. For example, instruction memory 207 can store instructions that, when executed by one or more processors 201 , cause one or more processors 201 to perform one or more of the functions of image reconstruction system 104, such as one or more of the histo-image generation processes, the multi-view attenuation histo-image generation processes, and / or the machine learning processes described herein.

[0049] Processors 201 can store data to, and read data from, working memory 202. For example, processors 201 can store a working set of instructions to working memory 202, such as instructions loaded from instruction memory 207. Processors 201 can also use working memory 202 to store dynamic data created during the operation of computing device 200. Working memory 202 can be a random access memory (RAM) such as a static random access memory (SRAM) or dynamic random access memory (DRAM), or any other suitable memory.

[0050] Input-output devices 203 can include any suitable device that allows for data input or output. For example, input-output devices 203 can include one or more ofDocket No.: 2024P21678WOa keyboard, a touchpad, a mouse, a stylus, a touchscreen, a physical button, a speaker, a microphone, or any other suitable input or output device.

[0051] Communication port(s) 209 can include, for example, a serial port such as a universal asynchronous receiver / transmitter (UART) connection, a Universal Serial Bus (USB) connection, or any other suitable communication port or connection. In some examples, communication port(s) 209 allows for the programming of executable instructions in instruction memory 207. In some examples, communication port(s) 209 allow for the transfer (e.g., uploading or downloading) of data.

[0052] Display 206 can display user interface 205. User interface 205 can enable user interaction with computing device 200. For example, user interface 205 can be a user interface for an application that allows for the viewing of final image volumes, such as final image volume 391 discussed further herein. In some examples, a user can interact with user interface 205 by engaging input-output devices 203. In some examples, display 206 can be a touchscreen, where user interface 205 is displayed on the touchscreen.

[0053] Transceiver 204 allows for communication with a network, such as a Wi-Fi network, an Ethernet network, a cellular network, or any other suitable communication network. For example, if operating in a cellular network, transceiver 204 is configured to allow communications with the cellular network. Processor(s) 201 is operable to receive data from, or send data to, a network via transceiver 204.

[0054] FIG. 3 illustrates further exemplary details of the nuclear imaging system 100. As illustrated, the multi-detection characteristic image scanning system 102 may generate, based on a scan by the first characteristic detector ring 110, first characteristic PET detection data 311. The multi-detection characteristic image scanning system 102 may also generate, based on a scan by one or more of the second characteristic detector rings 112, second characteristic PET detection data 313. The first characteristic detector ring 110 is configured with a first set of detection characteristics to capture first scans, while the second characteristic detector rings 112 are configured with a second set of detection characteristics to capture second scans.Docket No.: 2024P21678WOAs such, the first characteristic PET detection data 311 and second characteristic PET detection data 313 have differing image characteristics.

[0055] For example, the first characteristic detector ring 110 may be configured to capture the first scans with a higher spatial resolution than the second scans captured by the second characteristic detector rings 112. Further, the second scans captured by the second characteristic detector rings 112 may be configured to capture the second scans with a higher sensitivity than the first scans captured by the first characteristic detector ring 110. As such, the first characteristic PET detection data 311 and second characteristic PET detection data 313 have differing imaging characteristics.

[0056] The multi-detection characteristic image scanning system 102 may transmit the first characteristic PET detection data 311 and the second characteristic PET detection data 313 to the image reconstruction system 104.

[0057] As further illustrated, the image reconstruction system 104 includes a PET image reconstruction engine 302 and a memory device 310. In some examples, the image reconstruction system 104 are implemented in hardware, such as in one or more field-programmable gate arrays (FPGAs), one or more application-specific integrated circuits (ASICs), one or more state machines, one or more computing devices, digital circuitry, or any other suitable circuitry. For example, all or parts of the PET image reconstruction engine 302 may be implemented within one or more FPGAs. In some examples, parts or all of the image reconstruction system 104 can be implemented in software as executable instructions such that, when executed by one or more processors, cause the one or more processors to perform respective functions as described herein. The instructions can be stored in a non-transitory, computer-readable storage medium, and can be read and executed by the one or more processors.

