Method and apparatus for reducing motion related imaging artifacts using consistency values

A method for reducing, in an image, motion related imaging artifacts. The method includes obtaining a single image of a subject using a computed tomography (CT) imaging system, obtaining a plurality of images of the subject using a positron emission tomography (PET) imaging system, generating a plurality of consistency values, and utilizing the plurality of consistency values to register the CT image and the plurality of PET images.

BACKGROUND OF THE INVENTION

The subject matter disclosed herein relates generally to imaging systems, and more particularly to an apparatus and method for motion-correcting medical images.

Multi-modality imaging systems scan using different modalities, for example, computed tomography (CT) and positron emission tomography (PET) imaging. During operation, the image quality may be affected by the motion of the object being imaged. More specifically, image artifacts are produced by movement of the object during image acquisition. Respiratory motion is a common source of involuntary motion in mammals (e.g., people and animals) encountered in medical imaging systems. The respiratory motion may lead to errors during image review, such as when a physician is determining the size of a lesion, determining the location of the lesion, or quantifying the lesion.

Moreover, in multi-modality systems, for example, an integrated PET/CT system, the PET and CT images should be registered with one another. However, since the CT images are typically acquired during a short time period, the attenuation map generated by the CT images represents the attenuation characteristics of the patient during a portion of the breathing cycle where there is minimal breathing motion. In contrast, the PET images are typically acquired over a relatively long time period where a patient is allowed to breathe freely due to the long acquisition time. The mismatch in attenuation properties due to respiration between the two data acquisition modes may result in image artifacts in the attenuation corrected PET images.

One known method for reducing the imaging artifacts is to use a plurality of respiratory gated CT images to generate attenuation correction maps that better match the respiratory characteristics of a respiratory gated PET acquisition. A further method may include requesting the patient to hold their breath during the scan. However, because PET data may be acquired over several minutes, the patient typically has to breathe several times during the PET acquisition, potentially resulting in image artifacts.

BRIEF DESCRIPTION OF THE INVENTION

In one embodiment, a method for reducing, in an image, motion related imaging artifacts is provided. The method includes obtaining a single, motion-reduced image of a subject using a computed tomography (CT) imaging system, obtaining a plurality of images of the subject using a positron emission tomography (PET) imaging system, generating a PET motion correction, generating a plurality of attenuation consistency values, utilize the plurality of consistency values to match the CT image to a PET image, transform the CT image using the PET motion correction to match the other PET images and re-calculate the plurality of PET images.

In another embodiment, a dual-modality imaging system is provided. The dual-modality imaging system includes a computed tomography (CT) imaging system, a positron emission tomography (PET) imaging system, and a processor coupled to the CT and PET imaging systems. The processor is configured to obtain a single image of a subject using the CT imaging system, obtain a plurality of images of the subject using the PET imaging system, generate a PET motion correction, generate a plurality of attenuation consistency values, utilize the plurality of consistency values to register the CT image and the plurality of PET images and then re-calculate the plurality of PET images.

In a further embodiment, a non-transitory computer readable medium is provided. The non-transitory computer readable medium is encoded with a program programmed to instruct a computer to obtain a single image of a subject using a computed tomography (CT) imaging system, obtain a plurality of images of the subject using a positron emission tomography (PET) imaging system, generate a plurality of consistency values, and utilize the plurality of consistency values to register the CT image and the plurality of PET images.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1is a perspective view of an exemplary imaging system10that may be configured to implement the various embodiments described herein. Although various embodiments are described in the context of an exemplary dual modality imaging system that includes a computed tomography (CT) imaging system and a positron emission tomography (PET) imaging system, it should be understood that other imaging systems capable of performing the functions described herein are contemplated as being used.

The multi-modality imaging system10includes a CT imaging system12and a PET imaging system14. The imaging system10allows for multiple scans in different modalities to facilitate an increased diagnostic capability over single modality systems. Optionally, modalities other than CT and PET are employed with the imaging system10. The CT imaging system12includes a gantry16that has an x-ray source18that projects a beam of x-rays through a subject20. After being attenuating by the subject20, the x-rays impinge on a detector22located on the opposite side of the gantry16. The imaging system10also includes a computer30that receives an attenuation projection dataset40using the CT imaging system12and an emission projection data set42from the PET imaging system14. The imaging system10may also include a memory52. The memory52may be located internally within the computer30as illustrated inFIG. 1. Optionally, the memory52may be a storage device that is located remotely from the imaging system10. In operation, the computer30processes the attenuation projection data40and the emission projection data42to reconstruct images of the subject20.

