Database Matching Using Feature Evaluation

Abstract

The system and method include 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 imaging parameters, the first imaging system and the first imaging 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 different imaging parameter, determining a second image metric for each of the generated plurality of images, identifying one of the generated plurality of images based on the first image metric and the second image metric for each of the generated plurality of images, generating an image of the object using the imaging parameters used to generate the identified one of the generated plurality of images, and comparing the generated image of the object with one or more of the plurality of reference images.

Description

[Background technology]

[0001] "Normals" databases are used to assist in the evaluation of medical images. Normals databases contain reference medical images of healthy subjects and / or subjects with a low probability of developing one or more diseases. Normals databases are used in cardiology and neurology, but are not limited to these fields.

[0002] The reference images of healthy / low accuracy subjects stored in the Normals database are acquired using a specific imaging system, a specific imaging protocol, and a specific image data processing method. Thus, to use the Normals database, an image of a patient is acquired using the same imaging system, imaging protocol, and processing method used to acquire the reference image in the Normals database. The image is then compared to the reference image in the Normals database. If the image differs from the reference image by more than a certain amount, for example, the image is flagged as requiring clinical follow-up.

[0003] For this reason, a reference image in a Normals database is ideally used only to evaluate images acquired in the same way as the reference image. If an image is acquired using an imaging system, imaging protocol, and processing method that differs from those used to acquire the reference image in the Normals database, a new Normals database must be constructed.

[0004] Building a Normals database is very costly. Therefore, an imaging center using a particular Normals database may decide not to upgrade to a new imaging system or new processing method because the upgrade may render that particular Normals database unusable. Even if an imaging center decides to require building a new Normals database due to changes in its imaging chain, it may take a considerable amount of time before the new database is available for use.

[0005] Therefore, it is desirable for a system to efficiently facilitate the evaluation of medical images acquired using an imaging system, imaging protocol, and processing method against a normals database of reference images acquired using another imaging system, imaging protocol, and / or processing method. [Brief explanation of the drawings]

[0006] [Figure 1] FIG. 1 is a block diagram of a system for determining imaging parameters of an imaging system based on a reference imaging system and a phantom, according to one embodiment. [Figure 2] 1 is a block diagram of a system for generating an image for comparison with a reference image, according to one embodiment. [Figure 3] 1 is a flow diagram of a process for determining imaging parameters of an imaging system based on a reference imaging system and a phantom, and generating an image using the imaging parameters for comparison to a reference image, according to one embodiment. [Figure 4] 4A and 4B are diagrams of a phantom, according to one embodiment. [Figure 5] 10 is a graph of image resolution versus image noise for various imaging parameters of an imaging system, according to one embodiment. [Figure 6] 1 is a block diagram of a system for transforming a reference image based on a target imaging system and a phantom, according to one embodiment. [Figure 7]1 is a block diagram of a system for generating an image for comparison with a transformed reference image, according to one embodiment. [Figure 8] 1 is a flow diagram of a process 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 one embodiment. [Figure 9] 1 is a diagram of an imaging system according to one embodiment. Detailed Description

[0007] The following description is provided to enable one of ordinary skill in the art to make and use the disclosed embodiments and sets forth the best modes contemplated for carrying out the disclosed embodiments, although various modifications will be apparent to those skilled in the art.

[0008] Some embodiments use a phantom to establish a relationship between a first imaging system and first imaging parameters and a second imaging system and second imaging parameters. The first imaging system and first imaging parameters are associated with a reference image in a Normals database, while the second imaging system and second imaging parameters are used to acquire an image that is compared to the reference image. For purposes of this description, imaging parameters include various parameters related to the acquisition of data (e.g., acquisition time, injection profile, sensitivity) and / or the reconstruction of an image from the acquired data (e.g., noise reduction, reconstruction algorithm, number of updates, post-smoothing).

