阅读说明：本技术 双材事例分离并能连续运行的伽马事例处理机及方法 (Gamma case processor and method for separating two material cases and capable of continuous operation ) 是由 邓贞宙 封子纪 于 2020-11-24 设计创作，主要内容包括：本发明涉及一种双材事例分离并能连续运行的伽马事例处理机,其结构包括：光电探测模块、伽马事例分离模块、探测器电子学模块、数据处理及成像模块。光电探测模块用于探测外部伽马射线并转化为模拟电信号,包括锗酸铋(Bi_4Ge_3O_(12))晶体阵列和硅酸镥(Lu_2SiO_5)晶体阵列。其方法包括探测外部伽马射线并转化为模拟电信号,分离不同材料晶体产生的模拟电脉冲信号,从模拟的电脉冲信号抽取符合事件的信息,对信号进行处理并成像。其中用字典学习的方法分离不同材料晶体产生的模拟电脉冲信号。如此,本发明提供的伽马事例处理机具有优良的性能,有效地提升系统信噪比、时间分辨率、成像空间分辨率和灵敏度。(The invention relates to a gamma case processor capable of separating two material cases and continuously operating, which structurally comprises: the device comprises a photoelectric detection module, a gamma case separation module, a detector electronics module and a data processing and imaging module. The photoelectric detection module is used for detecting external gamma rays and converting the gamma rays into analog electric signals and comprises bismuth germanate (Bi) 4 Ge 3 O 12 ) Crystal array and lutetium silicate (Lu) 2 SiO 5 ) An array of crystals. The method comprises the steps of detecting external gamma rays, converting the gamma rays into analog electric signals, separating analog electric pulse signals generated by different material crystals, extracting information conforming to events from the analog electric pulse signals, processing the signals and imaging. Wherein, the simulated electric pulse signals generated by different material crystals are separated by a dictionary learning method. Thus, the present invention providesThe gamma case processor has excellent performance, and effectively improves the signal-to-noise ratio, the time resolution, the imaging spatial resolution and the sensitivity of the system.)

1. The gamma case processor is characterized by comprising a photoelectric detection module, a gamma case separation module, a detector electronics module and a data processing and imaging module, wherein,

the photoelectric detection module comprises a crystal array module and a photoelectric converter module and is used for detecting external gamma rays and converting the external gamma rays into analog electric signals;

the gamma case separation module comprises a D1 dictionary module and a D2 dictionary module and is used for separating analog electric pulse signals generated by different material crystals;

the detector electronics module comprises a pulse processing module and a coincidence processing module and is used for extracting coincidence event information from different analog electric pulse signals;

and the data processing and imaging module comprises a sampling module, an FPGA data processing module and an image reconstruction module and is used for processing and imaging the signals.

2. The gamma case processor with split binary cases and continuous operation of claim 1, wherein gamma rays are incident on the crystal array module and are ionized and excited, atomic decay produces fluorescence photons, and the number of visible photons produced is related to the energy of the ray photons.

3. The gamma case processor with split dual material cases and continuous operation of claim 1 wherein said crystal array module comprises bismuth germanate (Bi)₄Ge₃O₁₂) Crystal array and lutetium silicate (Lu)₂SiO₅) An array of crystals.

4. The gamma case processor with two-material case separation and continuous operation as claimed in claim 1 wherein said D1 dictionary module is used to acquire signal data generated by and train a bismuth germanate crystal array and said D2 dictionary module is used to acquire signal data generated by and train a lutetium silicate crystal array.

5. The gamma case processor with split two material cases and continuous operation of claim 1, wherein the FPGA data processing module processes the data sampled by the sampling module and sends the data to the image reconstruction module, and the image reconstruction module restores the received data to the image.

6. A gamma case processing method for separating two material cases and continuously operating is characterized by comprising the following steps:

s1: detecting external gamma rays and converting the gamma rays into analog electric signals;

s2: separating pulse signals generated by different material crystals from the scintillation pulse signals by utilizing a dictionary learning method;

s3: extracting information conforming to the event from the scintillation pulse signal;

s4: the signals are processed and imaged.