[0058] The PET image reconstruction engine 302 can receive the first characteristic PET detection data 311 and the second characteristic PET detection data 313 and reconstruct an image (e.g., a 3-dimensional (3D) image) based on one or more of the first characteristic PET detection data 311 and the second characteristic PET detection data 313.Docket No.: 2024P21678WO

[0059] For instance, in some examples, the PET image reconstruction engine 302 applies an image reconstruction process to the first characteristic PET detection data 311 and the second characteristic PET detection data 313 to reconstruct a final image volume 391. For example, assuming the first characteristic PET detection data 311 was captured with relatively higher resolution, and the second characteristic PET detection data 313 was captured with relatively higher sensitivity, the second characteristic PET detection data 313 can be utilized to reconstruct a low count image of sufficient count statistics. The first characteristic PET detection data 311 can be used as a penalty term to improve the spatial resolution of the low count image. For instance, in some examples an algorithm that uses prior information (e.g., segmentation-free anatomical priors), such as “Bowsher prior,” can use anatomical information (e.g., MR image) to guide a functional image reconstruction (e.g., a PET image reconstruction). In some examples, the first characteristic PET detection data 311 can be used as the prior information to improve the spatial resolution of the low count image.

[0060] The PET image reconstruction engine 302 then reconstructs the final image volume 391 based on the first characteristic PET detection data 311 and the low count image. For instance, the PET image reconstruction engine 302 may apply a filtered-back-projection (FBP) process to the first characteristic PET detection data 311 and the low count image to generate the final image volume 391. Other reconstruction processes can include, for instance, Maximum-likelihood expectation maximization (ML-EM), Ordered subset expectation maximization (OSEM), or any other suitable reconstruction process.

[0061] In some examples, the PET image reconstruction engine 302 may apply one or more trained machine learning processes to the first characteristic PET detection data 311 and the second characteristic PET detection data 313 and, based on applying the one or more trained machine learning processes, may generate the final image volume 391. For example, the PET image reconstruction engine 302 may input the first characteristic PET detection data 311 and the second characteristic PET detection data 313 to a trained neural network 319 (e.g., convolutional neural network (CNN)) and, in response, may generate output data characterizing the final image volume 391.Docket No.: 2024P21678WO

[0062] In some examples, to train the neural network 319, the image reconstruction system 104 inputs pairs of first characteristic PET detection data 311 and the second characteristic PET detection data 313 to the neural network 319, and compares the output data to expected final image volume data. For example, the image reconstruction system 104 may compute one or more metric values based on the output data and the expected final image volume data. Each metric may be, for example, an area-under-curve (AUC) value, any receiver operating characteristic (ROC) curve or precision-recall (PR) curve value, or any other suitable metric value. The image reconstruction system 104 may determine whether the neural network is trained based on the one or more metric values. For instance, the image reconstruction system 104 may determine that the neural network 319 is trained when a metric value at least meets (e.g., is the same as or greater than) a threshold value. If the metric value does not at least meet the threshold value, the image reconstruction system 104 continues training the neural network 319 until the metric value does at least meet the threshold value. Once trained, the image reconstruction system 104 may store parameters (e.g., hyperparameters, weights, coefficients, etc.) characterizing the trained neural network in the memory device 310. The image reconstruction system 104 may retrieve the parameters from the memory device 310 to establish and execute the trained neural network 319.

[0063] In some instances, the image reconstruction system 104 receives an attenuation map 305 (e.g., based on a co-modality scan, such as a CT scan) from the multi-detection characteristic image scanning system 102. The image reconstruction system 104 may perform operations to correct the first characteristic PET detection data 311 and the second characteristic PET detection data 313 for attenuation based on the attenuation map 305. For instance, the image reconstruction system 104 may apply one or more attenuation correction processes to the output of the trained machine learning process and the attenuation map 305 and, based on applying the one or more attenuation correction processes, may generate the final image volume 391.

[0064] In some examples, the PET image reconstruction engine 302 reconstructs a first characteristic image 379 based on the first characteristic PET detection data 311Docket No.: 2024P21678WOusing any of the reconstruction processes described herein, or any other suitable reconstruction process. Additionally or alternatively, the PET image reconstruction engine 302 may reconstruct a second characteristic image 381 based on the second characteristic PET detection data 313 using any of the reconstruction processes described herein, or any other suitable reconstruction process.

