Positron range correction

Abstract

A system and method includes acquiring emission data from a subject when a radiotracer is present in the subject, determining a first parameter of a first Gaussian distribution representing a positron range distribution of the radiotracer, determining a second parameter of a second Gaussian distribution associated with an imaging characteristic of an imaging system, and determining a second parameter of a second Gaussian distribution associated with the imaging characteristic of the imaging system. Generating a system matrix based on the first parameter and the second parameter, reconstructing a three-dimensional image based on the transmission data and the system matrix, and displaying the three-dimensional image.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to and the benefit of U.S. Provisional Patent Application No. 63 / 365,507, filed May 31, 2022, the contents of which are incorporated herein by reference for all purposes. Background Art

[0003] Tomographic reconstruction techniques enable three-dimensional imaging of volumes for various imaging modalities. For example, in some nuclear imaging modalities, a radioactive tracer is administered to a patient. The radioactive decay of the tracer generates a positron, which travels through the patient's body and eventually encounters an electron. The encounter results in an annihilation event, which annihilates the positron and produces two 511keV gamma photons.

[0004] Photons emitted from the patient are detected by a detector system. A data set representing the detected photons (i.e., emission data) is provided to a tomographic reconstruction unit that calculates a three-dimensional image object based on the emission data and the characteristics of the detector system. The characteristics include, but are not limited to, system geometry and detector sensitivity.

[0005] In a positron emission tomography (PET) system, a ring of detectors surrounding the patient detects photons emitted from the patient, based on which "coincidences" are identified, and tomographic reconstruction is used to reconstruct a PET image based on the identified coincidences. A coincidence is identified when two detectors set on opposite sides of the body detect the arrival of two photons within a specific coincidence time window.

[0006] Since an annihilation event generates two photons traveling in substantially opposite directions, two photons arriving within a particular coincidence time window are assumed to have been generated by the same annihilation event. Furthermore, the positions of the two detectors that detected the "coincident" photons define a line of response (LOR) along which an annihilation event may have occurred. The LORs defined by all detected coincidence events are sorted into parallel projections and then used to reconstruct a three-dimensional image representing the three-dimensional distribution of the positron emission tracer in the patient.

[0007] PET systems are used to produce images that are used to determine the biochemistry or physiology of a specific organ, tumor, or other metabolically active site. The biochemistry or physiology to be determined determines the identity of the radiotracer used. Fluorine-18 ( 18 F) is a glucose analog and can be used to track glucose use, oxygen-15 labeled water ( 15 O-water) is a water analog and can be used to track blood flow and organ perfusion, and gallium-68 ( 68Ga) is a metal and can be used to label larger molecules (such as proteins), and rubidium-82 ( 82 R) reacts in a similar manner to potassium and is often used for cardiac imaging.

[0008] Each radioactive tracer used in nuclear imaging emits a positron when it decays, resulting in an annihilation event. The positron travels a short distance from the nucleus that produced it before encountering an electron and producing a 511keV photon. The length of this short distance (i.e., the positron range) depends on the kinetic energy of the positron. In particular, the greater the kinetic energy of the positron, the more likely it will travel a greater distance before annihilation. Therefore, the uncertainty between the position of the nucleus of the tracer molecule and the position of the annihilation event caused by the positron emitted by the nucleus increases with the positron energy.

[0009] If the positron range distribution is ignored during reconstruction, the resolution of the reconstructed image will decrease as the positron energy increases. For example, rubidium-82 exhibits a 3.15 MeV positron energy, while fluorine-18 exhibits a 635 keW positron energy. Therefore, if the positron range is not taken into account, and all other conditions remain constant, an image reconstructed based on emission data acquired using rubidium-82 will exhibit greater blur than an image reconstructed based on emission data acquired using fluorine-18.

[0010] Some reconstruction techniques assume a specific positron range distribution corresponding to a specific tracer (e.g., fluorine-18). While these techniques can produce adequate images using a specific tracer, the use of tracers with higher positron energies will result in increased image blurring. Other reconstruction techniques attempt to take into account the exact positron range distribution of the tracer used, requiring the execution of complex and time-consuming mathematical functions.

