Database matching using feature evaluation

Abstract

A system and method includes determining a first image metric for a first image of a phantom, the first image being generated by a first imaging system based on first image formation parameters, and the first imaging system and the first image formation parameters being associated with a plurality of reference images; generating a plurality of images of the phantom, each of the plurality of images being generated using a respective different image formation parameter; determining a second image metric for each of the generated plurality of images; identifying an image of the generated plurality of images based on the first image metric and a second image metric for each of the generated plurality of images; generating an image of the object using an image forming parameter for generating the identified one of the generated plurality of images; and comparing the generated image of the object with one or more of a plurality of reference images.

Description

Background Art

[0001] The "Normals" database can be used to help evaluate medical images. The Normals database includes reference medical images of healthy subjects and / or subjects associated with a low likelihood of developing one or more diseases. The Normals database is used in cardiology and neurology, but is not limited to these fields.

[0002] Reference images of healthy / low-likelihood subjects stored in the Normals database are acquired using a specific imaging system, a specific imaging protocol, and a specific image data processing method. Therefore, in order to use the Normals database, images of the patient are acquired using the same imaging system, imaging protocol, and processing method as used to acquire the reference images of the Normals database. The image is then compared to the reference images of the Normals database. For example, if the image differs from the reference image by more than a certain degree, the image can be marked as requiring clinical follow-up.

[0003] Therefore, the reference images of the Normals database are ideally used only to evaluate images acquired in the same manner as the reference images. If images are to be acquired using an imaging system, imaging protocol, and processing method that are any different from those used to acquire the reference images of the Normals database, a new Normals database must be generated.

[0004] The generation of a Normals database is extremely expensive. Therefore, an imaging center that uses a particular Normals database may decide not to upgrade to a new imaging system or a new processing method because such an upgrade may render the particular Normals database unusable. Even if an imaging center decides to request the creation of a new Normals database due to a change in its imaging chain, a considerable amount of time may pass before the new database is available for use.

[0005] Therefore, there is a need for a system that effectively facilitates the evaluation of medical images acquired using imaging systems, imaging protocols, and processing methods relative to a Normals database of reference images acquired using different imaging systems, imaging protocols, and / or processing methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0006] Figure 1 is a block diagram of a system for determining image forming parameters of an imaging system based on a reference imaging system and a phantom according to some embodiments;

[0007] Figure 2 is a block diagram of a system for generating an image for comparison with a reference image according to some embodiments;

[0008] Figure 3is a flow chart of a process for determining image formation parameters for an imaging system based on a reference imaging system and a phantom and using the image formation parameters to generate an image for comparison with the reference image according to some embodiments;

[0009] Figure 4A and Figure 4B is a view of a phantom according to some embodiments;

[0010] Figure 5 is a graph of image resolution versus image noise for various image formation parameters of an imaging system according to some embodiments;

[0011] Figure 6 is a block diagram of a system for converting a reference image based on a target imaging system and a phantom according to some embodiments;

[0012] Figure 7 is a block diagram of a system for generating an image for comparison with a transformed reference image according to some embodiments;

[0013] Figure 8 is a flow chart of a process for transforming a reference image and generating an image for comparison with the transformed reference image based on a target imaging system and a phantom according to some embodiments; and

[0014] Fig. 9 is a diagram of an imaging system according to some embodiments. DETAILED DESCRIPTION

[0015] The following description is provided to enable anyone skilled in the art to make and use the described embodiments and to set forth the best mode of implementing the described embodiments. However, various modifications will still be apparent to those skilled in the art.

[0016] Some embodiments use a phantom to establish a relationship between a first imaging system and a first image formation parameter and a second imaging system and a second image formation parameter. The first imaging system and the first image formation parameter can be associated with a reference image of the Normals database, and the imaging system and the second image formation parameter will be used to acquire an image to be compared with the reference image. For the purposes of this description, image formation parameters include any parameters associated with data acquisition (e.g., acquisition time, injection distribution, sensitivity) and / or reconstruction of an image from the acquired data (e.g., noise reduction, reconstruction algorithm, number of updates, post-smoothing).