[0009] In one embodiment, second imaging parameters to be used by the second imaging system are determined based on a first image of a phantom acquired using a first imaging system and the first imaging parameters, and second images of the same phantom acquired using a second imaging system and different second imaging parameters. The determined second imaging parameters are those that result in an image of the phantom having metric values ​​most similar to the metric values ​​of the first image. The metric values ​​include, but are not limited to, noise and edge resolution. An image of the patient is then acquired by the second imaging system using the determined second imaging parameters and compared to the reference image.

[0010] In another embodiment, a first image of a phantom is acquired using a first imaging system and first imaging parameters, and a second image of the same phantom is acquired using a second imaging system and second imaging parameters. Metric values ​​for the first and second images are determined. A mapping is then determined based on the metric values ​​to transform the first image into an image having similar metric values ​​to the second image. The mapping is applied to reference images in the Normals database to generate a set of transformed reference images associated with the second imaging system and second imaging parameters. Images of the patient are then acquired with the second imaging system using the second imaging parameters and compared to the transformed reference images.

[0011] 1 illustrates a system 100, according to one embodiment. Each component of system 100, and each of the other components described herein, may be implemented using a combination of hardware and / or software. Some components may share the hardware and / or software of one or more other components.

[0012] The system 100 includes a database 110 that stores reference images 112. The database 110 may include a Normals database as described above, but the embodiment is not limited thereto. The reference images 112 are stored in a database 110 that stores reference images 112 for a predetermined model of an imaging system (i.e., System A). A ) 122) and a specific set of imaging parameters (i.e., parameters A (PARAMETERS A ) 124). As noted above, parameters A 124 may include parameters used to control the operation of system A 122 to acquire data, or may include parameters taken into account by image reconstructor 126 when reconstructing an image from the acquired data. System A 122 and image reconstructor 126 are also collectively referred to herein as imaging system 120.

[0013] Embodiments are not limited to any particular imaging modality. For example, system 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 other systems for producing medical images that are known or become known. In one example, system A122 includes a particular model of imaging system (e.g., a Siemens Symbia Evo).

[0014] System B (SYS B ) 152 comprises a certain type and model of imaging system. According to one embodiment, system B 152 acquires images using the same imaging modality (e.g., SPECT) as system A 122, but comprises a different model of imaging system. The model of system B 152 and the model of system A 122 may be manufactured by different companies or by the same company. In one embodiment, system A 122 and system B 152 are the same model of imaging system.

[0015] According to one embodiment, imaging system 120 generates image 135 of phantom 130a based on parameters A124. Specifically, system A122 executes an imaging protocol based on any corresponding parameter values ​​in parameters A124, and image reconstructor 126 reconstructs the resulting data based on any reconstruction or data processing parameter values ​​in parameters A124 (e.g., type of reconstruction, number of updates (i.e., iterations), level of post-smoothing).

[0016] The phantom 130a includes any object visible to the imaging modality of the system A 122. An example of the phantom 130a is described with respect to Figures 4A and 4B, but the embodiment is not limited thereto. The phantom 130a exhibits properties that result in an image 135 from which desired image metrics can be consistently determined using known techniques.

[0017] The metric determiner 140 determines one or more image metrics of the image 135. The image metrics include, but are not limited to, edge resolution, noise, uptake activity (if the imaging modality of the system A 122 is molecular and the phantom 130a includes photon-emitting radionuclides), and sphericity (if the phantom 130a includes detectable spherical objects).

[0018] Similarly, but not necessarily simultaneously, imaging system 150 may also generate parameters 1-n (PARAMETERS 1-n ) 154 to generate an image 155 of the phantom 130b. In particular, according to one embodiment, the imaging system 150 generates N images 155, each of the N images 155 generated using a different set of parameters 1-n 154. The different sets of parameters 1-n 154 can vary in any number of ways. For example, a first plurality of sets of parameters 1-n 154 specify a first reconstruction algorithm and respective different post-smoothing levels, while a second plurality of sets of parameters 1-n 154 specify a second reconstruction algorithm and respective different post-smoothing levels.

[0019] Phantom 130b is the same physical object as phantom 130a or a replica thereof. For example, phantom 130a and phantom 130b may be comprised of different objects, but the same model of a phantom having the same dimensions and configuration. If phantom 130a is loaded with a radionuclide prior to imaging with system 120, phantom 130b is similarly loaded prior to imaging with system 150.