The imaging system10also includes an image reconstruction module50that is configured to implement various methods described herein. The module50may be implemented as a piece of hardware that is installed in the computer30. Optionally, the module50may be implemented as a set of instructions that are installed on the computer30. The set of instructions may be stand alone programs, may be incorporated as subroutines in an operating system installed on the computer30, may be functions in an installed software package on the computer30, and the like. It should be understood that the various embodiments are not limited to the arrangements and instrumentality shown in the drawings.

FIG. 2is a block diagram of an exemplary method100performed by the imaging system10shown inFIG. 1. In the exemplary embodiment, the method100may be implemented using the image reconstruction module50. More specifically, the method100may be provided as a machine-readable medium or media having instructions recorded thereon for directing the processor30and/or the image reconstruction module50to perform an embodiment of the method described herein. The medium or media may be any type of CD-ROM, DVD, floppy disk, hard disk, optical disk, flash RAM drive, or other type of computer-readable medium or a combination thereof.

At102, the attenuation projection dataset40and emission projection dataset42of the subject20(each shown inFIG. 1) are input to the image reconstruction module50. In the exemplary embodiment, the attenuation projection dataset40is obtained using the CT imaging system12(shown inFIG. 1). The attenuation projection dataset40may be obtained by performing a scan of the subject20to produce the attenuation projection dataset40. Optionally, the attenuation projection dataset40may be obtained from data collected during a previous scan of the subject20, wherein the attenuation projection dataset40has been stored in a memory, such as the memory device52(shown inFIG. 1). The attenuation projection dataset40may be stored in any format, such as a list mode dataset, for example. The attenuation projection dataset40may be obtained during real-time scanning of the subject20. For example, the methods described herein may be performed on projection data as the attenuation projection dataset40is received from the CT imaging system12during a real-time examination of the subject20. In various embodiments, the attenuation projection dataset40is a single snapshot or a single image60(as shown inFIG. 1) of the subject20acquired during a short time interval during the CT scan. The single image60may be acquired, for example, during a CT helical scan. Thus, the single CT image60is labeled as (hCT) inFIG. 2to denote that in the illustrated embodiment, the single image60is acquired during a helical scan of the subject20. Acquiring just enough attenuation projection data40to generate the single CT image60facilitates reducing the time required to scan the subject20and therefore also reduces the dosage to the subject20during the scan.

Additionally, at102, the emission dataset42of the subject20(each shown inFIG. 1) is obtained. In the exemplary embodiment, the emission dataset42is obtained using the PET imaging system14(shown inFIG. 1). The emission dataset42may be obtained by performing an emission scan of the subject20to produce the emission dataset42. Optionally, the emission dataset42may be obtained from data collected during a previous scan of the subject20, wherein the emission dataset42has been stored in a memory, such as the memory device52(shown inFIG. 1). The emission dataset42may be stored in any format, such as a list mode dataset, for example. The emission dataset42may be obtained during real-time scanning of the subject20. For example, the methods described herein may be performed on emission data as the emission data42is received from the PET imaging system14during a real-time examination of the subject20. In various embodiments, the emission dataset42generally includes a plurality of PET images62(as shown inFIG. 1) that are, or have been, acquired over a predetermined length of time and segmented into bins (‘4D’) representing the various positional radiotracer distribution information available during the respiratory cycle. Thus, in the illustrated embodiment, at102a single CT image60is acquired at a single point in time and a plurality of 40 PET images62are acquired over a plurality of different points in time.

At104, a PET bin having a maximum consistency with the CT image60is identified. Maximum consistency can be computed, for example, using:

where E(s,φ) are the measured emission data, A(s,φ) are the measured projections of the attenuation image, in m≧0 is the moment being computed and k is the Fourier component. The radial distance from the center of rotation s and the azimuthal angle of rotation φ index the Radon transform space.FIG. 3is a flowchart illustrating the method of identifying the PET bin having the maximum consistency with the CT image60. In various embodiments, the emission dataset42is a plurality of sinograms or sinogram data (not shown). The sinogram dimension is based on the number of detectors installed in the PET imaging system14, and the number of bins containing sinograms varies based upon the user-prescribed respiratory binning protocol. The sinograms may be generated by operating the PET imaging system14in a sinogram mode. Sinogram mode generally refers to an acquisition mode in which annihilation events, optionally having an identical Time-of-Flight (TOF), are stored in sinograms in an (radius from axis, angle) format. The array of responses is known as a sinogram. It should be realized that other methods and/or devices may be used for data storage and that the sinograms described herein represent one such exemplary method of storing data.