[0017] In some embodiments, based on a first image of a phantom acquired using a first imaging system and first image forming parameters, and based on a second image of the same phantom acquired using a second imaging system and various second image forming parameters, second image forming parameters to be used by a second imaging system are determined. The determined second image forming parameters may be those that result in the phantom image having a metric value that is most similar to the metric value of the first image. The metric values ​​may include noise and edge resolution, but embodiments are not limited thereto. The determined second image forming parameters may then be used to acquire an image of the patient using the second imaging system and compared to the reference image.

[0018] In other embodiments, a first image of a phantom is acquired using a first imaging system and first image forming parameters, and a second image of the same phantom is acquired using a second imaging system and second image forming parameters. Metric values ​​for the first image and the second image are determined. Next, based on the metric values, a mapping is determined to convert the first image into an image having metric values ​​similar to the second image. The mapping is applied to reference images of the Normals database to generate a set of converted reference images associated with the second imaging system and the second image forming parameters. An image of the patient can then be acquired by the second imaging system using the second image forming parameters and compared to the converted reference images.

[0019] Figure 1 A system 100 according to some embodiments is shown. Each component of the system 100 and each other component described herein can be implemented using any combination of hardware and / or software. Some components can share the hardware and / or software of one or more other components.

[0020] The system 100 includes a database 110 storing reference images 112. The database 110 may include the Normals database as described above, but the embodiments are not limited thereto. The reference images 112 include images of a given model of imaging system (ie, Sys A 122) and a specific set of image formation parameters (i.e., parameters A 124) to obtain the image. As mentioned above, the parameters A 124 may include a control system A The operation of 122 to acquire data or the image reconstruction component 126 to reconstruct an image based on the acquired data. A 122 and the image reconstruction component 126 will be collectively referred to herein as the image forming system 120 .

[0021] Embodiments are not limited to any particular imaging modality. For example, Sys A122 may include a single photon emission computed tomography (SPECT) system, a positron emission tomography (PET) system, a magnetic resonance (MR) system, a computed tomography (CT) system, or any other known or soon-to-be-known system for generating medical images. In some embodiments, the system A 122 includes a specific model of imaging system (eg, Siemens Symbia Evo).

[0022] Sys B 152 may include any type and model of imaging system. According to some embodiments, Sys B 152 Use and Sys A 122 images acquired using the same imaging modality (eg, SPECT) but with different types of imaging systems. B 152 Model and Sys A The models of 122 may be manufactured by different companies or by the same company. In some embodiments, Sys A 122 and Sys B 152 is the same model imaging system.

[0023] According to some embodiments, imaging system 120 is based on parameters A 124 generates an image 135 of the phantom 130a. Specifically, Sys A 122 Based on parameters A 124 executes the imaging protocol, and the image reconstruction component 126 performs the imaging protocol based on the parameters A Any reconstruction or data processing parameter values ​​of 124 (e.g., reconstruction type, number of updates (ie, iterations), level of post-smoothing) reconstruct the resulting data.

[0024] The phantom 130a may include a A Any object visible in the imaging modality of 122. Figure 4A and Figure 4B An example of phantom 130a is described, but embodiments are not limited thereto. Phantom 130a may exhibit characteristics that result in image 135 from which desired image metrics may be consistently determined using known techniques.

[0025] Metric determination component 140 determines one or more image metrics of image 135. Image metrics may include, but are not limited to, edge resolution, noise, capture activity (in Sys A 122 is molecular and the phantom 130a includes a radionuclide that emits photons) and sphericity (in the case where the phantom 130a includes a detectable spherical object).