[0065] In some examples, the PET image reconstruction engine 302 stores one or more of the final image volume 391 , first characteristic image 379, and second characteristic image 381 in memory device 310. In some examples, the image reconstruction system 104 transmits one or more of the final image volume 391 , first characteristic image 379, and second characteristic image 381 for display, such as for display on display 460. Display 460 may be, for example, in a same room as the nuclear imaging system 100, or in a remote location (e.g., a separate healthcare facility).

[0066] FIG. 5 is a flowchart of an example method 500 to generate detection data based on varying characteristics of an image scanning system and, in some examples, reconstruct a corresponding image. The method can be performed by, for example, the nuclear imaging system 100 described herein.

[0067] Beginning at block 502, a first scan is captured by a first detector ring of an image scanning system, where the first detector ring has first detection characteristics. For example, the first characteristic detector ring 110 of the multidetection characteristic image scanning system 102 may capture a scan (e.g., scan 403) of the head of the patient 103. At block 504, a second scan is captured by a second detector ring of the image scanning system, where the second detector ring has second detection characteristics. For example, the second characteristic detector rings 112C, 112D of the multi-detection characteristic image scanning system 102 may capture a scan (e.g., scan 407) of the body of the patient 103. The first detection characteristics differ from the second detection characteristics. For instance, one or more detector elements of the first detector ring may be configured to scan at a relatively higher spatial resolution than one or more detector elements of the secondDocket No.: 2024P21678WOdetector ring. Additionally or alternatively, one or more detector elements of the second detector ring may be configured to scan at relatively higher sensitivity than one or more detector elements of the first detector ring.

[0068] Further, at block 506, first detection data is generated based on the first scan. For instance, the multi-detection characteristic image scanning system 102 may generate first detection data, such as first sinogram data 402, based on the scan 403. Similarly, at block 508, second detection data is generated based on the second scan. For instance, the multi-detection characteristic image scanning system 102 may generate second detection data, such as second sinogram data 404, based on the scan 405. At block 510 the first detection data and the second detection data is stored within a memory device (e.g., memory device 310).

[0069] In some examples, at block 512, an image is reconstructed based on the first detection data and the second detection data. For instance, and as described herein, the multi-detection characteristic image scanning system 102 may transmit the first detection data (e.g., first sinogram data 402) and the second detection data (e.g., second sinogram data 404) to the image reconstruction system 104. Further, the image reconstruction system 104 may reconstruct an image based on the first sinogram data and the second detection data. In some examples, the image reconstruction system 104 inputs the first detection data and the second detection data to a trained neural network (e.g., executed neural network 319) and, in response, the trained neural network generates output data characterizing the reconstructed image (e.g., the final image volume 391).

[0070] Further, in some examples, at block 514 the image is provided for display. For instance, the image reconstruction system 104 may transmit the image to display 460 for display.

[0071] FIG. 6 is a flowchart of an example method 600 to capture scans using an image scanning system with detector rings of varying characteristics. The method can be performed by, for example, the multi-detection characteristic image scanning system 102 described herein.Docket No.: 2024P21678WO

[0072] Beginning at block 602, a first signal is generated to move a table and place a first portion of a patient in the FOV of a first detector ring of an image scanning system. The first detector ring has first detection characteristics. For instance, the table motion controller 106 may generate a signal to move the patient table 105 until the patient’s 103 head is in the FOV of the first characteristic detector ring 110. Further, at block 604, a first scan of the first portion of the patient is captured by the first detector ring. At block 606 first detection data is generated based on the first scan.

[0073] Proceeding to block 608, a second scan of a second portion of the patient is captured by a second detector ring of the image scanning system. The second detector ring has second detection characteristics. At block 610 second detection data is generated based on the second scan.

[0074] At block 612, a second signal is generated to move the table and place the second portion of a patient in the FOV of the first detector ring of the image scanning system. For instance, the table motion controller 106 may generate a signal to move the patient table 105 until the at least a portion of the patient’s 103 body is in the FOV of the first characteristic detector ring 110.

[0075] Further, at block 614, a third scan of the second portion of the patient is captured by the first detector ring. At block 616 third detection data is generated based on the third scan. Additionally, at block 616, the first detection data, second detection data, and third detection data is transmitted. For example, the multi-detection characteristic image scanning system 102 may transmit the first detection data, second detection data, and third detection data to the image reconstruction system 104 to reconstruct one or more images, as described herein.

