CN109816740B - Coincidence processing method for scintillation pulse event - Google Patents

Coincidence processing method for scintillation pulse event Download PDF

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CN109816740B
CN109816740B CN201711150749.4A CN201711150749A CN109816740B CN 109816740 B CN109816740 B CN 109816740B CN 201711150749 A CN201711150749 A CN 201711150749A CN 109816740 B CN109816740 B CN 109816740B
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肖鹏
徐浩
王卫东
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Abstract

The invention provides a coincidence processing method of a scintillation pulse event, which comprises the following steps of S1: acquiring experimental data of a PET system, wherein the experimental data comprises time information of arrival of a pair of gamma photons with same energy and opposite directions generated after positron annihilation to two corresponding detectors and energy information of deposition of the gamma photons in the detectors, and the time information is Ti1And Ti2Energy information is Ei1And Ei2(ii) a Step S2: setting an energy window and a time window and acquiring a coincidence event, wherein the energy window is set as E, the time window is set as T, and gamma photons meeting the following conditions are marked as a pair of true coincidence events: | Ti1－Ti2∣≤T，Ei1E is not more than E, and Ei2E is less than or equal to E; step S3: weighting the true coincidence events, wherein two formulas are adopted for reinforcement processing; step S4: carrying out image reconstruction to obtain an image result; step S5: and analyzing the image result. The invention distributes the weight of the coincidence event by utilizing the energy information, improves the quality of the reconstructed image and has strong adaptability.