Accordingly, at104, the emission dataset42, e.g. the sinograms, is temporally sorted into a plurality of bins (n bins200). The emission dataset42may be sorted into the bins200using respiratory motion, cardiac motion, patient motion, etc. For example,FIG. 3illustrates n bins200numbered202. . .208, i.e. n=4 bins that may be generated in accordance with various embodiments described herein. However, it should be realized that the quantity of bins200illustrated inFIG. 3is exemplary, and that during operation, fewer than four bins or more than four bins may be utilized. As such, each bin202,204,206, and208includes approximately ¼ of the total information in the emission dataset42. For example, assume that the total length of the scan performed by the PET imaging system14to acquire the emission dataset42is three minutes. Moreover, assume that the emission dataset42is sorted into four bins200. Accordingly, each bin200includes approximately 45 seconds of information.

In the exemplary embodiment, after the emission dataset42is sorted into the bins200, a consistency condition is calculated using Eq. 1 for each respective bin200. More specifically, at104, the image reconstruction module50and/or the processor30is configured to determine how well the CT image60matches with the sinogram data stored in each respective bin200by generating a consistency condition for each respective bin200.

A consistency value, as used herein, is a value that represents a sum of the projection data from one view of the projection dataset40and is independent of the view-angle. More specifically, a consistency value is a sum, or row-sum of all the emission data acquired in one view and is view angle independent. The consistency condition may be calculated using, for example, a Helgason-Ludwig consistency condition (HLCC) algorithm (Eq. 1). As shown inFIG. 3, the graph212illustrates the consistency conditions for the bin202, wherein the x-axis represents the view angle, and the y-axis represent the row-sum of the data. Similarly, the graph214illustrates the consistency conditions for the bin204, the graph216illustrates the consistency conditions for the bin206, and the graph218illustrates the consistency conditions for the bin208. In various embodiments, the consistency conditions for each bin200may be calculated but not displayed. These consistency conditions based on the hCT help define the combinations of PET-based transformation necessary to transform the hCT to best match the bins of PET data (shown in a later processing step,FIG. 5).

In various other embodiments, the consistency values for each bin200may be calculated and displayed using for example, the graphs212,214,216, and218. In operation, if the attenuation data40is consistent with the emission data42, a row sum of the attenuation corrected emission data is calculated, and the resulting consistency values, if plotted in a graphical format, should be relatively linear and flat. For example, as shown inFIG. 3, the consistency conditions for the bin202are plotted as a line222in the graph212. Moreover, the consistency values for the bin204are plotted as a line224in the graph214, the consistency values for the bin206are plotted as a line226in the graph216, and the consistency values for the bin208are plotted as a line228in the graph218. Accordingly, in various embodiments, the image reconstruction module50and/or the processor30is configured to automatically determine the consistency conditions for the sinogram data stored in each bin200. Moreover, the image reconstruction module50and/or the processor30are further configured to automatically identify which set of consistency conditions are maximized. Maximized as used herein means the consistency conditions are linear or the line shown in one of the graphs212-218is the flattest which denotes the best consistency match between the emission data in a single bin200and the transmission dataset40. Accordingly, at104a single bin200having the maximum consistency values is identified. This identification may include use of an objective function which calculates the total deviation from flatness for each consistency calculation212,214,216,218. To further explain the methods described herein, in the illustrated embodiment, the bin206is selected as having emission data that is the best consistency match between with the transmission dataset40and may be referred to herein as the reference bin.

Referring again toFIG. 2, at106the emission projection data in each of the bins is attenuation corrected to generate a plurality of attenuation-corrected PET image datasets.FIG. 4illustrates a plurality of attenuation-corrected projection datasets230that may be generated. For example, at106an attenuation-corrected dataset232may be generated using information in the bin202. Similarly, an attenuation-corrected dataset234may be generated using information in the bin204, an attenuation-corrected dataset236may be generated using information in the bin206, and an attenuation-corrected dataset238may be generated using information in the bin208. Accordingly, an attenuation corrected dataset230is generated for each respective bin200.