[0011] It is intended to effectively account for the different positron range distributions of different radiotracers during image reconstruction. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 is a block diagram of a system for reconstructing an image from emission data based on different positron range distributions of different radioactive tracers according to some embodiments;

[0013] Figure 2 is a flow chart of a process for reconstructing an image from emission data based on different positron range distributions for different radiotracers according to some embodiments;

[0014] Figure 3 illustrates the generation of a point spread function (PSF) model for an imaging system according to some embodiments;

[0015] Figure 4illustrates determination of a Gaussian approximation of a positron range distribution according to some embodiments;

[0016] Figure 5 illustrates the difference between a positron range distribution and a Gaussian approximation of a positron range distribution according to some embodiments;

[0017] Figure 6 illustrates a combination of a PSF model for an imaging system according to some embodiments and a Gaussian approximation of a positron range distribution to generate a combined PSF model;

[0018] Figure 7 is a tabular view of parameter values ​​for a polynomial describing a PSF model for a plurality of radiotracers according to some embodiments;

[0019] Figure 8 illustrates a PSF model associated with an imaging system and the left and right sides of a combined PSF model according to some embodiments; and

[0020] Fig. 9 is an illustration of components of an imaging system according to some embodiments. DETAILED DESCRIPTION

[0021] The following description is provided to enable any person skilled in the art to make and use the embodiments and sets forth the best mode contemplated for carrying out the embodiments. However, various modifications will still be apparent to those skilled in the art.

[0022] Some embodiments modify the parameters of the PSF model used in the reconstruction to correct for the positron range distribution of the associated radiotracer. For example, a Gaussian distribution is determined that approximates the positron range distribution of the tracer. The Gaussian distribution is convolved with an existing Gaussian PSF model representing a system matrix of the imaging system to obtain a combined Gaussian PSF model. The combined Gaussian PSF model is used to replace the existing Gaussian PSF model to reconstruct emission data generated using the radiotracer. By providing a simple Gaussian PSF model that incorporates the positron range distribution in the tissue into the image reconstruction, the resulting image is deblurred (i.e., sharper and less noisy) compared to an image reconstructed based solely on the existing PSF model of the imaging system.

[0023] Figure 1 The system 100 according to some embodiments is illustrated. Each component of the system 100 and each other component described herein may be implemented using any combination of hardware and / or software. Some components may share the hardware and / or software of one or more other components.

[0024] The system 100 includes an imaging system 110. The imaging system 110 is not limited to any particular imaging modality. For example, the imaging system 110 may include a PET system, a PET / computed tomography (CT) system, or any other system for generating tomographic images of a known or to-be-known subject 115.

[0025] In the case of a conventional PET-based system, a radiotracer is administered to a subject 115, and the imaging system 110 detects gamma photons emitted from the subject 115, as described above. The detected gamma photons are represented in any suitable format known in the art within the emission data 120. The emission data 120 may include a set of projection images (i.e., a two-dimensional image associated with each projection angle, showing the spatial distribution of photons detected at each angle), list mode data, sinograms, and the like.

[0026] The image reconstruction component 125 computes the image object 130 based on the emission data 120 and the system matrix 135. The system matrix 135 describes the data acquisition properties of the imaging system 110. For example, the system matrix 135 may model how the detector element response varies based on the detector element position within the imaging system 110.

[0027] The image object 130 may be an N-dimensional image object (in medical imaging applications, typically N=3), and may be displayed to a user via a display 140 using known volume rendering techniques. As described herein, an "image object" is defined in object space and is a reconstruction of a data set acquired in data space. The object space is the space in which the result of the image reconstruction is defined, and corresponds to the subject 115 imaged using the imaging system 110.

[0028] The image reconstruction component 125 may implement any known or to-be-known tomographic reconstruction algorithm. Emission data acquired using PET imaging may include a small number of radiation counts and an unavoidable noise contribution. Some tomographic reconstruction algorithms are particularly suitable for reconstructing objects from such data sets.