[0020] The metric determiner 160 determines one or more image metrics for each of the images 155. The metric comparator 170 then compares the metrics determined by the metric determiner 140 with the metrics determined by the metric determiner 160. This comparison is intended to determine one of the images 155 whose metrics are closest to the metrics determined for the image 135. The parameter determiner 180 then determines which of the parameters 1-n 154 were used to obtain the determined one of the images 155. The determined parameters are then used as parameters B (PARAMETERS B )190.

[0021] According to one embodiment, the imaging system 150 generates one or more images 155 based on each parameter 154, and the metric determiner 160 determines the metric as described above. The metric comparator 170 compares the metric with the metric determined for the image 135 and outputs the comparison result to the parameter determiner 180. Conversely to the previous example, the parameter determiner 180 generates one or more parameter sets based on the comparison, and the imaging system 150 generates one or more new images 155 using the one or more parameter sets. The parameter determiner 180 generates one or more parameter sets in an attempt to reduce the difference between the metric determined for the image 155 and the metric determined for the image 135. This 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 determiner 180 outputs the parameters used to obtain the particular image 155 as parameter B 190.

[0022] Figure 2, according to one embodiment, is a block diagram of a system 200 that generates an image for comparison with a reference image using parameters determined by system 100. For example, assume that parameters B 190 were previously determined as described with respect to Figure 1. As shown in Figure 2, imaging system 150 of Figure 1 generates an image 220 of patient 210 based on parameters B 190. Image 220 may include an image of any portion of patient 210.

[0023] As a result of the method by which parameters B190 are determined, the image metrics of image 220 are assumed to be similar to the image metrics of reference images 112 in database 110, facilitating their comparison. Image comparator 230 therefore compares image 220 with reference images 112 using known image comparison techniques and algorithms, including, but not limited to, visual comparisons performed by a human.

[0024] Based on this comparison, the image comparator 230 outputs a clinical action 240. For example, if the image 220 does not adequately resemble a particular one of the reference images 112, the image comparator 230 may output a clinical action 240 that prescribes further clinical testing to be performed on the patient 210. Thus, the present embodiment facilitates the use of a Normals database that includes reference images associated with a first imaging system and first imaging parameters to evaluate images generated using a second imaging system and second imaging parameters.

[0025] 3 is a flow diagram of a process 300 for determining imaging parameters of an imaging system based on a reference imaging system and a phantom and generating an image using the imaging parameters for comparison to a reference image. In one embodiment, 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 process 300. The steps of process 300 need not be performed by a single device or system, nor need they be performed adjacent to each other in time or in the order presented.

[0026] Process 300, and all other processes described herein, may be implemented in executable program code that is read from one or more non-transitory computer-readable media, such as disk-type or solid-state hard drives, DVD-ROMs, flash drives, magnetic tapes, etc., and that may be stored in a compressed, uncompiled, and / or encrypted format. In one embodiment, hardwired circuitry may be used in place of or in combination with program code for implementation of a process according to an embodiment. Thus, the present embodiment is not limited to any specific combination of hardware and software.

[0027] Process 300 is performed by an imaging system vendor or imaging center that desires to use a normals database associated with a first imaging system and first imaging parameters to evaluate images generated using a second imaging system and second imaging parameters. As mentioned above, the first imaging system and the second imaging system are the same imaging system model in one embodiment.

[0028] In one embodiment, S310-S330 may be performed by one entity (e.g., an entity that owns the first imaging system), while S340-S380 are performed by another entity (e.g., an entity that owns the second imaging system). In another embodiment, S310-S330 are performed by one entity (e.g., an entity that owns the first imaging system), S340-S360 are performed by another entity (e.g., an entity that owns the second imaging system), and S370-S380 are 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).

[0029] First, in S310, a plurality of reference images are determined. The plurality of reference images are associated with a first imaging system (e.g., a particular model of a SPECT imaging system) and first imaging parameters. The plurality of reference images are each acquired using the first imaging system and the first imaging parameters, and are stored in the Normals database described above.

