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 Art

[0001] A "Normals (reference values)" database is used to assist in the evaluation of medical images. The Normals database includes reference medical images of healthy subjects and / or subjects with a low probability of developing one or more diseases. The Normals database is used in cardiology and neurology, but is not limited to these fields.

[0002] The reference images of healthy / low-probability subjects stored in the Normals database are obtained using a specific imaging system, a specific imaging protocol, and a specific image data processing method. Therefore, to use the Normals database, a patient's image must be obtained using the same imaging system, imaging protocol, and processing method as those used to obtain the reference images of the Normals database. Then, the image is compared with the reference images of the Normals database. If the image is different from the reference images by a certain degree, for example, the image is flagged as requiring clinical follow-up.

[0003] Therefore, the reference images of the Normals database are ideally used only to evaluate images obtained in the same way as the reference images. When an image is obtained using an imaging system, an imaging protocol, and a processing method, if any of them is different from those used to obtain the reference images of the Normals database, a new Normals database must be constructed.

[0004] Building a Normals database is extremely costly. Therefore, an imaging center using a particular Normals database may decide against upgrading to a new imaging system or processing method, as the upgrade could render that specific Normals database unsuitable. Even if an imaging center decides to request the construction of a new Normals database due to changes in its imaging chain, it will take considerable time before the new database becomes available.

[0005] Therefore, it is desirable for the 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 a different imaging system, imaging protocol, and / or processing method. [Brief explanation of the drawing]

[0006] [Figure 1] A block diagram of a system that determines image formation parameters of an imaging system based on a reference imaging system and a phantom, according to one embodiment. [Figure 2] A block diagram of a system that generates an image for comparison with a reference image, according to one embodiment. [Figure 3] A flowchart illustrating a process according to one embodiment in which image formation parameters of an imaging system are determined based on a reference imaging system and a phantom, and an image is generated using those image formation parameters for comparison with a reference image. [Figure 4] Figures 4A and 4B show a phantom according to one embodiment. [Figure 5] A graph of image resolution versus image noise relating to various image formation parameters of an imaging system according to one embodiment. [Figure 6] A block diagram of a target imaging system and a system for transforming a reference image based on a phantom, according to one embodiment. [Figure 7]A block diagram of a system that generates an image for comparison with a converted reference image, according to one embodiment. [Figure 8] A flowchart illustrating a process according to one embodiment for converting a reference image based on a target imaging system and a phantom, and generating an image for comparison with the converted reference image. [Figure 9] A diagram of an imaging system according to one embodiment. Detailed explanation

[0007] The following description is provided to enable persons with ordinary skill in the art to use the disclosed embodiments and describes the best form considered for carrying out the disclosed embodiments, although various modifications will be apparent to persons with ordinary skill in the art.

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

[0009] In one embodiment, a second image-forming parameter to be used by the second imaging system is determined based on a first image of the phantom acquired using the first imaging system and the first image-forming parameter, and a second image of the same phantom acquired using the second imaging system and various different second image-forming parameters. The determined second image-forming parameter yields an image of the phantom having a metric value most similar to the metric value of the first image. The metric value includes, but is not limited to, noise and edge resolution. Next, an image of the patient is acquired by the second imaging system using the determined second image-forming parameter and compared to a reference image.

[0010] In another embodiment, a first image of the phantom is acquired using a first imaging system and first image formation parameters, and a second image of the same phantom is acquired using a second imaging system and second image formation parameters. Metric values ​​for the first and second images are determined. Next, a mapping is 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 a Normals database to generate a set of transformed reference images associated with the second imaging system and second image formation parameters. Then, an image of the patient is acquired by the second imaging system using the second image formation parameters and compared with the transformed reference images.