[0029] Conventional image reconstruction involves a process called backprojection. In simple backprojection, individual data samples are backprojected by setting all image pixels along a line of response pointing to the sample to the same value. In other words, the backprojected views are formed by smearing each view back across the image in the direction in which it was originally acquired. The backprojected image is determined as the sum of all backprojected views. Areas where backprojected lines from different angles intersect represent regions containing higher concentrations of tracer.

[0030] The quality of the reconstructed image depends on an accurate model of the relationship between the image and projection spaces (i.e., the system matrix). The linear relationship between the projection and image spaces is expressed as:

[0031] p j =Σ i C ij x i

[0032] Where p is the true coincident mean of the projection data for a given line of response (LOR) j, C is the system matrix that models the relationship between the image and projection space, and x is the image value at voxel i. The elements of the system matrix C are estimated before reconstruction and can be directly decomposed into crystal efficiency (diagonal matrix), geometric efficiency (diagonal matrix), and attenuation (diagonal matrix provided by the CT scan).

[0033] The system PSF model 145 accounts for additional blurring caused by the data acquisition characteristics of the imaging system 110. The basic parameters of such a PSF model 145 can be derived from data collected by the imaging system 110 using point sources, for example, as described in VY Panin et al., "Full 3-D PET Reconstruction Using a System Matrix Derived from Point Source Measurements," Institute of Electrical and Electronics Engineers (IEEE) Transactions on Medical Imaging, Vol. 25, pp. 907-921, 2006, which is incorporated herein by reference in its entirety. The collected data can be normalized, including with respect to geometric and crystal components. This data collection is described in more detail below.

[0034] In existing systems such as that described by Panin, the system matrix 135 uses a Gaussian system PSF model 145 to represent the crystal efficiency and geometric efficiency of the imaging system 110. The Gaussian system PSF model 145 can be parameterized, and those parameters can be incorporated into the system matrix 135 and used by the image reconstruction component 125 to account for the crystal efficiency and geometric efficiency of the imaging system 110.

[0035] According to an embodiment of the present example, the system matrix 135 also models the positron range distribution of the radiotracer used to acquire the emission data 120. The positron range PSF model 155 represents the distribution of the positron range of the tracer used to acquire the emission data 120. The actual positron range distribution is typically non-Gaussian, so the positron range PSF model 155 is a Gaussian approximation of the actual distribution in some embodiments to facilitate combination with the Gaussian system PSF model 135. A Gaussian combined PSF model 150 is generated based on the Gaussian system PSF model 145 and the positron range PSF model 155. The Gaussian combined PSF model 150 can be parameterized, and those parameters can be incorporated into the system matrix 135 and used by the image reconstruction component 125 in the same manner as the prior system, but now taking into account both the crystal efficiency and geometric efficiency of the imaging system 110 and the positron range distribution of the radiotracer used to acquire the emission data 120.

[0036] The operator 142 may apply any operation(s) to combine the positron range PSF model 155 and the system PSF model 145 into a combined PSF model 150. The combined PSF model 150 may include a model of a Gaussian distribution and may be generated by convolving the positron range PSF model 155 and the system PSF model 145. In some embodiments, each of the positron range PSF model 155 and the system PSF model 145 is represented by parameters of a polynomial, and the resulting combined PSF model 150 is similarly represented by parameters of a polynomial that is incorporated into the system matrix 135 and used by the reconstruction component 125.

[0037] Figure 2 is a flow chart of a process 200 for reconstructing an image from emission data based on different positron range distributions for different radiotracers, according to some embodiments. In some embodiments, various hardware elements (e.g., one or more processing units such as one or more processors, one or more processor cores, and one or more processor threads) execute program code to perform process 200. The steps of process 200 need not be performed by a single device or system, nor need they be performed adjacent to each other in time or necessarily in the order shown.

[0038] Process 200 and all other processes mentioned herein may be embodied in executable program code that is read from one or more of a non-transitory computer-readable medium, such as a disk-based or solid-state hard drive, DVD-ROM, flash drive, and tape, and then stored in a compressed, uncompiled, and / or encrypted format. In some embodiments, hardwired circuitry may be used in place of or in conjunction with program code for implementing processes according to some embodiments. Thus, embodiments are not limited to any specific combination of hardware and software.