[0030] Next, in S320, a first image of the phantom is generated using the first imaging system and first imaging parameters. The generation of the first image includes acquiring a plurality of projection images and reconstructing the projection images into a three-dimensional image. In one embodiment, two or more images are generated in S320.

[0031] The phantom may include any object visible to the first imaging system. FIG. 4A shows a side view of phantom 400 according to one embodiment, and FIG. 4B shows a top view. Phantom 400 is cylindrical and includes thirteen hollow spheres that may be loaded with photon-emitting material, contrast agent, etc., depending on the imaging modality. Phantom 400 and its containing spheres are constructed from an acrylic material, although embodiments are not so limited. In one embodiment, one or more spheres are loaded with contrast agent and photon-emitting material, while other spheres are loaded with contrast agent but not photon-emitting material. The remaining volume of phantom 400 is filled with water and an appropriate amount of photon-emitting material to achieve a desired background level of activity.

[0032] At S330, first image metrics of the first image are determined. The determined first image metrics include edge resolution, noise, uptake activity, and sphericity. Each of these metrics is determined using known techniques. One method for determining noise and edge resolution is described in Vija, A. Hans, Siemens Healthineers, and Molecular Imaging Business Line. "xSPECT reconstruction method." White Paper Order A91MI-10462 (2017): T1-7600, the contents of which are incorporated herein by reference in all respects.

[0033] In S340, multiple images of the phantom are generated. The phantom may be the same physical object as the phantom in S320, or may be different instances of the same model phantom with identical dimensions and configuration. The phantom imaged in S340 is preferably loaded with the same material at the same concentration as the phantom imaged in S320.

[0034] Each of the multiple images of the phantom is generated using imaging parameters different from the first imaging parameters. The multiple images are generated using a second imaging system that 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 imaging parameters may be different from the reconstruction method specified by the first imaging parameters.

[0035] 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. Then, at S360, one of 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 is compared against each of the plurality of determined image metrics to identify a closest set of the plurality of determined image metrics. At S360, one of the plurality of images generated using a closest set of the determined plurality of image metrics is identified.

[0036] Because one or more image metrics may be determined for each image, identifying the closest set of image metrics includes determining a feature vector for each of the multiple images. The feature vector for an image represents the values ​​of the image metrics determined for the image. Some metrics are weighted differently in the feature vector than other metrics. Thus, S360 includes determining which of the feature vectors of the multiple images is closest in feature space to the feature vector of the first image of the phantom.

[0037] In one embodiment, S360 includes determining whether the image accurately represents the phantom. In this regard, one or more metrics are evaluated against predetermined metric values ​​based on a priori knowledge of the physical properties of the phantom. For example, images associated with values ​​of a shape deformation (or sphericity) metric, an activity metric, and / or a congruence metric that fall outside a predetermined range are excluded from consideration in S360, regardless of whether values ​​of other metrics of the image (e.g., edge resolution and noise) are closest to the corresponding metric values ​​of the first image.

[0038] In S370, an image of the patient is generated using the imaging parameters used to generate the image identified in S360. By the preceding steps, it is assumed that the image metrics of the generated image are similar to the image metrics of the reference image determined in S310. Accordingly, in S380, the patient image is compared to one or more of the plurality of reference images. A clinical task is generated as a result of the comparison.

[0039] 5, according to one embodiment, illustrates a graph 500 of image resolution versus image noise for various imaging parameters of an imaging system. In this example, an image of a phantom was generated in S320 using an imaging system model (i.e., an ND imaging system) and imaging parameters associated with a Normals database of reference images (i.e., Flash 3D (F3D) 10i8s8.0g). Indicators 510 indicate the image edge resolution and noise values ​​determined in S330.

[0040] Assume that it is desired to use the Normals database with a second, different imaging system model. However, for purposes of illustration, indicator 520 indicates the edge resolution and noise of an image of a phantom acquired with the second imaging system model using the imaging parameters associated with the Normals database (i.e., F3D 10i8s8.0g). Due to the difference in the metrics indicated by indicator 510 and indicator 520, it may not be appropriate to compare the image acquired with the second imaging system model using the imaging parameters associated with the Normals database to a reference image in the Normals database.