Additionally, at106the attenuation-corrected datasets230are utilized to reconstruct a plurality of PET images240. For example, the attenuation-corrected dataset232may be utilized to reconstruct a PET image242. Similarly, the attenuation-corrected dataset234may be utilized to reconstruct a PET image244, the attenuation-corrected dataset236may be utilized to reconstruct a PET image246, and the attenuation-corrected dataset238may be utilized to reconstruct a PET image248. Accordingly, in various embodiments, a PET image240is reconstructed from each attenuation corrected dataset230.

Referring again toFIG. 2, at108a global non-rigid registration of the PET images242-248is performed to motion correct the PET images. This process generates transformations on a bin-to-bin basis of the non-rigid registration between all bins not identified as the reference bin and the reference bin. This registration may be performed in image space. The non-rigid registration may be performed manually by the operator or automatically by the processor30. It should be realized, that registration may not be required in certain areas where there is little or no motion.

In the exemplary embodiment, performing a non-rigid registration includes transforming the information within the bins, i.e. the PET images242-248, in three-dimensional (3D) space to align the information within the bins242-248with respect to a reference bin. To perform the non-rigid registration, at least one of the bins242-242is selected as a reference bin and the remaining bins are then registered one-by-one to the reference bin. In various embodiments, the PET image generated from the bin having the best consistency with the CT image60is selected as the reference gate. Accordingly, in the illustrated embodiment shown inFIG. 4, the attenuation-corrected dataset232, which is derived from the information in the bin202and used to reconstruct the PET image242, is used as the reference bin. Therefore, in the illustrated embodiment, the PET images244,246and248are registered with respect to the PET image242.

For example, the PET image246may be slighted tilted with respect to the PET image242. Accordingly, the PET image246is tilted to align the images with the PET image242. The remaining images244and248are also realigned to substantially match the PET image242. In operation, the non-rigid registration procedure may be utilized to perform the motion correction on the information within the PET images242-248. In operation, the non-rigid registration or elastic registration may also include non-rigid transformations. These non-rigid transformations allow local warping of image features and provide registrations that account for local deformations. Non-rigid transformation approaches include, for example, polynomial warping, interpolation of smooth basis functions (thin-plate splines and wavelets), and physical continuum models (viscous fluid models and large deformation diffeomorphisms). Accordingly, in various embodiments, a plurality of transformation matrices is generated to perform the non-rigid registration of the plurality of PET images242-248to generate n image volumes.

Accordingly, at108, a plurality of deformation vectors250, or transformation matrices are generated using the gated 4D image data, images242-248. As discussed above, each 3D PET image242-248includes a plurality of voxels that are points in three-dimensional space that can be referenced using three-dimensional coordinates, usually x, y and z. A deformation vector represents a motion and/or deformation of the object or voxel(s) of interest, such as the motion of the patient20, at a certain point of time, with respect to the reference location. Specifically, for each image voxel and for each image242-248, the vector magnitude [v] (or set of magnitudes) and components [x,y,z], referred to herein as the deformation components, are stored in the memory. In the exemplary embodiment, at108, a reference image from the 4D image data set is selected. In the illustrated embodiment, the PET image242is selected as the reference image. The remaining bins or images244-248are then mapped to the reference bin or image242to generate the motion deformation vectors

In the illustrated example shown inFIG. 4, the PET image242is selected as the reference image or gate. Accordingly, in one such example, based on the registration transformation, a set of transformation vectors250may be obtained that describe the motion of each respective gate relative to the reference gate. For example, the vectors T21describe the motion differences between the reference PET image242and the PET image244. Moreover, the vectors T31describe the motion differences between the reference PET image242and the PET image246, and the vectors TN1describe the motion differences between the reference PET image242and the PET image248. Accordingly, at step108, a plurality of motion vectors are acquired and utilized to register the PET images244-248to a selected reference image242.

Referring again toFIG. 2, at110, a plurality of transformation vectors (T's) are generated using the reference PET bin and the hCT data. In the illustrated example shown inFIG. 5, a set of transformation vectors260may be obtained that describe the motion of each respective gate relative to the reference gate based upon the PET-PET transformation data and knowledge of which PET bin the hCT is most consistent with (fromFIG. 3). For example, the vectors T31describe the motion differences between the reference PET image206and the PET image202and may be utilized to generate respiratory phase-matched CT image272. Moreover, the vectors T32derived from the combination of T31and T21, describe the motion differences between the reference PET image206and the PET image204and may be utilized to generate a respiratory phase-matched CT image274, and the vectors T3N, derived from T31and TN1, describe the motion differences between the reference PET image206and the PET image208. Accordingly, at step110, a plurality of motion vectors252are acquired and at step112, the plurality of vectors252are utilized to transform the CT image60to be better attenuation-matched to the PET data202-208.