Description

Coincidence processing method for scintillation pulse event
Technical Field
The invention relates to a scintillation pulse processing method in the field of positron emission computed tomography, in particular to a coincidence processing method of scintillation pulse events.
Background
PET (Positron Emission Tomography, hereinafter PET) is a non-invasive nuclear medicine imaging method. After the radioactive tracer is injected into the human body or the animal body, the tracer can present different concentration distributions at different positions in the human body or the animal body according to the metabolism levels at different positions in the human body or the animal body. The radioactive substance generates beta + decay in the human body or the animal body to generate positron, the positron annihilates with electrons in the human body or the animal body to generate a pair of gamma photons with the same energy and opposite directions, so that the time information, the energy information and the position information of the gamma photons reaching the detection device are measured by the external detection device, and the distribution level of the radioactive tracer in the human body or the animal body can be calculated, and image reconstruction and display are carried out. PET belongs to functional metabolism molecule imaging equipment and plays a very important role in oncology and research, diagnosis and treatment of diseases related to a plurality of nervous systems.
After the positron is annihilated in a human body, a pair of gamma photon pairs with opposite movement directions can be generated, the energy values of the gamma photon pairs are 511KeV, the gamma photons can be converted into scintillation pulse signals through a photoelectric conversion device and a subsequent processing circuit, and the time information, the energy information and the position information of the gamma photons reaching a detector module can be measured through the scintillation pulse signals, so that the coincidence of scintillation pulse events is an important step in the PET image reconstruction process, and the high-quality coincidence method greatly improves the quality of reconstructed images under the same condition. At present, a coincidence method of scintillation pulse events mainly includes establishing a time window and an energy window to determine whether two scintillation pulse events are true coincidence events, that is, by establishing the time window and the energy window, when an absolute value of a difference between times of two scintillation pulses reaching a detector is within the time window and energy values of the two scintillation pulses are both within the energy window, the two scintillation pulses can be regarded as a pair of true coincidence events in an event coincidence project. The corresponding response line of the pair of coincidence events in the PET system can be obtained through the position information of the scintillation pulse. In the existing image reconstruction algorithm, due to the large number of crystal strips, when a pair of scintillation pulses is measured between the crystal strip a and the crystal strip B, the response lines corresponding to the two crystal strips are back-projected as a coincidence event (expressed as +1 when counting) in the back-projection step of iterative reconstruction. However, in this reconstruction method, for each pair of scintillation pulses, each gamma photon may be over-scattered before striking the detector or suffer from measurement errors of the detector, the energy value of the measured gamma photon is not necessarily 511KeV, but these coincidence event pairs are still regarded as true coincidence events in the back projection and are back projected, for example, two pairs of gamma photons are measured, the time information measured by the first pair of gamma photon detectors is Ta1 and Ta2, and the energy information is Ea1 and Ea 2; the time information measured by the second pair of gamma photon detectors is Tb1 and Tb2 respectively, the energy information is Eb1 and Eb2 respectively, the time window of the PET system is set to be T, and the energy window is set to be E. However, Ea1 in actual measurement is not necessarily equal to Eb1, nor is Ea2 necessarily equal to Eb 2. Therefore, it is not reasonable to consider the weight of all coincident events to be equal.
In the prior art, there is also a weighting processing method based on time information, i.e. a TOF (time of flight) reconstruction method, which is a reconstruction method that weights by using the time information of the arrival of the gamma photon at the detector. However, this reconstruction method has high requirements on the performance of the detector, and therefore is mostly used only for reconstructing simulation data.
In summary, in the coincidence method of the scintillation pulse events using the time window and the energy window in the prior art, since the weights of each pair of coincidence events in the back projection are considered to be equal, the method has certain defects in theory; although the method of weighting by using time information is feasible, the time measurement performance of the detector is too high, and the method cannot be widely applied in practice. Therefore, in order to improve the quality of image reconstruction in PET systems, it is necessary to find a more sophisticated coincidence processing method of scintillation pulse events that is more convenient for practical applications.
Disclosure of Invention
The invention aims to provide a coincidence processing method of a scintillation pulse event, so as to solve the problems of error and inconvenience for practical application in coincidence processing of the scintillation pulse event in the prior art.
In order to solve the above technical problem, a technical solution of the present invention is to provide a coincidence processing method for a scintillation pulse event, the coincidence processing method for the scintillation pulse event comprising the following steps:
step S1: acquiring experimental data of a PET system, wherein the experimental data comprises time information of arrival of a pair of gamma photons with same energy and opposite directions generated after positron annihilation at two corresponding detectors and energy information of deposition of the gamma photons in the detectors, and the time information is Ti1And Ti2The energy information is respectively Ei1And Ei2；
Step S2: setting an energy window and a time window of the PET system and acquiring coincidence events, wherein the energy window is set as E, the time window is set as T, and the gamma photons meeting the following conditions are marked as a pair of true coincidence events:
∣Ti1－Ti2∣≤T，Ei1e is not more than E, and Ei2≤E；
Step S3: weighting the true coincidence events, wherein the strengthening processing adopts the following two formulas:
the first method comprises the following steps:
Figure BDA0001473246360000031
and the second method comprises the following steps:
Figure BDA0001473246360000032
wherein p isiRepresenting a projection value on the ith response line, i is a natural number, and sigma and a are variable parameters related to the PET system, the imaging prosthesis and the activity respectively;
step S4: carrying out image reconstruction to obtain an image result;
step S5: and analyzing the image result.
In the step S1, the experimental data further includes position information of gamma photons, which is obtained by numbering scintillation crystal strips of the PET system.
In step S2, the energy window T is set to a range of 150keV to 650KeV, and the energy resolution is set to 20%.
According to an embodiment of the present invention, in the step S3, a value of σ ranges from 0.1 to 1000.
According to one embodiment of the invention, σ has a value in the range of 1.5 to 100.
According to an embodiment of the present invention, in the step S3, a has a value ranging from 0.01 to 100.
According to one embodiment of the invention, a ranges from 1 to 50.
According to an embodiment of the present invention, σ is 2 and a is 0.5.
In step S4, the image reconstruction method is a maximum likelihood-expectation maximization algorithm, and the formula adopted is as follows:
Figure BDA0001473246360000041
wherein the content of the first and second substances,
Figure BDA0001473246360000042
represents the value of j pixel after n +1 times of iteration, j is a natural number, aijIndicating the proportion of the j pixel on the ith response line.