[0041] Each of curves 530-560 represents the noise (σ / μ) and edge resolution (in mm) of an image of the phantom acquired using a second imaging system model and a different set of imaging parameters (i.e., a different reconstruction voxel size). Each vertical line of each curve represents a metric value for an image produced using the imaging parameters of the curve and a different post-smoothing level. At a constant post-smoothing level, the imaging parameters associated with curves 540, 550, and 560 produce images of the phantom with the same or better resolution as indicated by indicator 510, at low noise levels.

[0042] Even so, S360 includes determining the closest matching set of resolution and noise metrics. In S360, it is determined that point 545 of curve 540 is closest to indicator 510. Thus, the image generated using the imaging parameters associated with curve 540 and the post-smoothing level associated with point 545 is the image determined in S360. Thus, the imaging parameters associated with curve 540 and the post-smoothing level associated with point 545 are used in S370 to generate an image of the patient for comparison with reference images in the Normals database.

[0043] In one embodiment, steps S340-S360 may be iterative. For example, in S340, one or more images of the phantom are generated using different imaging parameters, in S350, image metrics are determined for each image, and in S360, it is determined whether any of the set of metrics is sufficiently close to the first image metrics of the first image. In this case, it may also be determined whether the image metrics of the image corresponding to the closest set of metrics are physically accurate (e.g., with respect to shape deformation, activity, and congruence).

[0044] If no metrics are sufficiently close to the first image metrics of the first image, or if such close metrics are not physically accurate, flow returns to S340 to acquire one or more additional images using different imaging parameters. The different imaging parameters may be determined based on differences between the previously determined image metrics and the first image metrics. Flow proceeds from S360 to S370 once a set of metrics is identified that is sufficiently close and physically accurate to the first image metrics of the first image.

[0045] 6 is a block diagram of a system 600 for converting a reference image based on a target imaging system and a phantom, according to one embodiment. The system 600 includes a database 630 that stores reference images 632. The reference images 632 consist of images acquired using a predetermined model of an imaging system (i.e., system A612) and a particular set of imaging parameters (i.e., parameters A614). The parameters A614 may be parameters used to control the operation of system A612 to acquire data, or may include parameters that an image reconstructor 616 takes into account when reconstructing an image. System A612 and image reconstructor 616 are collectively referred to herein as imaging system 610.

[0046] Imaging system 610 generates image 635 of phantom 620a based on parameter A614. Specifically, system A612 executes an imaging protocol based on any corresponding parameter values ​​of parameter A614, and image reconstructor 616 reconstructs the resulting data based on any reconstruction or data processing parameter values ​​of parameter A614 (e.g., type of reconstruction, number of updates (i.e., iterations), level of post-smoothing). Phantom 620a is implemented as described with respect to phantom 130a and / or phantom 400, although embodiments are not limited in this respect. Metric determiner 640 determines one or more image metrics of image 635, as described above.

[0047] System C (SYS C System C 652 includes any type and model of imaging system. System C 652 acquires images using the same imaging modality (e.g., SPECT) as system A 612, but includes a different type of imaging system. The model of system C 652 and the model of system A 612 may be manufactured by different companies or the same company. In one embodiment, system A 612 and system C 652 are the same imaging system model.

[0048] The image forming system 650 uses parameters C (PARAMETERS C1-3 , parameter C 654 is used to generate image 655 of phantom 620b. Phantom 620a and phantom 620b include different objects, but are the same model of a phantom having the same dimensions and configuration. If phantom 620a was loaded with a radionuclide prior to imaging with system 120, phantom 620b is similarly loaded prior to imaging with system 150. Parameter C 654 may include a default set of parameters, a set of preferred parameters for imaging system 652 with respect to image quality, speed, and / or patient region to be imaged. That is, unlike the embodiment described with respect to FIGS. 1-3 , parameter C 654 is not a parameter determined based on considerations related to imaging system 610 or parameter A 614, but rather is a “target” parameter used with imaging system 650.