[0076] The following is a list of non-limiting illustrative embodiments disclosed herein:

[0077] Illustrative Embodiment 1 : A computer-implemented method comprising:Docket No.: 2024P21678WO receiving a first scan captured by a first detector ring of an image scanning system, the first detector ring having first detection characteristics;receiving a second scan captured by a second detector ring of the image scanning system, the second detector ring having second detection characteristics, wherein the second detection characteristics differ from the first detection characteristics;generating first detection data based on the first scan;generating second detection data based on the second scan; andstoring the first detection data and the second detection data within a memory device.

[0078] Illustrative Embodiment 2: The computer-implemented method of illustrative embodiment 1, wherein the first detection characteristics comprise high spatial resolution, and the second detection characteristics comprise high sensitivity.

[0079] Illustrative Embodiment 3: The computer-implemented method of any of illustrative embodiments 1-2, wherein the first detector ring comprises a first number of crystals and the second detector ring comprises a second number of crystals, wherein the first number differs from the second number.

[0080] Illustrative Embodiment 4: The computer-implemented method of any of illustrative embodiments 1-3, wherein the first detector ring comprises a first number of crystals made of a first material, and the second detector ring comprises a secondDocket No.: 2024P21678WO number of crystals made of a second material, wherein the first material differs from the second material.

[0081] Illustrative Embodiment 5: The computer-implemented method of any of illustrative embodiments 1-4, wherein the first detector ring comprises a first number of detector elements and the second detector ring comprises a second number of detector elements, wherein the first number differs from the second number.

[0082] Illustrative Embodiment 6: The computer-implemented method of any of illustrative embodiments 1-5, further comprising applying a reconstruction process to the first detection data and, based on the application of the reconstruction process, generating a first image.

[0083] Illustrative Embodiment 7: The computer-implemented method of illustrative embodiment 6, further comprising applying the reconstruction process to the second detection data and, based on the application of the reconstruction process, generating a second image.

[0084] Illustrative Embodiment 8: The computer-implemented method of any of illustrative embodiments 1-7, further comprising applying a reconstruction process to the first detection data and the second detection data and, based on the application of the reconstruction process, generating an image.Docket No.: 2024P21678WO

[0085] Illustrative Embodiment 9: The computer-implemented method of any of illustrative embodiments 1-8, further comprising generating a first signal, the first signal causing a table to move so that a portion of a patient lying on the table is within a field-of-view of the first detector ring.

[0086] Illustrative Embodiment 10: The computer-implemented method of illustrative embodiment 9, further comprising generating a second signal, the second signal causing the table to move so that the portion of the patient lying on the table is within a field-of-view of the second detector ring.

[0087] Illustrative Embodiment 11 : The computer-implemented method of any of illustrative embodiments 1-10, wherein the first scan and the second scan are positron emission tomography (PET) scans.

[0088] Illustrative Embodiment 12: The computer-implemented method of any of illustrative embodiments 1-10, wherein the first detector ring comprises a first number of crystals of a first size and the second detector ring comprises a second number of crystals of a second size, wherein the first size differs from the second size.

[0089] Illustrative Embodiment 13: A non-transitory computer readable medium storing instructions that, when executed by at least one processor, cause the at least one processor to perform operations comprising:Docket No.: 2024P21678WO receiving a first scan captured by a first detector ring of an image scanning system, the first detector ring having first detection characteristics;receiving a second scan captured by a second detector ring of the image scanning system, the second detector ring having second detection characteristics, wherein the second detection characteristics differ from the first detection characteristics;generating first detection data based on the first scan;generating second detection data based on the second scan; andstoring the first detection data and the second detection data within a memory device.

[0090] Illustrative Embodiment 14: The non-transitory computer readable medium of illustrative embodiment 13, wherein the first detection characteristics comprise high spatial resolution, and the second detection characteristics comprise high sensitivity.

[0091] Illustrative Embodiment 15: The non-transitory computer readable medium of any of illustrative embodiments 13-14, wherein the first detector ring comprises a first number of crystals and the second detector ring comprises a second number of crystals, wherein the first number differs from the second number.

[0092] Illustrative Embodiment 16: The non-transitory computer readable medium of any of illustrative embodiments 13-15, wherein the first detector ringDocket No.: 2024P21678WO comprises a first number of crystals made of a first material, and the second detector ring comprises a second number of crystals made of a second material, wherein the first material differs from the second material.