[0039] Initially, at S210, emission data associated with the radiotracer and acquired by the imaging system is obtained. The emission data may include a set of emission data for each projection angle. The emission data acquired by the PET imaging system represents the photon counts detected over the field of view and provides information about the distribution of the radiotracer throughout the object. The emission data may be formatted in any manner that is or becomes known (e.g., sinusoidal graph, list mode, projection image).

[0040] As mentioned above, an imaging system may be associated with a system PSF model as is known in the art. Figure 3 3 is a block diagram illustrating determination of a system PSF model 330 for an imaging system 310. As shown, the imaging system 310 is based on a point source 315 (e.g., a 0.5 mm diameter and 100 μCi activity) 68 The emission data 320 is collected using a Ge point source. The point source 315 can be positioned within the system 310 using a positioning robot that provides a minimum step size of 0.01 mm along each of the three orthogonal axes. The system symmetry can be exploited by collecting emission data 320 as the point source 315 is moved within a volume corresponding to a symmetry unit (e.g., a block in the transverse plane). The collected emission data 320 can be normalized, including with respect to geometric and crystal components, and used to determine a system PSF model 330, such as described in Panin.

[0041] The system PSF model 330 can be divided into a radial component and an axial component. The radial component can be assumed to be an asymmetric function with a maximum value of the response in the projection space at the coordinate ρ0 and is composed of two half-Gaussian functions (left and right) with different standard deviations. The PSF function for a given radial image space r coordinate and azimuth angle θ of the LOR can be expressed as:

[0042] in

[0043]

[0044] The projection data can be parameterized by the projection radial index ρ and the azimuthal index θ. left (r) and σ right (r) is derived from point source measurements as described above. The parameter σ j (r,θ) can be estimated by simulation of the Josephson projector from the sinusoidal diagram of a point source. At θ = 0, this parameter has a minimum value. At θ = 45°, where the Josephson method interpolation is most prominent, this parameter will have a maximum value.

[0045] At S220, a positron range PSF model representing the positron range distribution of the radioactive tracer is determined. Figure 4 A representation 410 of positrons produced by a decaying nucleus that travel until annihilation is included. Each positron of representation 410 travels a specific distance before annihilation, and the statistical distribution of these distances can be modeled as known in the art. Curve 420 represents one such statistical distribution for a given tracer, which is typically non-Gaussian.

[0046] At S220, the Gaussian approximation component 430 generates a Gaussian approximation 440 of the distribution represented by the curve 420. The Gaussian approximation 440 is considered to be a positron range PSF model. Figure 5 Curve 420 and approximation 440 are superimposed to illustrate the difference between them, for example in the case of rubidium-82. Other possible tracers include sodium-22, as well as those mentioned above.

[0047] Next, at S230, the PSF model determined at S220 is combined with the system PSF model to generate a combined PSF model. The combined PSF model may include a Gaussian distribution model and may be generated by convolving the system PSF model and the positron range PSF model. Figure 6 Illustrated is a Gaussian positron range PSF model 610 and a Gaussian system PSF model 620 combined into a combined PSF model 640 by a PSF model combination component 630 in accordance with some embodiments.

[0048] Each of the positron range PSF model 610 and the system PSF model 620 may be represented by polynomial coefficients and / or other parameters. Figure 7 is a representation of table 700 showing polynomial coefficients for a Gaussian approximation of the positron range distribution for each of the four radiotracers. Table 700 also includes other parameters describing the Gaussian approximation (e.g., full width at half maximum, axial sigma). According to some embodiments, the Gaussian approximation of the positron range distribution and the system PSF model are represented by two sets of coefficients / parameters, where each set corresponds to the left or right side of the approximation. Therefore, the combined PSF model can also be represented by two sets of coefficients / parameters, where each set corresponds to the left or right side of the combined PSF model.

[0049] Figure 8The full width at half maximum values ​​of the system PSF model and the combined PSF model generated at S230 as a function of the radial position r according to some embodiments are illustrated. The system PSF model is represented as left σ810 and right σ820, each of which is regularized by a second-order polynomial with an additional constraint of reflection symmetry about r = 0. Similarly, the combined PSF model is represented as left σ830 and right σ840, each of which is regularized by a second-order polynomial with the same additional constraint.