[0049] A metric determiner 660 determines one or more image metrics for image 655. A mapping determiner 665 then determines a mapping based on the metrics determined by determiner 640 and determiner 660. In one example, mapping determiner 665 determines a mapping that will result in image 635 exhibiting similar metric values ​​as image 655. Such a mapping specifies the application of different reconstruction algorithms or different reconstruction parameters to data acquired by system A 612.

[0050] The image transformer 675 applies the determined mapping to the reference image 632 to generate a transformed image 682. The transformed image 682 is stored along with the reference image 632 in an updated normals database 680. As shown in the updated normals database 680, the transformed image 682 is associated with system C 652 and parameter C 654.

[0051] 7, according to one embodiment, is a block diagram of a system 700 for generating images for comparison with a reference image transformed by system 600. As shown, imaging system 650 generates an image 720 of patient 710 based on parameter C 654. Image 720 may include an image of any portion of patient 710.

[0052] Image comparer 730 compares image 720 and transformed image 682 using known image comparison techniques and algorithms, including, but not limited to, a visual comparison performed by a human. Image comparer 730 outputs clinical action 740 based on the comparison. Thus, the embodiments shown in Figures 6 and 7 also facilitate using a Normals database containing reference images associated with a first imaging system and first imaging parameters to evaluate images generated using a second imaging system and second imaging parameters.

[0053] 8 is a flow diagram 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. Process 800 may be performed by an imaging system vendor or imaging center that desires to use a normals database associated with a first imaging system and first imaging parameters to evaluate images generated using a second imaging system and second imaging parameters. As mentioned above, the first imaging system and the second imaging system are, in one embodiment, the same imaging system model.

[0054] S810-S830 proceed as described above with respect to S310-S330 of process 300. Next, in S840, a second image of the phantom is generated using imaging parameters that are different from the first imaging parameters used in S820. The second image is generated using a second imaging system that may or may not be the same model as the first imaging system used in S820. For example, the second imaging system is the same model as the first imaging system, but the reconstruction method specified by the different imaging parameters is different from the reconstruction method specified by the first imaging parameters.

[0055] At S850, image metrics are determined for the second image. Then, at S860, a mapping is determined based on the first image metrics and the second image metrics. In one embodiment, S860 includes determining a reprocessing step to apply to the first image such that the reprocessed first image exhibits image metrics sufficiently close to the image metrics of the second image. The reprocessing step comprises a reconstruction algorithm including a particular number of update and / or post-smoothing levels. The determination at S860 includes reprocessing the first image in several different ways and selecting the reprocessing step that produced an image exhibiting image metrics closest to the image metrics of the second image.

[0056] Each of the plurality of reference images is transformed based on the determined mapping in S870. A second imaging system generates an image of the patient in S880 using second imaging parameters. S880 occurs at a different time and location than S870, and the second imaging system in S880 comprises the same model as the second imaging system in S870 but a different instance. It can be assumed that the image metrics of the images generated in S880 are similar to the image metrics of the reference images transformed in S870. The patient image is compared to one or more of the plurality of transformed reference images in S890, resulting in the generation of a clinical task.

[0057] 9 illustrates an imaging system 900, according to one embodiment. System 900 is a SPECT imaging system known in the art, although embodiments are not limited thereto. Each component of system 900 may include additional components that provide functionality beyond that described herein, in addition to other components necessary for its operation.

[0058] The system 900 includes a housing 910 for a gantry 902 to which two or more gamma cameras 904 a, 904 b are mounted, although any number of gamma cameras can be used. The detectors of each gamma camera detect gamma photons (i.e., emission data) emitted by a radioactive tracer injected into the body of a patient 906 lying on a bed 908. The bed 908 is slidable along a motion axis A. At each bed position (i.e., imaging position), a portion of the body of the patient 906 is positioned between the gamma cameras 904 a, 904 b to capture emission data from that body portion from various projection angles.