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

[0012] System 100 includes a database 110 that stores reference images 112. The database 110 includes a Normals database as described above, but the embodiments are not limited thereto. The reference images 112 are from a predetermined model of the imaging system (i.e., System A (SYS)). A )122) and a specific set of image formation parameters (i.e., parameter A(PARAMETERS A The image consists of images acquired using (124). As described above, parameter A124 includes parameters used to control the operation of system A122 to acquire data, or parameters taken into account by the image reconstruction unit 126 when reconstructing an image from the acquired data. System A122 and the image reconstruction unit 126 are collectively referred to here as the image forming system 120.

[0013] The embodiments are not limited to any particular imaging modality. For example, System A122 includes single-photon emission computed tomography (SPECT) systems, positron emission tomography (PET) systems, magnetic resonance (MR) systems, computed tomography (CT) systems, or other systems for medical image generation that are known or may become known. In one embodiment, System A122 includes a model of a particular imaging system (e.g., Siemens Symbia Evo).

[0014] System B (SYS B System B152 consists of an imaging system of a certain type and model. According to one embodiment, system B152 acquires images using the same imaging modality (e.g., SPECT) as system A122, but includes an imaging system of a different model. The model of system B152 and the model of system A122 may be manufactured by different companies or the same company. In one embodiment, system A122 and system B152 are imaging systems of the same model.

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

[0016] Phantom 130a includes any object visible to the imaging modality of system A122. An example of phantom 130a is described with reference to Figures 4A and 4B, but the embodiments are not limited thereto. Phantom 130a exhibits properties that result in image 135, in which a desired image metric can be consistently determined using known techniques.

[0017] The metric determination unit 140 determines one or more image metrics of the image 135. The image metrics include, without limitation, edge resolution, noise, uptake activity (if the imaging modality of system A122 is molecular and the phantom 130a contains a photon-emitting radionuclide), and sphericity (if the phantom 130a contains a detectable spherical object).

[0018] Similarly, though not necessarily simultaneously, the image forming system 150 uses parameters 1-n(PARAMETERS 1-n Based on )154, an image 155 of the phantom 130b is generated. In particular, according to one embodiment, the image forming system 150 generates N images 155, each of which is generated using a different set of parameters 1-n154. The different sets of parameters 1-n154 can vary in several ways. For example, the parameters 1-n154 of a first set specify a first reconstruction algorithm and different post-smoothing levels, while the parameters 1-n154 of a second set specify a second reconstruction algorithm and different post-smoothing levels.

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

[0020] Metric determination unit 160 determines one or more image metrics for each of images 155. Next, metric comparison unit 170 compares the metric determined by metric determination unit 140 with the metric determined by metric determination unit 160. This comparison is intended to determine the one of images 155 whose metric is closest to the metric determined for image 135. Then, parameter determination unit 180 determines which of parameters 1 - n 154 was used to acquire the determined one of images 155. The determined parameter is output as parameter B (PARAMETERS B ) 190.

[0021] According to one embodiment, the image forming system 150 generates one or more images 155 based on each parameter 154, and the metric determination unit 160 determines the metric as described above. The metric comparison unit 170 compares the metric with the metric determined for the image 135 and outputs the result of the comparison to the parameter determination unit 180. Contrary to the previous example, the parameter determination unit 180 generates one or more parameter sets based on the comparison, and the image forming system 150 uses the one or more parameter sets to generate one or more new images 155. The parameter determination unit 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. The above process can continue until the difference between the metric determined for a specific image 155 and the metric determined for the image 135 is within a threshold. The parameter determination unit 180 outputs the parameter used to obtain the specific image 155 as the parameter B190.

[0022] FIG. 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 the system 100. For example, assume that the parameter B190 is determined in advance as described with respect to FIG. 1. As shown in FIG. 2, the image forming system 150 of FIG. 1 generates an image 220 of the patient 210 based on the parameter B190. The image 220 includes an image of any part of the patient 210.