[0026] Similarly, but not necessarily simultaneously, the image forming system 150 generates an image based on the parameters 1-n 154 generates an image 155 of the phantom 130b. In particular and according to some embodiments, the image forming system 150 generates N images 155, wherein each of the N images 155 is generated using a different set of parameters. 1-n 154 generated. Different sets of parameters 1-n 154 can be varied in any number of ways. For example, the first plurality of sets of parameters 1-n 154 can specify a first reconstruction algorithm and different corresponding post-smoothing levels, while a second plurality of sets of parameters 1-n 154 A second reconstruction algorithm and a different corresponding post-smoothing level may be specified.

[0027] Phantom 130b may be the same physical object as phantom 130a or a replica thereof. For example, phantoms 130a and 130b may include different objects, but the same model of the phantom has the same size and configuration. If phantom 130a is loaded with radionuclides prior to imaging by system 120, phantom 130b is similarly loaded prior to imaging by system 150.

[0028] Metric determination component 160 determines one or more image metrics for each image 155. Next, metric comparison component 170 compares the metric determined by metric determination component 140 with the metric determined by metric determination component 160. The comparison may be intended to determine one of images 155 whose metric is closest to the metric determined for image 135. Parameter determination component 180 then determines the parameters 1-n 154 is used to obtain the determined one of the images 155. The determined parameters are used as parameters B 190 output.

[0029] According to some embodiments, the image forming system 150 generates one or more images 155 based on the corresponding parameters 154, and the metric determination component 160 determines its metric as described above. The metric comparison component 170 compares the metric with the metric determined for the image 135 and outputs the comparison result to the parameter determination component 180. In contrast to the previous example, the parameter determination component 180 generates one or more sets of parameters based on the comparison, and the image forming system 150 generates one or more new images 155 using the one or more sets of parameters. The parameter determination component 180 can generate one or more sets of parameters in an attempt to reduce the difference between the metric determined for the image 155 and the metric determined for the image 135. The foregoing process can continue until the difference between the metric determined for the particular image 155 and the metric determined for the image 135 is within a threshold. The parameter determination component 180 outputs the parameters used to obtain the particular image 155 as parameters. B 190.

[0030] Figure 2 is a block diagram of a system 200 for generating an image for comparison with a reference image using parameters determined by system 100 according to some embodiments. For example, assuming that the parameters B 190 as reference Figure 1 is predetermined as described. Figure 2 As shown, Figure 1 The image forming system 150 is based on the parameters B 190 generates an image 220 of the patient 210 . The image 220 may include an image of any portion of the patient 210 .

[0031] Attributed to the determination parameters B 190, the image metrics of the image 220 are assumed to be similar to the image metrics of the reference image 112 of the database 110, thereby facilitating their comparison. Therefore, the image comparison component 230 compares the image 220 to the reference image 112 using any known image comparison techniques and algorithms, including but not limited to visual comparisons performed by humans.

[0032] Based on the comparison, the image comparison component 230 may output a clinical task 240. For example, if the image 220 is not suitably similar to certain images in the reference images 112, the image comparison component 230 may output a clinical task 240 indicating further clinical tests to be performed on the patient 210. Thus, embodiments may facilitate using a Normals database containing reference images associated with a first imaging system and first image formation parameters to evaluate images generated using a second imaging system and second image formation parameters.

[0033] Figure 3 is a flow chart of a process 300 for determining image formation parameters of an imaging system based on a reference imaging system and a phantom and using the image formation parameters to generate an image for comparison with the reference image, according to some embodiments. In some embodiments, various hardware elements (e.g., one or more processing units such as one or more processors, one or more processor cores, and one or more processor threads) execute program code to perform the process 300. The steps of the process 300 need not be performed by a single device or system, nor need they be performed adjacent in time to each other or in the order shown.