More specifically, the plurality of transformation vectors generated at108are utilized to map or register the PET images242-248to generate a plurality of phase-matched CT images. More specifically, because it is calculated via consistency which PET bin the attenuation map (CT image60) was closest to, the transformation matrices or vectors acquired at108may be utilized to generate the phase-matched images.

Referring again toFIG. 2, at112, the transforms acquired at110, from the PET-PET registration, are utilized to generate a plurality of phase-matched CT images. These newly-derived CT images are used to update the per-bin consistency measurements. For example, as shown inFIG. 6, the phase-matched CT image276is used to update the consistency criterion for bin N. Accordingly, a phase-matched CT image272may be formed using the vectors T31, a phase-matched CT image274may be formed using the vectors T32, and a phase-matched CT image276may be formed using the vectors T3N. Accordingly, at112, the plurality of vectors are acquired and utilized to form the phase-matched CT images which are then used to update the consistency criterion for each bin.

Referring again toFIG. 2, at114, the initial consistency conditions acquired at104are revised. For example,FIG. 6illustrates an exemplary method of revising the consistency conditions. In various embodiments, the CT images272-276are again used in conjunction with gated PET bins282-288to generate an updated set of consistency measurements per bin. Moreover, a consistency condition292-298is calculated for each respective bin of updated (“+pm CT”) attenuation and emission data. The consistency conditions may be calculated using, for example, a Helgason-Ludwig consistency condition (HLCC) algorithm (Eq. 1). As shown inFIG. 6, a graph302illustrates the consistency conditions for the bin282, a graph304illustrates the consistency conditions for the bin284, a graph306illustrates the consistency conditions for the bin286, and a graph308illustrates the consistency conditions for the bin288.

At116, an objective function is updated or revised. More specifically, as described above, the consistency conditions are initially calculated in104. More specifically, and as shown inFIG. 3, the PET image having the most consistency with the CT image60, determined using the consistency conditions, is identified and thus the consistency map is the flattest as a function of angle (for instance) indicating the that the one set of PET projection data is substantially most consistent with the CT image60. Accordingly, at116, the attenuation map from the CT may be revised or re-calculated to generate a second set of consistency maps which may be better or worse than the original consistency maps shown inFIG. 3. For example,FIG. 7illustrates an exemplary initial consistency map312that may be generated and compared to the current consistency map212to determine whether the consistency between the PET image282and the CT images270-276has either increased, decreased, or remained the same as compared to the consistency generated using the initial hCT image60. Additionally, a consistency map314may be generated and compared to the consistency map214, a consistency map316may be generated and compared to the consistency map216, and a consistency map318may be generated and compared to the consistency map218.

Referring again toFIG. 2, at118, steps108-116are iteratively repeated until the change in the consistency conditions are within a predetermined threshold. In various embodiments, if the consistency conditions are not within a predetermined threshold, then at120, the emission dataset is again compared to the updated CT images acquired at114and the method proceeds to step108. In various other embodiments, if the consistency conditions are within a predetermined threshold, then at122, a final global non-rigid registration is performed on the PET images. In operation, the non-rigid registration is utilized to perform the motion correction on the information within the bins282-288. In operation, the non-rigid registration or elastic registration includes non-rigid transformations. These non-rigid transformations allow local warping of image features and provide registrations that account for local deformations. At124, the transformed PET images acquired at122are averaged together or the median result per voxel from the set of transformed PET images is used to generate a three-dimensional (3D) rigid-registration (RRA) volume. At126, the 3D RRA volume is utilized

FIG. 8is a schematic block diagram of the imaging system10(shown inFIG. 1). As described above, the imaging system10includes the CT imaging system12and the PET imaging system14. The imaging system10allows for multiple scans in different modalities to facilitate an increased diagnostic capability over single modality systems. The CT imaging system12includes the gantry16that has the x-ray source18that projects the beam of x-rays14toward the imaging detector22on the opposite side of the gantry16. The imaging detector22includes the plurality of detector elements24that are arranged in rows and channels that together sense the projected x-rays that pass through an object, such as the subject20. The imaging system10also includes the computer30that receives the projection data from the imaging detector22and processes the projection data to reconstruct an image of the subject20.