[0023] As a result of the method of determining the parameter B190, it is assumed that the image metric of the image 220 is similar to the image metric of the reference image 112 in the database 110, facilitating their comparison. Accordingly, the image comparison unit 230 compares the image 220 with the reference image 112 using known image comparison techniques and algorithms, including visual comparison that would be performed by a human if not intended to be limited.

[0024] Based on this comparison, the image comparison unit 230 outputs a clinical task 240. For example, if image 220 is not sufficiently similar to a specific reference image 112, the image comparison unit 230 may output a clinical task 240 instructing further clinical trials to be performed on patient 210. Thus, this embodiment facilitates the use of a Normals database containing reference images associated with the first imaging system and first image formation parameters in order to evaluate images generated using the second imaging system and second image formation parameters.

[0025] Figure 3, relating to one embodiment, is a flowchart of process 300, which determines the image formation parameters of an imaging system based on a reference imaging system and a phantom, and generates an image using those image formation parameters for comparison with 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. Each step of process 300 does not need to be performed by a single device or system, nor does it need to be performed adjacent to one another in time, nor does it need to be performed in the order presented.

[0026] Process 300 and all other processes mentioned herein are carried out by executable program code that is read from one or more non-temporary computer-readable media, such as disk-type or solid-state hard drives, DVD-ROMs, flash drives, or magnetic tapes, and stored in a compressed, uncompiled, and / or encrypted format. In one embodiment, hardwired circuitry is used instead of or in combination with program code for executing the process according to one embodiment. Thus, this embodiment is not limited to any particular combination of hardware and software.

[0027] Process 300 is performed by an imaging system vendor or imaging center that wishes to evaluate images 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 described 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 possessing a first imaging system), while S340-S380 may be performed by another entity (e.g., an entity possessing a second imaging system). In another embodiment, S310-S330 may be performed by one entity (e.g., an entity possessing a first imaging system), S340-S360 may be performed by another entity (e.g., an entity possessing a second imaging system), and S370-S380 may be performed by yet another entity (e.g., an entity using a second imaging system or a third imaging system of the same model as the second imaging system to acquire and evaluate patient images using a Normals database).

[0029] First, in S310, multiple reference images are determined. These multiple reference images are associated with a first imaging system (e.g., a specific model of a SPECT imaging system) and a first image formation parameter. Each of the multiple reference images is acquired using the first imaging system and the first image formation parameter and is 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 image formation parameters. This generation of the first image includes acquiring multiple 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 contains any object visible to the first imaging system. Figure 4A is a side view and Figure 4B is a top view of a phantom 400 according to one embodiment. The phantom 400 is cylindrical and contains 13 hollow spheres into which photon-emitting material, contrast agent, etc. are loaded, depending on the imaging modality. The phantom 400 and its contained spheres are constructed from acrylic material, but embodiments are not limited thereto. 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 the phantom 400 is filled with water and an appropriate amount of photon-emitting material to achieve a desired background level of activity.

[0032] In S330, the first image metrics of the first image are determined. The first image metrics determined 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 it may be a different instance of the same model phantom having the same dimensions and configuration. Preferably, the phantom imaged in S340 is loaded with the same material at the same density as the phantom imaged in S320.

[0034] Each of the multiple images of the phantom is generated using image formation parameters different from those of the first image formation parameter. These multiple 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 differ from the reconstruction method specified by the first image formation parameter.

[0035] In S350, an image metric is determined for each of the multiple images. The image metric can be determined in the same way as described above for S330. Next, in S360, one of the multiple images is identified based on the first image metric and the image metrics of each of the multiple images. For example, the first image metric is compared against each of the multiple determined image metrics to identify the closest set of the multiple determined image metrics. In S360, one of the multiple images generated using the closest set of the multiple determined image metrics is identified.

[0036] Since one or more image metrics can be determined for each image, identifying the closest set of image metrics involves determining a feature vector for each of multiple images. The image feature vector represents the values ​​of the image metrics determined for the image. Some metrics are weighted differently from others in the feature vector. Thus, S360 involves determining which of the feature vectors of multiple images is closest to the feature vector of the first image of the phantom in the feature space.