[0034] Process 300 and all other processes mentioned herein may be embodied in executable program code that is read from one or more non-transitory computer-readable media, such as disk-based or solid-state hard drives, DVD-ROMs, flash drives, and tapes, and then stored in a compressed, uncompiled, and / or encrypted format. In some embodiments, hardwired circuitry may be used in place of or in conjunction with program code for implementing processes according to some embodiments. Thus, embodiments are not limited to any particular combination of hardware and software.

[0035] Process 300 may be performed by an imaging system vendor or imaging center that wishes to use a Normals database associated with a first imaging system and first image formation parameters to evaluate images generated using a second imaging system and second image formation parameters. As mentioned above, in some embodiments, the first imaging system and the second imaging system may be the same imaging system model.

[0036] In some examples, S310-S330 may be performed by one entity (e.g., an entity that owns the first imaging system), and S340-S380 may be performed by another entity (e.g., an entity that owns the second imaging system). In still other examples, S310-S330 may be performed by one entity (e.g., an entity that owns the first imaging system), S340-S360 may be performed by another entity (e.g., an entity that owns the second imaging system), and S370-S380 may be performed by yet another entity (e.g., an entity that uses the second imaging system or a third imaging system of the same model as the second imaging system to acquire and evaluate patient images using the Normals database).

[0037] Initially, at S310, a plurality of reference images are determined. The plurality of reference images are associated with a first imaging system (e.g., a specific model of a SPECT imaging system) and first image formation parameters. Each of the plurality of reference images may be acquired using the first imaging system and the first image formation parameters and stored in the Normals database as described above.

[0038] Next, at S320, a first image of the phantom is generated using the first imaging system and the first image formation parameters. The generation of the first image may include acquiring a plurality of projection images and reconstructing the projection images into a three-dimensional image. In some embodiments, more than one image is generated at S320.

[0039] The phantom may include any object visible to the first imaging system. Figure 4A is a side view of a phantom 400 according to some embodiments, and Figure 4B4 is a top view thereof. Phantom 400 is cylindrical and includes thirteen hollow spheres, which can be loaded with photon emitting materials, contrast agents, etc., depending on the imaging modality. Phantom 400 and the spheres included therein can be constructed of acrylic material, but the embodiment is not limited thereto. In some embodiments, one or more spheres are loaded with contrast agents and photon emitting materials, and other spheres are loaded with contrast agents without photon emitting materials. The remaining volume of phantom 400 can be filled with water and an appropriate amount of photon emitting material to achieve a desired background activity level.

[0040] At S330, a first image metric of the first image is determined. The determined first image metric may include edge resolution, noise, uptake activity, and sphericity. Each of these metrics may be determined using known techniques. A method for determining noise and edge resolution is described in "Vija, A. Hans, Siemens Healthineers, and Molecular Imaging Business Line." xSPECTreconstruction method." White Paper Order A91MI-10462 (2017): T1-7600", the contents of which are incorporated herein by reference for all purposes.

[0041] A plurality of images of the phantom are generated at S340. The phantom may be the same physical object as the phantom of S320, or a different instance of the same phantom model having the same size and structure. The phantom imaged at S340 is preferably loaded with the same material at the same concentration as the phantom imaged at S320.

[0042] Each of the plurality of images of the phantom is generated using image formation parameters that are different from the first image formation parameters. The plurality of images are generated using a second imaging system, which may or may not be the same model as the first imaging system. For example, the second imaging system may be the same model as the first imaging system, but the reconstruction method specified by the different image formation parameters may be different from the reconstruction method specified by the first image formation parameters.

[0043] At S350, an image metric is determined for each of the plurality of images. The image metric may be determined in the same manner as described above with respect to S330. Next, at S360, an image in the plurality of images is identified based on the first image metric and the image metric of each of the plurality of images. For example, the first image metric may be compared to each of the plurality of determined image metrics to identify a closest set of image metrics in the plurality of determined image metrics. At S360, an image in the plurality of images is identified that was generated using the closest set of image metrics in the plurality of determined image metrics.