[0093] Illustrative Embodiment 17: The non-transitory computer readable medium of any of illustrative embodiments 13-16, wherein the first detector ring comprises a first number of detector elements and the second detector ring comprises a second number of detector elements, wherein the first number differs from the second number.

[0094] Illustrative Embodiment 18: The non-transitory computer readable medium of any of illustrative embodiments 13-17, wherein the instructions, when executed by the at least one processor, further cause the at least one processor to perform operations comprising applying a reconstruction process to the first detection data and, based on the application of the reconstruction process, generating a first image.

[0095] Illustrative Embodiment 19: The non-transitory computer readable medium of illustrative embodiment 18 wherein the instructions, when executed by the at least one processor, further cause the at least one processor to perform operations comprising applying the reconstruction process to the second detection data and, based on the application of the reconstruction process, generating a second image.Docket No.: 2024P21678WO

[0096] Illustrative Embodiment 20: The non-transitory computer readable medium of any of illustrative embodiments 13-19 wherein the instructions, when executed by the at least one processor, further cause the at least one processor to perform operations comprising applying a reconstruction process to the first detection data and the second detection data and, based on the application of the reconstruction process, generating an image.

[0097] Illustrative Embodiment 21 : The non-transitory computer readable medium of any of illustrative embodiments 13-20 wherein the instructions, when executed by the at least one processor, further cause the at least one processor to perform operations comprising generating a first signal, the first signal causing a table to move so that a portion of a patient lying on the table is within a field-of-view of the first detector ring.

[0098] Illustrative Embodiment 22: The non-transitory computer readable medium of illustrative embodiment 21 wherein the instructions, when executed by the at least one processor, further cause the at least one processor to perform operations comprising generating a second signal, the second signal causing the table to move so that the portion of the patient lying on the table is within a field-of-view of the second detector ring.Docket No.: 2024P21678WO

[0099] Illustrative Embodiment 23: The non-transitory computer readable medium of any of illustrative embodiments 13-22, wherein the first scan and the second scan are positron emission tomography (PET) scans.

[0100] Illustrative Embodiment 24: The non-transitory computer readable medium of any of illustrative embodiments 13-23, wherein the first detector ring comprises a first number of crystals of a first size and the second detector ring comprises a second number of crystals of a second size, wherein the first size differs from the second size.

[0101] Illustrative Embodiment 25: An image scanning system comprising:a first detector ring having first detection characteristics;a second detector ring having second detection characteristics, wherein the second detection characteristics differs from the first detection characteristics;a memory device; andat least one processor communicatively coupled to the memory device and configured to:receive a first scan captured by a first detector ring of an image scanning system;receive a second scan captured by a second detector ring of the image scanning system;generate first detection data based on the first scan;generate second detection data based on the second scan; andDocket No.: 2024P21678WO store the first detection data and the second detection data within a memory device.

[0102] Illustrative Embodiment 26: The system of illustrative embodiment 25, wherein the first detection characteristics comprise high spatial resolution, and the second detection characteristics comprise high sensitivity.

[0103] Illustrative Embodiment 27: The system of any of illustrative embodiments 25-26, wherein the first detector ring comprises a first number of detector elements and the second detector ring comprises a second number of detector elements, wherein the first number differs from the second number.

[0104] Illustrative Embodiment 28: The system of any of illustrative embodiments 25-27, wherein the first detector ring comprises a first number of crystals made of a first material, and the second detector ring comprises a second number of crystals made of a second material, wherein the first material differs from the second material.

[0105] Illustrative Embodiment 29: The system of any of illustrative embodiments 25-28, wherein the first detector ring comprises a first number of detector elements and the second detector ring comprises a second number of detector elements, wherein the first number differs from the second number.Docket No.: 2024P21678WO

[0106] Illustrative Embodiment 30: The system of any of illustrative embodiments 25-29, wherein the at least one processor is configured to apply a reconstruction process to the first detection data and, based on the application of the reconstruction process, generate a first image.

[0107] Illustrative Embodiment 31 : The system of illustrative embodiment 30, wherein the at least one processor is configured to apply the reconstruction process to the second detection data and, based on the application of the reconstruction process, generate a second image.

[0108] Illustrative Embodiment 32: The system of any of illustrative embodiments 25-31, wherein the at least one processor is configured to apply a reconstruction process to the first detection data and the second detection data and, based on the application of the reconstruction process, generate an image.