[0037] In one embodiment, S360 includes determining whether an image accurately represents the phantom. In this regard, one or more metrics are evaluated against predetermined metric values ​​based on deductive knowledge of the phantom's physical properties. For example, images associated with shape deformation (or sphericity), activity, and / or congruence metric values ​​that are outside the predetermined range are excluded from consideration in S360, regardless of whether the 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, a patient image is generated using the image formation parameters used to generate the image identified in S360. The preceding steps assume that the image metrics of the generated image are similar to those of the reference image determined in S310. Therefore, in S380, the patient image is compared to one or more of several reference images. Clinical work is then generated as a result of this comparison.

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

[0040] Let us assume that it is desirable to use the Normals database with a second, different imaging system model. However, for illustrative purposes, indicator 520 shows the edge resolution and noise of the phantom image acquired by the second imaging system model using the image formation parameters associated with the Normals database (i.e., F3D 10i8s8.0g). Due to the differences in metrics shown by indicators 510 and 520, it can be said that it is not appropriate to compare the image acquired by the second imaging system model using the image formation parameters associated with the Normals database with the reference image in the Normals database.

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

[0042] Nevertheless, 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. 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 in S360. Thus, the image formation 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 a reference image in the Normals database.

[0043] In one embodiment, steps S340 to S360 may be iterative. For example, in S340, one or more images of the phantom are generated using different image forming 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 metric of the first image. In this case, it is also possible to determine 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 there is no metric that is sufficiently close to the first image metric of the first image, or if such a close metric is not physically accurate, the flow returns to S340 to acquire one or more other images using different image formation parameters. The different image formation parameters may be determined based on the difference between the previously determined image metric and the first image metric. The flow proceeds from S360 to S370 once a set of metrics has been identified that is sufficiently close and physically accurate to the first image metric of the first image.

[0045] Figure 6, relating to one embodiment, is a block diagram of a system 600 that transforms a reference image based on a target imaging system and a phantom. System 600 includes a database 630 that stores a reference image 632. The reference image 632 consists of an image acquired using a predetermined model of the imaging system (i.e., system A612) and a specific set of image formation parameters (i.e., parameter A614). Parameter A614 includes parameters used to control the operation of system A612 to acquire data, or parameters taken into account when the image reconstruction unit 616 reconstructs the image. System A612 and the image reconstruction unit 616 are collectively referred to here as the image formation system 610.

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

[0047] System C(SYS C System C652 includes imaging systems of any type and model. System C652 acquires images using the same imaging modality (e.g., SPECT) as System A612, but includes a different type of imaging system. The models for System C652 and System A612 are manufactured by different companies or the same company. In one embodiment, System A612 and System C652 are the same imaging system model.

[0048] The image forming system 650 uses parameter C(PARAMETERS CBased on 654, an image 655 of phantom 620b is generated. Phantoms 620a and 620b are the same model of phantoms, having the same dimensions and configuration, although they contain different objects. If phantom 620a is loaded with radionuclides before imaging by system 120, phantom 620b is similarly loaded before imaging by system 150. Parameter C654 includes a default set of parameters, a preferred set of parameters for imaging system 652 with respect to image quality, speed, and / or patient area to be imaged. That is, unlike the embodiments described with respect to Figures 1-3, parameter C654 is a “target” parameter used with imaging system 650, rather than a parameter determined based on considerations regarding imaging system 610 or parameter A614.

[0049] The metric determination unit 660 determines one or more image metrics for image 655. Next, the mapping determination unit 665 determines a mapping based on the metrics determined by determination units 640 and 660. In one example, the mapping determination unit 665 determines a mapping that will result in image 635 showing similar metric values ​​to image 655. Such a mapping specifies the application of a different reconstruction algorithm or different reconstruction parameters to the data acquired by system A612.