[0059] The control system 920 may include a general-purpose or special-purpose computing system. The control system 920 includes one or more processing units 922 configured to execute executable program code to cause the system 920 to operate as described herein, and a storage device 930 for storing the program code. The storage device 930 may include one or more fixed disks, solid-state random access memory, and / or removable media (e.g., thumb drives) connected to a corresponding interface (e.g., a USB port).

[0060] The storage device 930 stores program code for a control program 931. One or more processing units 922 execute the control program 931 and, in conjunction with the SPECT system interface 924, control motors, servos, and encoders, rotate the gamma cameras 904a, 904b relative to the gantry 902 and acquire two-dimensional emission data 932 at defined imaging positions during the rotation.

[0061] Control program 931 is further executed to reconstruct image 933 based on specified parameters, determined as described with respect to Figures 1 and 3 to facilitate comparison of image 933 with reference image 934. In another embodiment, reference image 934 comprises a reference image transformed based on specified parameters of system 900, as described above with respect to Figures 6 and 8.

[0062] Terminal 940 includes a display device and an input device coupled to terminal interface 925 of system 920. Terminal 940 receives and displays image 933 and reference image 934 as well as a user interface to facilitate comparison therebetween. In one embodiment, terminal 940 is a separate computing device such as, but not limited to, a desktop computer, a laptop computer, a tablet computer, and a smartphone.

[0063] Those skilled in the art will appreciate that various adaptations and modifications of the above-described embodiments may be made without departing from the scope of the claims, and it is therefore to be understood that the claimed invention may be practiced otherwise than as specifically disclosed herein.

Claims

1. determining a first image metric of a first image of the phantom; the first image is generated by a first imaging system based on first imaging parameters, and the first imaging system and the first imaging parameters are associated with a plurality of reference images; generating a plurality of images of the phantom; each of the plurality of images is generated using a different imaging parameter; determining a second image metric for each of the plurality of generated images; identifying one of the plurality of generated images based on the first image metric and the second image metric of each of the plurality of generated images; generating an image of the object using the imaging parameters used to generate the identified one of the plurality of generated images; comparing the generated image of the object with 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. 4. The method of claim 3, wherein identifying one of the plurality of generated images comprises identifying one of the plurality of generated images associated with the second image metric that is closest to the first image metric.

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

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

7. determining a first image metric of a first image of the phantom; the first image is generated by a first imaging system based on first imaging parameters, and the first imaging system and the first imaging parameters are associated with a plurality of reference images; generating a second image of the phantom using a second imaging system and second imaging parameters; determining a second image metric for the second image; determining a mapping based on the first image metric and the second image metric; transforming 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 imaging parameters; comparing the generated image of the object with one or more of the transformed 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 the mapping comprises determining reconstruction parameters based on the first image metric and the second image metric.

11. The method of claim 7 further comprising storing the transformed reference images together with the reference images.

12. generating a third image of the phantom using a third imaging system and third imaging parameters; determining a third image metric for the third image; determining a second mapping based on the first image metric and the third image metric; transforming each of the plurality of reference images into a plurality of second transformed reference images based on the second mapping; The method of claim 7 , further comprising storing the second transformed plurality of reference images together with the transformed plurality of reference images and the plurality of reference images.

13. A non-transitory computer-readable medium having program code stored thereon, The program code determining a first image metric for a first image of the phantom; the first image is generated by a first imaging system based on first imaging parameters, and the first imaging system and the first imaging parameters are associated with a plurality of reference images; generating a plurality of images of the phantom; each of the plurality of images is generated using a different imaging parameter; determining a second image metric for each of the plurality of generated images; identifying one of the plurality of generated images based on the first image metric and the second image metric for each of the plurality of generated images; generating an image of the object using the imaging parameters used to generate the identified one of the plurality of generated images; a non-transitory computer-readable medium executable by a processing unit to compare the generated image of the object with 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. 16. The medium of claim 15, wherein identifying one of the plurality of generated images includes identifying one of the plurality of generated images associated with the second image metric that is closest to the first image metric.

17. 16. The medium of claim 15, wherein identifying one of the plurality of generated images includes identifying one of the plurality of generated images associated with the 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 object is generated using the second imaging system.

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