[0044] Because one or more image metrics may be determined for each image, identifying the closest set of image metrics may include determining a feature vector for each of the plurality of images. The feature vector of an image represents the value of the image metric determined for the image. The weights of certain metrics may be different from other metrics in the feature vector. Thus, S360 may include determining which of the feature vectors of the plurality of images is closest in feature space to the feature vector of the first image of the phantom.

[0045] In some embodiments, S360 includes determining whether the image accurately represents the phantom. In this regard, one or more metrics may be evaluated against expected metric values, given prior knowledge of the physical properties of the phantom. For example, at S360, an image associated with values ​​of a shape deformation (or sphericity) metric, an activity metric, and / or a consistency metric that fall outside of an expected range may be disqualified from consideration, regardless of whether values ​​of other metrics for the image (e.g., edge resolution and noise) are closest to corresponding metric values ​​for the first image.

[0046] At S370, an image of the patient is generated using the image formation parameters used to generate the image identified at S360. Due to the previous steps, it is assumed that the image metrics of the generated image are similar to the image metrics of the reference image determined at S310. Therefore, at S380, the image of the patient is compared to one or more of the plurality of reference images. As a result of the above comparison, a clinical task can be generated.

[0047] Figure 5 A graph 500 of image resolution versus image noise for various image formation parameters of an imaging system according to some embodiments is depicted. In this example, an image of a phantom is generated at S320 using an imaging system model (i.e., ND imaging system) and image formation parameters (i.e., Flash 3D (F3D) 10i8s8.0g) associated with a Normals database of reference images. Indicators 510 depict values ​​of edge resolution and noise of the image determined at S330.

[0048] Assume that it is desired to use the Normals database with a second, different imaging system model. However, for purposes of explanation, indicator 520 indicates edge resolution and noise of a phantom image acquired by the second imaging system model using image forming parameters associated with the Normals database (i.e., F3D 10i8s8.0g). Due to the differences in the metrics indicated by indicators 510 and 520, it is inappropriate to compare an image acquired by the second imaging system model using image forming parameters associated with the Normals database with a reference image of the Normals database.

[0049] Each of the curves 530-560 represents noise and edge resolution of a phantom image acquired using a second imaging system model and a different corresponding set of image forming parameters (i.e., a different reconstruction voxel size). Each vertical line of each curve represents a metric value for an image generated using the image forming parameters of the curve and a different level of post-smoothing. At certain levels of post-smoothing, the image forming parameters associated with curves 540, 550, and 560 can generate a phantom image with a resolution that is the same as or better than the resolution indicated by indicator 510 at a lower noise level.

[0050] However, S360 includes determining the best matching set of resolution and noise metrics. It can be determined at S360 that point 545 of curve 540 is closest to indicator 510. Therefore, the image generated using the image formation parameters associated with curve 540 and the post-smoothing level associated with point 545 is the image determined at S360. Therefore, at S370, an image of the patient is generated using the image formation parameters associated with curve 540 and the post-smoothing level associated with point 545 for comparison with the reference image of the Normals database.

[0051] In some embodiments, S340-S360 may be iterative. For example, one or more images of the phantom are generated using different image formation parameters at S340, image metrics for each image are determined at S350, and it is determined at S360 whether any metric in the plurality of sets of metrics is acceptably close to a first image metric of the first image. If so, it may also be determined whether the image metrics corresponding to the image of the closest set of metrics are physically accurate (e.g., with respect to shape deformation, activity, and consistency).

[0052] If no metric is acceptably close to the first image metric of the first image, or if such a close metric is not physically accurate, the process returns to S340 to acquire another one or more images using different image formation parameters. The different image formation parameters can be determined based on the difference between the previously determined image metric and the first image metric. Once a set of metrics that are acceptably close to and physically accurate to the first image metric of the first image are identified, the process proceeds from S360 to S370.