In step S5, the contrast parameter used for analyzing the image result is a recovery contrast coefficient, which includes a hot-zone recovery contrast coefficient and a cold-zone recovery contrast coefficient, where the hot-zone recovery contrast coefficient is:
Figure BDA0001473246360000043
wherein, CH,jRepresenting the average count of hot areas in the reconstructed image, CB,jRepresenting the average count of the background in the reconstructed image, aHRepresenting the activity value of the hot zone in simulation, aBThe activity value of the background at the time of simulation is represented.
The cold zone recovery contrast ratio is:
Figure BDA0001473246360000044
wherein, CC,jRepresenting the average count of cold regions in the reconstructed image, CB,jRepresenting the average count of the background in the reconstructed image.
According to the coincidence processing method of the scintillation pulse event, in the step S3, a unique weighting processing mode is adopted, each pair of response lines are weighted according to the energy value of gamma photons, different weights are given to each pair of response lines, the energy information obtained in the data acquisition process can be maximized, and the weights of the coincidence events are distributed by using the energy information, so that the effect of improving the quality of a reconstructed image can be achieved in the subsequent image reconstruction, and particularly, the hot zone recovery degree contrast coefficient and the cold zone recovery degree contrast coefficient in the step S5 can be obviously improved; the invention can select different weighting modes or different parameters according to the imaging characteristics of different living bodies so as to improve the image reconstruction quality in a more targeted manner, and has strong adaptability. Meanwhile, the coincidence processing method of the scintillation pulse event provided by the invention is simple to implement and is suitable for various PET systems with different structures.
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FIG. 1 is a schematic illustration of the steps of a coincidence processing method of scintillation pulse events according to a preferred embodiment of the present invention.
Detailed Description
The present invention will be further described with reference to the following specific examples. It should be understood that the following examples are illustrative only and are not intended to limit the scope of the present invention.
The coincidence processing method of the scintillation pulse event provided by the invention is used for data processing in a PET system, and mainly comprises the following steps:
step S1: acquiring experimental data, wherein the experimental data are time information of gamma photons arriving at a detector, energy information of gamma photons deposited in the detector and position information of gamma photons, which are generated after positron annihilation, and the time information of gamma photons collected by two corresponding detectors is Ti1And Ti2Energy information is Ei1And Ei2；
Step S2: setting an energy window and a time window, acquiring a coincidence event, respectively setting the energy window and the time window in the PET system as E and T, and when the time information and the energy information of gamma photons respectively meet the following conditions:
∣Ti1－Ti2∣≤T，
Ei1≤E
Ei2≤E；
judging the gamma photons collected by the corresponding detectors to be a pair of true coincidence events;
step S3: and carrying out weighting processing on the true coincidence events, wherein the weighting processing mode comprises two modes:
the first method comprises the following steps:
Figure BDA0001473246360000051
and the second method comprises the following steps:
Figure BDA0001473246360000061
wherein p isiRepresenting a projection value on the ith response line, wherein i is a natural number, and sigma and a are variable parameters related to a PET system, an imaging prosthesis and activity, and different sigma and a can be selected as required in the process of image reconstruction;
step S4: the image reconstruction is carried out, the specific method of the image reconstruction is maximum likelihood-Expectation Maximization (ML-EM), and the maximum likelihood-Expectation Maximization algorithm adopts the formula as follows:
Figure BDA0001473246360000062
wherein the content of the first and second substances,
Figure BDA0001473246360000063
represents the value of j pixel after n +1 times of iteration, j is a natural number, aijRepresents the proportion of the j pixel on the ith response line, piRepresenting the projection value on the ith response line;
fifthly, analyzing image results, wherein the contrast parameter adopted in the image result analysis is a recovery degree contrast coefficient which comprises a hot area recovery degree contrast coefficient and a cold area recovery degree contrast coefficient, and the hot area recovery degree contrast coefficient is as follows:
Figure BDA0001473246360000064
wherein, CH,jTo representAverage count of hotspots in reconstructed images, CB,jRepresenting the average count of the background in the reconstructed image, aHRepresenting the activity value of the hot zone in simulation, aBThe activity value of the background at the time of simulation is represented.
The cold zone recovery contrast ratio is:
Figure BDA0001473246360000065
wherein, CC,jRepresenting the average count of cold regions in the reconstructed image, CB,jRepresenting the average count of the background in the reconstructed image.
More specifically, in step S1, the energy window range set when the experimental data is acquired is 150KeV-650KeV, the energy resolution is set to 20%, the prosthesis used for acquiring the experimental data is an iq (image quality) prosthesis under the american Electrical Manufacturers Association (NEMA) NU 2-2007 standard, and the parameters set by the PET system are as follows: the detector radius 371mm, the number of detector panels was 88, each detector panel included 1 x 4 arrays of detector modules, each detector module included 1 x 6 arrays of scintillation crystal strips, the total number of scintillation crystal strips was 12672, the size of a single scintillation crystal strip was 4.25mm x 4.25mm, and the number of pixels was 500 x 96; the position information for acquiring the experimental data is obtained by numbering the scintillation crystal strips.
In the above step S2, the acquisition of coincidence events and the data comparison may be performed by a processor suitable for data operation.
In the above step S3, the value range of σ is preferably 0.1 to 1000. The value range of σ is more preferably 1.5 to 100. The value of a preferably ranges from 0.01 to 100. The value range of a is more preferably 1 to 50. In the preferred embodiment of the present invention, σ is 2 and a is 0.5.
In step S3, a unique weighting processing mode is adopted, each pair of response lines is weighted according to the energy value of gamma photons, different weights are given to each pair of response lines, the energy information obtained in the data acquisition process can be utilized to the maximum, and the weights of the coincidence events are distributed by using the energy information, so that the effect of improving the quality of reconstructed images can be achieved in the subsequent image reconstruction, and particularly, the hot-zone recovery contrast coefficient and the cold-zone recovery contrast coefficient in step S5 can be obviously improved; the invention can select different weighting modes or different parameters according to the imaging characteristics of different living bodies so as to improve the image reconstruction quality in a more targeted manner, and has strong adaptability. Meanwhile, the coincidence processing method of the scintillation pulse event provided by the invention is simple to implement and is suitable for various PET systems with different structures.
The above embodiments are merely preferred embodiments of the present invention, which are not intended to limit the scope of the present invention, and various changes may be made in the above embodiments of the present invention. All simple and equivalent changes and modifications made according to the claims and the content of the specification of the present application fall within the scope of the claims of the present patent application. The invention has not been described in detail in order to avoid obscuring the invention.