[0050] The image conversion unit 675 applies the determined mapping to the reference image 632 to generate the converted image 682. The converted image 682 is 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 associated with the system C652 and the parameter C654.

[0051] Figure 7, relating to one embodiment, is a block diagram of a system 700 that generates an image for comparison with a reference image converted by system 600. As shown in the figure, the image forming system 650 generates an image 720 of patient 710 based on parameter C654. Image 720 includes an image of any part of patient 710.

[0052] The image comparison unit 730 compares image 720 with the converted image 682 using known image comparison techniques and algorithms, including, without limitation, visual comparisons performed by humans. Based on the comparison, the image comparison unit 730 outputs a clinical task 740. Thus, the embodiments shown in Figures 6 and 7 also facilitate the use of a Normals database containing reference images associated with the first imaging system and first image formation parameters in order to evaluate images generated using the second imaging system and second image formation parameters.

[0053] Figure 8, relating to one embodiment, is a flowchart of process 800, which transforms a reference image based on a target imaging system and a phantom, and generates an image for comparison with the transformed reference image. Process 800 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 the image generated using a second imaging system and second image formation parameters. As described above, the first imaging system and the second imaging system are the same imaging system model in one embodiment.

[0054] Steps S810 to S830 proceed as described above with respect to steps S310 to S330 of process 300. Next, in step S840, a second image of the phantom is generated using image formation parameters different from the first image formation parameters used in step S820. The second image is generated using a second imaging system that is either the same model as the first imaging system used in step S820 or a different model. 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 is different from the reconstruction method specified by the first image formation parameters.

[0055] In S850, an image metric is determined for the second image. Next, in S860, a mapping is determined based on the first and 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 an image metric that is sufficiently close to the image metric of the second image. The reprocessing step consists of a reconstruction algorithm that includes a specific number of update and / or post-smoothing levels. The decision in S860 includes reprocessing the first image in several different ways and selecting a reprocessing step that produces an image exhibiting an image metric that is closest to the image metric of the second image.

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

[0057] Figure 9, relating to one embodiment, shows an imaging system 900. System 900 is a SPECT imaging system known in the art, but the embodiments are not limited thereto. Each component of system 900 may include additional elements that provide functions other than those described herein, in addition to other elements necessary for its operation.

[0058] System 900 can use several gamma cameras, but includes a housing 910 for a gantry 902 to which two or more gamma cameras 904a, 904b are mounted. The detector of each gamma camera detects gamma photons (i.e., radiation 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 the motion axis A. At each bed position (i.e., imaging position), a portion of the patient 906's body is positioned between the gamma cameras 904a, 904b to capture radiation data from that body portion from various projection angles.

[0059] The control system 920 includes a general-purpose or dedicated computing system. The control system 920 includes one or more processing units 922 configured to execute executable program code to operate the system 920 as described herein, and a storage device 930 for storing the program code. The storage device 930 includes 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 the program code of the control program 931. One or more processing units 922 execute the control program 931 and, in cooperation with the SPECT system interface 924, control motors, servos, and encoders, rotate the gamma cameras 904a and 904b according to the gantry 902, and acquire two-dimensional radiation data 932 at a defined imaging position during rotation.

[0061] The control program 931 is further executed to reconstruct image 933 based on specified parameters. The specified parameters are determined as described with respect to Figures 1 and 3 to facilitate the comparison between image 933 and reference image 934. In other embodiments, reference image 934 includes a reference image transformed based on specified parameters of the system 900, as described above with respect to Figures 6 and 8.

[0062] Terminal 940 comprises a display device and an input device connected to the terminal interface 925 of system 920. Terminal 940 receives and displays images 933 and reference images 934, as well as a user interface to assist in comparing them. In one embodiment, terminal 940 is a separate computing device such as a desktop computer, laptop computer, tablet computer, and smartphone, but is not limited to these.