[0053] Figure 6 6 is a block diagram of a system 600 for converting reference images based on a target imaging system and a phantom according to some embodiments. The system 600 includes a database 630 storing reference images 632. The reference images 632 include images obtained using a given model of imaging system (i.e., Sys A 612) and a specific set of image formation parameters (i.e., parameters A 614) The image is acquired. Parameters A 614 may include a control system for controllingA The operation of 612 acquires data or any parameters based on which the image reconstruction component 616 reconstructs the image from the acquired data. A 612 and image reconstruction component 616 are collectively referred to herein as image forming system 610 .

[0054] The image forming system 610 is based on the parameters A 614 generates an image 635 of the phantom 620a. Specifically, Sys A 612 Based on parameters A 614 executes the imaging protocol, and the image reconstruction component 616 performs the image reconstruction based on the parameters A The metric determination component 640 determines one or more image metrics of the image 635 as described above.

[0055] Sys C 652 may include any type and model of imaging system. C 652 can be used with Sys A 612 The same imaging modality (eg, SPECT) is used to acquire images, but includes different types of imaging systems. C 652 Model and Sys A The models of 612 may be manufactured by different companies, or may be manufactured by the same company. In some embodiments, Sys A 612 and Sys C 652 is the same imaging system model.

[0056] The image forming system 650 is based on the parameters C 654 generates an image 655 of phantom 620b. Phantoms 620a and 620b may include different objects, but the same model of the phantom has the same size and structure. If phantom 620a is loaded with radionuclides before being imaged by system 120, then phantom 620b is similarly loaded before being imaged by system 150. Parameters C 654 may include a set of default parameters, a set of preferred parameters for the imaging system 652 in terms of image quality, speed, and / or the area of ​​the patient to be imaged. Figure 1-3 The implementations discussed are different, the parameters C 654 is a "target" parameter used in conjunction with image forming system 650, rather than based on image forming system 610 or parameter A614 Parameters determined based on relevant considerations.

[0057] Metric determination component 660 determines one or more image metrics for image 655. Next, mapping determination component 665 determines a mapping based on the metrics determined by components 640 and 660. In one example, mapping determination component 665 determines a mapping that will cause image 635 to exhibit similar metric values ​​as image 655. Such a mapping may specify a mapping for Sys A 614 A different reconstruction algorithm or different reconstruction parameters are applied to the acquired data.

[0058] The image conversion component 675 applies the determined mapping to the reference image 632 to generate a converted image 682. The converted image 682 can be stored in the updated Normals database 680 along with the reference image 632. As shown in the updated Normals database 680, the converted image 682 is consistent with the Sys C 652 and parameters C 654 associated.

[0059] Figure 7 is a block diagram of a system 700 for generating an image for comparison with a reference image converted by system 600 according to some embodiments. As shown, image forming system 650 generates an image based on parameters C 654 generates an image 720 of the patient 710. The image 720 may include an image of any portion of the patient 710.

[0060] The image comparison component 730 compares the image 720 to the transformed image 682 using any known image comparison techniques and algorithms, including but not limited to visual comparison performed by a human. The image comparison component 730 can output a clinical task 740 based on the comparison. Figure 6 and Figure 7 The depicted embodiments may also facilitate using a Normals database containing reference images associated with a first imaging system and first image formation parameters to evaluate images generated using a second imaging system and second image formation parameters.

[0061] Figure 8 8 is a flow chart of a process 800 for transforming a reference image based on a target imaging system and a phantom and generating an image for comparison with the transformed reference image according to some embodiments. The process 800 may be performed by an imaging system vendor or imaging center that wishes to evaluate an image generated using a second imaging system and second image formation parameters using a Normals database associated with a first imaging system and first image formation parameters. As mentioned above, in some embodiments, the first imaging system and the second imaging system may be the same imaging system model.