[0050] At S240, a system matrix is ​​determined based on the combined PSF model. In some embodiments, coefficients of polynomial regularization are used to determine the system matrix in the same manner as described in the article cited above by Panin. In contrast to Panin, the coefficients of polynomial regularization combine the system PSF model as described in Panin and a PSF model that approximates the positron range distribution of the radiotracer.

[0051] At S250, a three-dimensional image is reconstructed based on the emission data and the system matrix. With the above generation of the system matrix, the reconstruction at S250 takes into account both the crystal efficiency and the geometric efficiency of the imaging system and the positron range distribution of the radioactive tracer used to collect the emission data. Therefore, due to the uncertainty of the positron range, the three-dimensional image will show less blur than the previous image.

[0052] At S260, the three-dimensional image is displayed. The image may be displayed via a display and using known volume rendering techniques. Such volume rendering techniques may include rendering of a perspective view of the three-dimensional image, rendering of a two-dimensional slice of the three-dimensional image, or any other known or to-be-known technique.

[0053] Fig. 9 The PET / CT imaging system 900 used to perform one or more of the processes described herein is illustrated. Embodiments are not limited to the system 900, multimodal imaging system, or imaging system. For example, the system used to perform the process 200 on the emission data can be separate from the imaging system that acquires the emission data. In addition, the acquisition and execution of the process 200 need not be contiguous in time.

[0054] The system 900 includes a gantry 910 defining an aperture 912. As is known in the art, the gantry 910 houses a PET imaging assembly for acquiring PET image data and a CT imaging assembly for acquiring CT image data. The CT imaging assembly may include one or more x-ray tubes and one or more corresponding x-ray detectors, as is known in the art. The PET imaging assembly may include a ring of any number or type of detectors in any configuration, as is known in the art. The pulses generated by such detectors may be processed by analog and digital components as described herein to discern valid pulses and determine the triggering time of valid pulses.

[0055] The couch 915 and base 916 are operable to move a patient lying on the couch 915 into and out of the aperture 912 before, during, and after imaging. In some embodiments, the couch 915 is configured to translate on the base 916, and in other embodiments, the base 916 can move with the couch 915 or alternatively move from the couch 915.

[0056] The patient entry and exit aperture 912 may allow the patient to be scanned using the CT imaging element and the PET imaging element of the gantry 910. According to some embodiments, during such a scan, the couch 915 and base 916 may provide continuous couch motion and / or step-and-shoot motion.

[0057] The control system 920 may include any general or special purpose computing system. Thus, the control system 920 includes one or more processing units 922, and a storage device 930 for storing program code, the one or more processing units 922 being configured to execute processor executable program code to cause the system 920 to acquire image data and generate images therefrom. The storage device 930 may include one or more fixed disks, solid state random access memory, and / or removable media (e.g., a thumb drive) installed in a corresponding interface (e.g., a universal serial bus port).

[0058] The storage device 930 stores program code for a control program 931. The one or more processing units 922 may execute the control program 931 to control hardware components (not shown) in conjunction with the PET system interface 923 and the bed interface 925 to inject radioactive tracers into a patient, move the patient into the bore 912 past the PET detector of the gantry 910, and detect coincidences occurring in the patient based on pulses generated by the PET detector. The detected events may be stored in the storage device 930 as PET data, which may include raw (i.e., list mode) data and / or sinograms.

[0059] The PSF model combination component may be executed to combine the system PSF model 933 and the positron range PSF model 934 of the tracer as described above. The control program 931 may use the resulting combined PSF model to reconstruct a PET image 935 based on PET data acquired using any suitable reconstruction algorithm that is or becomes known.

[0060] The one or more processing units 922 may execute a control program 931 to control the CT imaging elements of the system 900 using the CT system interface 924 and the bed interface 925 to acquire CT data. Any suitable reconstruction algorithm may be used to generate a CT image based on the CT data. According to some embodiments, a PET image 935 may be generated based at least in part on the CT data (e.g., using a linear attenuation coefficient map determined from the CT data).