7. The method for gamma case processing with separated two material cases and capable of continuous operation as claimed in claim 6, wherein in step S1, the photo detection module is used to detect external gamma rays and convert the gamma rays into analog electrical signals, wherein the crystal array for detecting gamma rays comprises bismuth germanate crystal array and lutetium silicate crystal array.

8. The method for processing gamma cases with separated two material cases and capable of continuous operation as claimed in claim 6, wherein in step S2, the D1 dictionary module and the D2 dictionary module are used to separate the analog electrical pulse signals generated by different material crystals, and the dictionary learning is performed by the following steps:

t11: when training sample data Y is known, initializing a dictionary X1 into a random matrix with each element in the matrix being close to zero;

t12: the LARS algorithm is utilized to solve the beta 1,the LARS algorithm is expected to find a regression coefficientMake regression prediction

T13: updating the dictionary X2 by the solved beta, wherein Y is X beta, Y is training sample data, X is the dictionary, and beta is the solution of the algorithm;

t14: solving beta 2 again by using the test data Y, the updated dictionary X2 and the LARS algorithm;

t15: calculating an error between test data Y and X2 × β 2;

t16: updating X3 by using the test data and the solved beta 2, and solving beta 3 by using an LARS algorithm;

t17: repeating step T16 until there is no error between the test sample and XM × β M, wherein:

XM＝(x1，x2，...，xn)^T∈R^N*M。

9. the method for gamma case processing with dual case separation and continuous operation according to claim 6, wherein in step S3, the detector electronics module extracts information of coincidence events, including time, energy and location information, from the different analog electrical pulse signals.

10. The gamma case processing method with separated two material cases and capable of continuously running as claimed in claim 6, wherein in step S4, the FPGA data processing module processes the sampled data and sends the processed data to the image reconstruction module, and the image reconstruction module restores the received data to the image again.

Technical Field

The invention relates to the fields of high-energy physics and particle physics application, nuclear medicine equipment and biomedical photonics, in particular to a gamma case processing machine and method capable of separating two material cases and continuously operating.

Background

Positron Emission Tomography (PET) is a non-invasive in vivo imaging method, can non-invasively, quantitatively and dynamically evaluate the metabolic level, biochemical reaction, functional activity and perfusion of various organs in a human body, and is a clinical functional imaging device with the highest sensitivity. Radionuclides with short half-lives are synthesized with compounds required for human metabolism, such as glucose, choline, and acetic acid, and then injected into a human body, and these proton-rich radionuclides spontaneously convert protons into neutrons and emit positrons and neutrals. Positron annihilations produce a pair of gamma photons. These high-penetrating gamma photons are coincidence detected and the distribution of the locations where annihilation events occur is analytically or statistically reconstructed to reconstruct an image of the interior of the patient. The positron emission tomography imaging instrument plays an important role in assisting in diagnosing tumor and cancer, cardiovascular and cerebrovascular diseases, nervous system diseases and the like.

Bismuth germanate (Bi)₄Ge₃O₁₂BGO) crystal is a colorless and transparent artificially synthesized crystal of cubic system, which is a multifunctional optical crystal material with various physical effects such as electro-optic, magneto-optic and scintillation, and can be used as laser medium. In 1973, weber (m.weber) and Mongolian (R.Monchamp) have first discovered the scintillation effect of BGO crystals, which emit green fluorescence with a peak wavelength of 480nm under the action of high-energy rays or particles, and the scintillation property of the crystals can be used to detect the high-energy particles and the high-energy rays. The density of the BGO crystal is 7.13g/cm3, the effective atomic number is 75.2, the BGO crystal has extremely high detection efficiency on gamma rays, and is very suitable for application needing high gamma ray detection efficiency. But the light-emitting efficiency is low, the energy resolution is poor, the time distribution is poor, and the refractive index is high, so that the photon collection is not facilitated.