[0062] S810-S830 may be performed as described above with respect to S310-S330 of process 300. Next, at S840, a second image of the phantom is generated using image formation parameters different from the first image formation parameters used at S820. The second image is generated using a second imaging system, which may or may not be the same model as the first imaging system used at S820. For example, the second imaging system may be the same model as the first imaging system, but the reconstruction method specified by the different image formation parameters may be different from the reconstruction method specified by the first image formation parameters.

[0063] At S850, an image metric is determined for the second image. Next, at S860, a mapping is determined based on the first image metric and the second image metric. In some embodiments, S860 includes determining a reprocessing step to be applied to the first image so that the reprocessed first image exhibits image metrics that are appropriately close to the image metrics of the second image. The reprocessing step may include a reconstruction algorithm that includes a particular number of updates and / or a level of post-smoothing. The determination at S860 may include reprocessing the first image in several different ways and selecting the reprocessing step that generates an image that exhibits image metrics that are closest to the image metrics of the second image.

[0064] At S870, each of the plurality of reference images is transformed based on the determined mapping. At S880, a second imaging system generates an image of the patient using second image formation parameters. S880 may occur at a different time and location than S870, and the second imaging system of S880 may include a different instance but the same model as the second imaging system of S870. It may be assumed that image metrics of the image generated at S880 are similar to image metrics of the reference image transformed at S870. At S890, the image of the patient is compared to one or more of the plurality of transformed reference images, possibly resulting in the generation of a clinical task.

[0065] Fig. 9 An imaging system 900 according to some embodiments is shown. System 900 is a SPECT imaging system known in the art, but embodiments are not limited thereto. Each component of system 900 may include other elements necessary for its operation, as well as additional elements for providing functionality other than that described herein.

[0066] The system 900 includes a housing 910 of a gantry 902 to which two or more gamma cameras 904a, 904b are attached, although any number of gamma cameras may be used. A detector within each gamma camera detects gamma photons (i.e., emission data) emitted by a radioactive tracer injected into a patient 906 lying on a bed 908. The bed 908 is slidable along a motion axis A. In a corresponding bed position (i.e., imaging position), a portion of the patient 906's body is located between the gamma cameras 904a, 904b to capture emission data from the body portion from various projection angles.

[0067] The control system 920 may include any general or special purpose computing system. The control system 920 includes one or more processing units 922 and a storage device 930 for storing program code, the processing unit 922 being configured to execute executable program code to cause the system 920 to operate as described herein. The storage device 930 may include one or more fixed disks, solid state random access memory, and / or removable media (e.g., a thumb drive) installed in a corresponding interface (e.g., a USB port).

[0068] The storage device 930 stores program code of a control program 931. The one or more processing units 922 can execute the control program 931 to control the motors, servos, and encoders in conjunction with the SPECT system interface 924 to rotate the gamma cameras 904a, 904b along the gantry 902 and acquire two-dimensional emission data 932 at defined imaging positions during the rotation.

[0069] The control program 931 may further be executed to reconstruct an image 933 based on specified parameters. The specified parameters may have been as described with respect to Figure 1 and Figure 3 934 to facilitate comparison of image 933 with reference image 934. In other embodiments, reference image 934 includes a reference image that has been transformed based on specified parameters of system 900, as described above with respect to Figure 6 and Figure 8 described.

[0070] Terminal 940 may include a display device and an input device coupled to terminal interface 925 of system 920. Terminal 940 may receive and display image 933 and reference image 934, as well as a user interface to facilitate comparison therebetween. In some embodiments, terminal 940 is a separate computing device, such as, but not limited to, a desktop computer, a laptop computer, a tablet computer, and a smart phone.

[0071] Those skilled in the art will appreciate that various adjustments and modifications may be configured to the above-described embodiments without departing from the claims. Therefore, it should be understood that the claims may be practiced in ways other than those specifically described herein.