[0061] The PET image 935 may be transmitted to a terminal 940 via the terminal interface 926. The terminal 940 may include a display device and an input device coupled to the system 920. The terminal 940 may display the received PET image 935. The terminal 940 may receive user input for controlling the display of data, the operation of the system 900, and / or the processes described herein. In some embodiments, the terminal 940 is a separate computing device such as, but not limited to, a desktop computer, a laptop computer, a tablet computer, and a smart phone.

[0062] Each component of system 900 may include other elements required for its operation, as well as additional elements for providing functions other than those described herein. Each functional component described herein may be implemented in computer hardware, program code, and / or one or more computing systems that execute such program codes as known in the art. Such computing systems may include one or more processing units that execute processor executable program codes stored in a memory system.

[0063] Those skilled in the art will appreciate that various adaptations and modifications of the above-described embodiments may be configured without departing from the claims.It is therefore to be understood that the claims may be practiced in other ways than as specifically described herein.

Claims

1. A method comprising: obtaining emission data associated with the radioactive tracer and acquired by the imaging system; determining first parameters of a first Gaussian distribution representing a positron range distribution of the radiotracer; determining a second parameter of a second Gaussian distribution associated with an imaging characteristic of the imaging system; generating a system matrix based on the first parameter and the second parameter; and A three-dimensional image is reconstructed based on the emission data and the system matrix.

2. The method according to claim 1, further comprising: determining a positron range distribution of the radioactive tracer; as well as The first Gaussian distribution is determined based on the positron range distribution.

3. The method according to claim 2, wherein: The positron range distribution of the radiotracer is not Gaussian.

4. The method according to claim 3, wherein: Generating the system matrix includes convolving a first PSF model based on the first parameters with a second PSF model based on the second parameters.

5. The method according to claim 1, wherein: Generating the system matrix includes convolving a first PSF model based on the first parameters with a second PSF model based on the second parameters.

6. The method according to claim 1, wherein: The positron range distribution of the radiotracer is not Gaussian.

7. A system comprising: an imaging system for acquiring emission data from a subject while a radioactive tracer is present in the subject; A processing unit that is used to: determining first parameters of a first Gaussian distribution representing a positron range distribution of the radiotracer; determining a second parameter of a second Gaussian distribution associated with an imaging characteristic of the imaging system; generating a system matrix based on the first parameter and the second parameter; and reconstructing a three-dimensional image based on the emission data and the system matrix; as well as A display is used to display the three-dimensional image.

8. The system according to claim 7, wherein the processing unit is configured to: determining a positron range distribution of the radiotracer; and The first Gaussian distribution is determined based on the positron range distribution.

9. The system according to claim 8, wherein: The positron range distribution of the radiotracer is not Gaussian.

10. The system according to claim 9, wherein: The generating of the system matrix includes convolving a first PSF model based on the first parameters with a second PSF model based on the second parameters.

11. The system according to claim 7, wherein: The generating of the system matrix includes convolving a first PSF model based on the first parameters with a second PSF model based on the second parameters.

12. The system according to claim 7, wherein: The positron range distribution of the radiotracer is not Gaussian.

13. A non-transitory computer readable medium storing program code executable by a processing unit to: acquiring emission data from the subject while a radioactive tracer is present in the subject; determining first parameters of a first Gaussian distribution representing a positron range distribution of the radiotracer; determining a second parameter of a second Gaussian distribution associated with an imaging characteristic of the imaging system; generating a system matrix based on the first parameter and the second parameter; reconstructing a three-dimensional image based on the emission data and the system matrix; as well as The three-dimensional image is displayed.

14. The medium of claim 13, wherein the program code is executable by a processing unit to: determining a positron range distribution of the radiotracer; and The first Gaussian distribution is determined based on the positron range distribution.

15. The medium according to claim 14, wherein The positron range distribution of the radiotracer is not Gaussian.

16. The medium according to claim 15, wherein The generating of the system matrix includes convolving a first PSF model based on the first parameters with a second PSF model based on the second parameters.

17. The medium according to claim 13, wherein: The generating of the system matrix includes convolving a first PSF model based on the first parameters with a second PSF model based on the second parameters.

18. The medium according to claim 13, wherein: The positron range distribution of the radiotracer is not Gaussian.

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