In operation, operator supplied commands and parameters are used by the computer30to provide control signals and information to reposition a motorized table422. More specifically, the motorized table422is utilized to move the subject20into and out of the gantry16. Particularly, the table422moves at least a portion of the subject20through a gantry opening424that extends through the gantry16.

The imaging system10also includes the image reconstruction module50that is configured to implement various methods described herein. As discussed above, the detector22includes the plurality of detector elements24. Each detector element24produces an electrical signal, or output, that represents the intensity of an impinging x-ray beam and hence allows estimation of the attenuation of the beam as it passes through the subject20. During a scan to acquire the x-ray projection data, the gantry16and the components mounted thereon rotate about a center of rotation440.FIG. 8shows only a single row of detector elements24(i.e., a detector row). However, the multislice detector array22includes a plurality of parallel detector rows of detector elements24such that projection data corresponding to a plurality of slices can be acquired simultaneously during a scan.

Rotation of the gantry16and the operation of the x-ray source18are governed by a control mechanism442. The control mechanism442includes an x-ray controller426that provides power and timing signals to the x-ray source18and a gantry motor controller446that controls the rotational speed and position of the gantry16. A data acquisition system (DAS)428in the control mechanism442samples analog data from detector elements24and converts the data to digital signals for subsequent processing. For example, the subsequent processing may include utilizing the module50to implement the various methods described herein. An image reconstructor450receives the sampled and digitized x-ray data from the DAS428and performs high-speed image reconstruction. The reconstructed images are input to the computer30that stores the image in a storage device452. Optionally, the computer30may receive the sampled and digitized x-ray data from the DAS428and perform various methods described herein using the module50. The computer30also receives commands and scanning parameters from an operator via a console460that has a keyboard. An associated visual display unit462allows the operator to observe the reconstructed image and other data from computer.

The operator supplied commands and parameters are used by the computer30to provide control signals and information to the DAS428, the x-ray controller426and the gantry motor controller446. In addition, the computer30operates a table motor controller464that controls the motorized table422to position the subject20in the gantry16. Particularly, the table422moves at least a portion of the subject20through the gantry opening424as shown inFIG. 1.

Referring again toFIG. 8, in one embodiment, the computer30includes a device470, for example, a floppy disk drive, CD-ROM drive, DVD drive, magnetic optical disk (MOD) device, or any other digital device including a network connecting device such as an Ethernet device for reading instructions and/or data from a non-transitory computer-readable medium472, such as a floppy disk, a CD-ROM, a DVD or an other digital source such as a network or the Internet, as well as yet to be developed digital means. In another embodiment, the computer30executes instructions stored in firmware (not shown). The computer30is programmed to perform functions described herein, and as used herein, the term computer is not limited to just those integrated circuits referred to in the art as computers, but broadly refers to computers, processors, microcontrollers, microcomputers, programmable logic controllers, application specific integrated circuits, and other programmable circuits, and these terms are used interchangeably herein.

In the exemplary embodiment, the x-ray source18and the imaging detector22are rotated with the gantry16within the imaging plane and around the subject20to be imaged such that the angle at which an x-ray beam474intersects the subject16constantly changes. A group of x-ray attenuation measurements, i.e., projection data, from the imaging detector22at one gantry angle is referred to as a “view”. A “scan” of the subject20comprises a set of views made at different gantry angles, or view angles, during one revolution of the x-ray source18and the imaging detector20. In a CT scan, the projection data is processed to reconstruct an image that corresponds to a two dimensional slice taken through the subject20.

The imaging system10also controls the operation of the PET imaging system14. Accordingly, in various embodiments, the imaging system10may also include a coincidence processor502that is coupled between a PET detector500and a PET scanner controller504. The PET scanner controller504is utilized to control the operation of the PET system14. In various embodiments, the PET scanner controller504may be coupled to the computer30. In operation, the signals output from the detector500are input to the coincidence processor502. In various embodiments, the coincidence processor502assembles information regarding each valid coincidence event into an event data packet that indicates when the event took place and the position of a detector that detected the event. The valid events may then be conveyed to the controller504and/or the computer30to reconstruct an image.

Exemplary embodiments of a multi-modality imaging system are described above in detail. The multi-modality imaging system components illustrated are not limited to the specific embodiments described herein, but rather, components of each multi-modality imaging system may be utilized independently and separately from other components described herein. For example, the multi-modality imaging system components described above may also be used in combination with other imaging systems.