Claims (9)

1. A coincidence processing method of a scintillation pulse event, characterized in that the coincidence processing method comprises the steps of:
step S1: acquiring experimental data of a PET system, wherein the experimental data comprises time information of arrival of a pair of gamma photons with same energy and opposite directions generated after positron annihilation at two corresponding detectors and energy information of deposition of the gamma photons in the detectors, and the time information is Ti1And Ti2The energy information is respectively Ei1And Ei2；
Step S2: setting an energy window and a time window of the PET system and acquiring coincidence events, wherein the energy window is set as E, the time window is set as T, and the gamma photons meeting the following conditions are marked as a pair of true coincidence events:
∣Ti1－Ti2∣≤T，Ei1e is not more than E, and Ei2≤E；
Step S3: weighting the true coincidence events, wherein the strengthening processing adopts the following two formulas:
the first method comprises the following steps:
Figure FDA0002526736140000011
and the second method comprises the following steps:
Figure FDA0002526736140000012
wherein p isiRepresenting a projection value on the ith response line, i is a natural number, and sigma and a are variable parameters related to the PET system, the imaging prosthesis and the activity respectively;
step S4: carrying out image reconstruction to obtain an image result, wherein the image reconstruction method is a maximum likelihood-expectation maximization algorithm and adopts a formula as follows:
Figure FDA0002526736140000013
wherein the content of the first and second substances,
Figure FDA0002526736140000014
represents the value of j pixel after n +1 times of iteration, j is a natural number, aijThe specific weight of the jth pixel on the ith response line is represented;
step S5: and analyzing the image result.
2. The coincidence processing method of scintillation pulse events of claim 1, wherein in said step S1, said experimental data further comprises position information of gamma photons, said position information being obtained by numbering scintillation crystal strips of said PET system.
3. The coincidence processing method of scintillation pulse events of claim 1, wherein in step S2, the energy window T is set to a range of 150keV to 650keV and the energy resolution is set to 20%.
4. The coincidence processing method of the scintillation pulse event according to claim 1, characterized in that, in step S3, σ has a value in the range of 0.1-1000.
5. The coincidence processing method of scintillation pulse events of claim 4, wherein σ ranges from 1.5 to 100.
6. The coincidence processing method of the scintillation pulse event according to claim 1, wherein in step S3, a has a value in the range of 0.01 to 100.
7. The coincidence processing method of scintillation pulse events of claim 6, wherein a ranges from 1 to 50.
8. The coincidence processing method of scintillation pulse events of claim 1, wherein σ has a value of 2 and a has a value of 0.5.
9. The coincidence processing method of the scintillation pulse event of claim 1, wherein in step S5, the image result is analyzed by using a recovery contrast coefficient as a contrast parameter, the recovery contrast coefficient includes a hotspot recovery contrast coefficient and a cold-area recovery contrast coefficient, wherein the hotspot recovery contrast coefficient is:
Figure FDA0002526736140000021
wherein, CH,jRepresenting the average count of hot areas in the reconstructed image, CB,jRepresenting the average count of the background in the reconstructed image, aHRepresenting the activity value of the hot zone in simulation, aBAn activity value representing the background at the time of simulation;
the cold zone recovery contrast ratio is:
Figure FDA0002526736140000031
wherein, CC,jRepresenting the average count of cold regions in the reconstructed image, CB,jRepresenting the average count of the background in the reconstructed image.
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