Claims

1. A method comprising: determining a first image metric of a first image of a phantom, the first image being generated by a first imaging system based on first image formation parameters, and the first imaging system and the first image formation parameters being associated with a plurality of reference images; generating a plurality of images of the phantom, each of the plurality of images being generated using a respective different image formation parameter; determining a second image metric for each of the generated plurality of images; identifying an image in the plurality of generated images based on the first image metric and a second image metric for each of the plurality of generated images; generating an image of the object using image formation parameters used to generate the identified one of the generated plurality of images; as well as The generated image of the object is compared to one or more of the plurality of reference images. 2 . The method of claim 1 , wherein the first image metric comprises edge resolution and noise, and the second image metric comprises edge resolution and noise.

3. The method of claim 2, wherein the first image metric comprises uptake activity and sphericity, and the second image metric comprises uptake activity and sphericity. 4 . The method of claim 3 , wherein identifying one of the generated plurality of images comprises identifying one of the generated plurality of images that is associated with a second image metric that is closest to the first image metric. 5 . The method of claim 2 , wherein identifying one of the generated plurality of images comprises identifying one of the generated plurality of images that is associated with a second image metric that is closest to the first image metric.

6. The method of claim 1, wherein a plurality of images of the phantom are generated using a second imaging system, and an image of the subject is generated using the second imaging system.

7. A method comprising: determining a first image metric of a first image of a phantom, the first image being generated by a first imaging system based on first image formation parameters, and the first imaging system and the first image formation parameters being associated with a plurality of reference images; generating a second image of the phantom using a second imaging system and second image formation parameters; determining a second image metric for the second image; determining a mapping based on the first image metric and the second image metric; converting each of the plurality of reference images into a second plurality of reference images based on the mapping; generating an image of the object using the second imaging system and the second image formation parameters; as well as The generated image of the object is compared to one or more of the converted plurality of reference images.

8. The method of claim 7, wherein the first image metric comprises edge resolution and noise, and the second image metric comprises edge resolution and noise.

9. The method of claim 8, wherein the first image metric comprises uptake activity and sphericity, and the second image metric comprises uptake activity and sphericity.

10. The method of claim 8, wherein determining a mapping comprises determining a reconstruction parameter based on the first image metric and the second image metric.

11. The method according to claim 7, further comprising: The converted multiple reference images are stored together with the multiple reference images.

12. The method according to claim 7, further comprising: generating a third image of the phantom using a third imaging system and third image formation parameters; determining a third image metric of the third image; determining a second mapping based on the first image metric and the third image metric; converting each of the plurality of reference images into a second converted plurality of reference images based on the second mapping; as well as The second converted plurality of reference images are stored together with the converted plurality of reference images and the plurality of reference images.

13. A non-transitory computer readable medium storing program code, the program code being executable by a processing unit to perform the following operations: determining a first image metric of a first image of a phantom, the first image being generated by a first imaging system based on first image formation parameters, and the first imaging system and the first image formation parameters being associated with a plurality of reference images; generating a plurality of images of the phantom, each of the plurality of images being generated using a respective different image formation parameter; determining a second image metric for each of the generated plurality of images; identifying an image in the plurality of generated images based on the first image metric and a second image metric for each of the plurality of generated images; generating an image of the object using image formation parameters used to generate the identified one of the generated plurality of images; as well as The generated image of the object is compared to one or more of the plurality of reference images.

14. The medium of claim 13, wherein the first image metric comprises edge resolution and noise, and the second image metric comprises edge resolution and noise.

15. The medium of claim 14, wherein the first image metric comprises uptake activity and sphericity, and the second image metric comprises uptake activity and sphericity.

16. The medium of claim 15, wherein identifying one of the generated plurality of images comprises identifying one of the generated plurality of images that is associated with a second image metric that is closest to the first image metric.

17. The medium of claim 15, wherein identifying one of the generated plurality of images comprises identifying one of the generated plurality of images that is associated with a second image metric that is closest to the first image metric.

18. The medium of claim 13, wherein the plurality of images of the phantom are generated using a second imaging system, and the image of the subject is generated using the second imaging system.

Images