[0109] Illustrative Embodiment 33: The system of any of illustrative embodiments 25-32, wherein the at least one processor is configured to generate a first signal, the first signal causing a table to move so that a portion of a patient lying on the table is within a field-of-view of the first detector ring.

[0110] Illustrative Embodiment 34: The system of illustrative embodiment 33, wherein the at least one processor is configured to generate a second signal, theDocket No.: 2024P21678WO second signal causing the table to move so that the portion of the patient lying on the table is within a field-of-view of the second detector ring.

[0111] Illustrative Embodiment 35: The system of any of illustrative embodiments 25-34, wherein the first scan and the second scan are positron emission tomography (PET) scans

[0112] Illustrative Embodiment 36: The system of any of illustrative embodiments 25-35, wherein the first detector ring comprises a first number of crystals of a first size and the second detector ring comprises a second number of crystals of a second size, wherein the first size differs from the second size.

[0113] Illustrative Embodiment 37: A system comprising:a means for receiving a first scan captured by a first detector ring of an image scanning system, the first detector ring having first detection characteristics;a means for receiving a second scan captured by a second detector ring of the image scanning system, the second detector ring having second detection characteristics, wherein the second detection characteristics differ from the first detection characteristics;a means for generating first detection data based on the first scan;a means for generating second detection data based on the second scan; and a means for storing the first detection data and the second detection data within a memory device.Docket No.: 2024P21678WO

[0114] Illustrative Embodiment 38: The system of illustrative embodiment 37, wherein the first detection characteristics comprise high spatial resolution, and the second detection characteristics comprise high sensitivity.

[0115] Illustrative Embodiment 39: The system of any of illustrative embodiments 37-38, wherein the first detector ring comprises a first number of crystals and the second detector ring comprises a second number of crystals, wherein the first number differs from the second number.

[0116] Illustrative Embodiment 40: The system of any of illustrative embodiments 37-39, wherein the first detector ring comprises a first number of crystals made of a first material, and the second detector ring comprises a second number of crystals made of a second material, wherein the first material differs from the second material.

[0117] Illustrative Embodiment 41 : The system of any of illustrative embodiments 37-40, wherein the first detector ring comprises a first number of detector elements and the second detector ring comprises a second number of detector elements, wherein the first number differs from the second number.

[0118] Illustrative Embodiment 42: The system of any of illustrative embodiments 37-41 , further comprising a means for applying a reconstruction process to the first detection data and, based on the application of the reconstruction process, generating a first image.Docket No.: 2024P21678WO

[0119] Illustrative Embodiment 43: The system of illustrative embodiment 42, further comprising a means for applying the reconstruction process to the second detection data and, based on the application of the reconstruction process, generating a second image.

[0120] Illustrative Embodiment 44: The system of any of illustrative embodiments 37-43, further comprising a means for applying a reconstruction process to the first detection data and the second detection data and, based on the application of the reconstruction process, generating an image.

[0121] Illustrative Embodiment 45: The system of any of illustrative embodiments 37-44, further comprising a means for generating a first signal, the first signal causing a table to move so that a portion of a patient lying on the table is within a field-of-view of the first detector ring.

[0122] Illustrative Embodiment 46: The system of illustrative embodiment 45, further comprising a means for generating a second signal, the second signal causing the table to move so that the portion of the patient lying on the table is within a field-of-view of the second detector ring.Docket No.: 2024P21678WO

[0123] Illustrative Embodiment 47: The system of any of illustrative embodiments 37-46, wherein the first scan and the second scan are positron emission tomography (PET) scans.

[0124] Illustrative Embodiment 48: The system of any of illustrative embodiments 37-47, wherein the first detector ring comprises a first number of crystals of a first size and the second detector ring comprises a second number of crystals of a second size, wherein the first size differs from the second size.

[0125] The apparatuses and processes are not limited to the specific embodiments described herein. In addition, components of each apparatus and each process can be practiced independent and separate from other components and processes described herein.

[0126] The previous description of embodiments is provided to enable any person skilled in the art to practice the disclosure. The various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein can be applied to other embodiments without the use of inventive faculty. The present disclosure is not intended to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

Docket No.: 2024P21678WO What is claimed is:

1. A computer-implemented method comprising:receiving a first scan captured by a first detector ring of an image scanning system, the first detector ring having first detection characteristics;receiving a second scan captured by a second detector ring of the image scanning system, the second detector ring having second detection characteristics, wherein the second detection characteristics differ from the first detection characteristics;generating first detection data based on the first scan;generating second detection data based on the second scan; andstoring the first detection data and the second detection data within a memory device.