[0063] Those in the art will understand that various applications and modifications of the above embodiments can be made without departing from the scope of the claims. Therefore, it should be understood that the claimed invention may be implemented in ways other than those specifically disclosed herein.

Claims

1. To determine the first image metric of the first image of the phantom, The first image is generated by a first imaging system based on first image formation parameters, and the first imaging system and the first image formation parameters are associated with a plurality of reference images. To generate multiple images of the aforementioned phantom, Each of these multiple images is generated using different image formation parameters. To determine a second image metric for each of the plurality of images of the phantom, Identifying one of the plurality of images of the phantom based on the first image metric and the second image metric of each of the plurality of images of the phantom, To generate a target image using the image forming parameters used to generate one of the multiple images of the phantom, A method comprising comparing the image of the target with one or more of the multiple reference images.

2. The method according to claim 1, wherein the first image metric includes edge resolution and noise, and the second image metric includes edge resolution and noise.

3. The method according to claim 2, wherein the first image metric includes uptake activity and sphericity, and the second image metric includes uptake activity and sphericity.

4. The method according to claim 3, wherein identifying one of the plurality of images of the phantom includes identifying one of the plurality of images of the phantom that is associated with the second image metric that is closest to the first image metric.

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

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

7. To determine the first image metric of the first image of the phantom, The first image is generated by a first imaging system based on first image formation parameters, and the first imaging system and the first image formation parameters are associated with a plurality of reference images. A second image of the phantom is generated using a second imaging system and second image formation parameters. To determine the second image metric of the second image, Determining the mapping based on the first image metric and the second image metric, Based on the mapping, each of the multiple reference images is converted to a second set of multiple reference images. To generate an image of the target using the second imaging system and the second image formation parameters, A method comprising comparing the generated image of the target with one or more of the converted reference images.

8. The method according to claim 7, wherein the first image metric includes edge resolution and noise, and the second image metric includes edge resolution and noise.

9. The method according to claim 8, wherein the first image metric includes uptake activity and sphericity, and the second image metric includes uptake activity and sphericity.

10. The method according to claim 8, wherein determining the mapping includes determining reconstruction parameters based on the first image metric and the second image metric.

11. The method according to claim 7, further comprising storing the converted plurality of reference images together with the plurality of reference images.

12. A third image of the phantom is generated using a third imaging system and third image formation parameters. To determine the third image metric of the third image, A second mapping is determined based on the first image metric and the third image metric. Based on the second mapping, each of the plurality of reference images is converted to the second plurality of converted reference images. The method according to claim 7, further comprising storing the converted plurality of reference images and the second converted plurality of reference images together with the plurality of reference images.

13. A non-temporary computer-readable medium storing program code, The aforementioned program code is: Determine the first image metric of the first image of the phantom, The first image is generated by a first imaging system based on first image formation parameters, and the first imaging system and the first image formation parameters are associated with a plurality of reference images. Multiple images of the aforementioned phantom are generated, Each of these multiple images is generated using different image formation parameters. A second image metric is determined for each of the plurality of images of the phantom. Based on the first image metric and the second image metric of each of the plurality of images of the phantom, one of the plurality of images of the phantom is identified. A target image is generated using the image forming parameters used to generate one of the multiple images of the phantom, A non-temporary computer-readable medium that can be operated by a processing unit to compare the aforementioned image of the target with one or more of the aforementioned reference images.

14. The medium according to claim 13, wherein the first image metric includes edge resolution and noise, and the second image metric includes edge resolution and noise.

15. The medium according to claim 14, wherein the first image metric includes uptake activity and sphericity, and the second image metric includes uptake activity and sphericity.

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

17. The medium according to claim 14, wherein identifying one of the plurality of images of the phantom includes identifying one of the plurality of images of the phantom that is associated with the second image metric that is closest to the first image metric.

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

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