2. The computer-implemented method of claim 1 , wherein the first detection characteristics comprise high spatial resolution, and the second detection characteristics comprise high sensitivity.

3. The computer-implemented method of claim 1 , wherein the first detector ring comprises a first number of crystals and the second detector ring comprises a second number of crystals, wherein the first number differs from the second number.

4. The computer-implemented method of claim 1 , wherein the first detector ring comprises a first number of crystals made of a first material, and the second detectorDocket No.: 2024P21678WO ring comprises a second number of crystals made of a second material, wherein the first material differs from the second material.

5. The computer-implemented method of claim 1 , wherein the first detector ring comprises a first number of detector elements and the second detector ring comprises a second number of detector elements, wherein the first number differs from the second number.

6. The computer-implemented method of claim 1 , further comprising applying a reconstruction process to the first detection data and, based on the application of the reconstruction process, generating a first image.

7. The computer-implemented method of claim 6, further comprising applying the reconstruction process to the second detection data and, based on the application of the reconstruction process, generating a second image.

8. The computer-implemented method of claim 1 , further comprising applying a reconstruction process to the first detection data and the second detection data and, based on the application of the reconstruction process, generating an image.

9. The computer-implemented method of claim 1 , further comprising generating a first signal, the first signal causing a table to move so that a portion of a patient lying on the table is within a field-of-view of the first detector ring.Docket No.: 2024P21678WO10. The computer-implemented method of claim 9, further comprising generating a second signal, the second signal causing the table to move so that the portion of the patient lying on the table is within a field-of-view of the second detector ring.

11. The computer-implemented method of claim 1 , wherein the first scan and the second scan are positron emission tomography (PET) scans.

12. A non-transitory computer readable medium storing instructions that, when executed by at least one processor, cause the at least one processor to perform operations comprising:receiving a first scan captured by a first detector ring of an image scanning system, the first detector ring having first detection characteristics;receiving a second scan captured by a second detector ring of the image scanning system, the second detector ring having second detection characteristics, wherein the second detection characteristics differ from the first detection characteristics;generating first detection data based on the first scan;generating second detection data based on the second scan; andstoring the first detection data and the second detection data within a memory device.Docket No.: 2024P21678WO 13. The non-transitory computer readable medium of claim 12, wherein the first detection characteristics comprise high spatial resolution, and the second detection characteristics comprise high sensitivity.

14. The non-transitory computer readable medium of claim 12, wherein the instructions, when executed by the at least one processor, further cause the at least one processor to perform operations comprising applying a reconstruction process to the first detection data and, based on the application of the reconstruction process, generating a first image.

15. The non-transitory computer readable medium of claim 1 wherein the instructions, when executed by the at least one processor, further cause the at least one processor to perform operations comprising applying the reconstruction process to the second detection data and, based on the application of the reconstruction process, generating a second image.

16. The non-transitory computer readable medium of claim 12 wherein the instructions, when executed by the at least one processor, further cause the at least one processor to perform operations comprising applying a reconstruction process to the first detection data and the second detection data and, based on the application of the reconstruction process, generating an image.

17. An image scanning system comprising:Docket No.: 2024P21678WO a first detector ring having first detection characteristics;a second detector ring having second detection characteristics, wherein the second detection characteristics differs from the first detection characteristics;a memory device; andat least one processor communicatively coupled to the memory device and configured to:receive a first scan captured by a first detector ring of an image scanning system;receive a second scan captured by a second detector ring of the image scanning system;generate first detection data based on the first scan;generate second detection data based on the second scan; and store the first detection data and the second detection data within a memory device.

18. The system of claim 17, wherein the first detection characteristics comprise high spatial resolution, and the second detection characteristics comprise high sensitivity.

19. The system of claim 17, wherein the first detector ring comprises a first number of detector elements and the second detector ring comprises a second number of detector elements, wherein the first number differs from the second number.Docket No.: 2024P21678WO 20. The system of claim 17, wherein the first detector ring comprises a first number of crystals of a first size and the second detector ring comprises a second number of crystals of a second size, wherein the first size differs from the second size.

Images