LSO (Lu) was discovered since 1990 by Melcher and Schweitzer₂SiO₅LSO crystal is a scintillation crystal with potential application value, and a great deal of research on growth and scintillation characteristics has been carried out by a great number of experts and scholars at home and abroad, and particularly, the LSO crystal has gained wide attention in the pharmaceutical industry and high-energy physics. It has a high density (7.4g/c m)^-3) Equivalent to BGO; effective atomic number Z_eff66; has high light output characteristic, about 30000 photon Me^V-1(4-5 times BGO); short decay timeLess than 40 ns; the time resolution is up to 450 ps; the luminescent wavelength is 420nm, the method is suitable for rapid detection of high-energy gamma rays, LSO has weak photon blocking capability and good time distribution rate, and the method is mainly applied to the fields of high-energy physics, nuclear medicine imaging, oil well drilling, nuclear physics, safety inspection and the like.

Accordingly, there is a need for an improved PET detector that overcomes the deficiencies of the prior art.

Disclosure of Invention

The invention aims to provide a gamma case processor and a gamma case processing method which can separate two material cases and can continuously run. The gamma case processor and the method have better performance, and effectively improve the signal-to-noise ratio, the time resolution, the imaging spatial resolution and the sensitivity of a system.

The purpose of the invention is realized by the following technical scheme: a gamma case processor with separated two material cases and continuous operation comprises a photoelectric detection module, a gamma case separation module, a detector electronics module and a data processing and imaging module, wherein,

the photoelectric detection module comprises a crystal array module and a photoelectric converter module and is used for detecting external gamma rays and converting the external gamma rays into analog electric signals;

the gamma case separation module comprises a D1 dictionary module and a D2 dictionary module and is used for separating analog electric pulse signals generated by different material crystals;

the detector electronics module comprises a pulse processing module and a coincidence processing module and is used for extracting coincidence event information from different analog electric pulse signals;

and the data processing and imaging module comprises a sampling module, an FPGA data processing module and an image reconstruction module and is used for processing and imaging the signals.

As a further improvement of the invention, the gamma ray is incident to the crystal array module, ionization and excitation are carried out, atomic de-excitation generates fluorescence photons, and the quantity of generated visible photons is related to the energy of ray photons.

As a further improvement of the invention, the crystal arrayThe module comprises bismuth germanate (Bi)₄Ge₃O₁₂) Crystal array and lutetium silicate (Lu)₂SiO₅) An array of crystals.

As a further improvement of the invention, the D1 dictionary module is used for acquiring signal data generated by the bismuth germanate crystal array and training the signal data generated by the bismuth germanate crystal, and the D2 dictionary module is used for acquiring signal data generated by the lutetium silicate crystal array and training the signal data generated by the lutetium silicate crystal.

As a further improvement of the invention, the FPGA data processing module processes the data sampled by the sampling module and sends the data to the image reconstruction module, and the image reconstruction module restores the received data into an image again.

A gamma case processing method for separating two material cases and capable of continuously operating comprises the following steps:

s1: detecting external gamma rays and converting the gamma rays into analog electric signals (scintillation pulses);

s2: separating pulse signals generated by different material crystals from the scintillation pulse signals by utilizing a dictionary learning method;

s3: extracting information conforming to the event from the scintillation pulse signal;

s4: the signals are processed and imaged.

As a further improvement of the present invention, in step S1, the photo-detection module is used for detecting external gamma rays and converting the gamma rays into analog electrical signals, wherein the crystal array for detecting gamma rays includes a bismuth germanate crystal array and a lutetium silicate crystal array.

As a further improvement of the present invention, in the step S2, the D1 dictionary module and the D2 dictionary module are used to separate the analog electrical pulse signals generated by different material crystals, and the specific steps of dictionary learning are as follows:

t11: when training sample data Y is known, initializing a dictionary X1 into a random matrix with each element in the matrix being close to zero;

t12: beta 1 is solved by using an LARS algorithm, and the LARS algorithm is expected to find a regression coefficientMake regression prediction

T13: updating the dictionary X2 by the solved beta, wherein Y is X beta, Y is training sample data, X is the dictionary, and beta is the solution of the algorithm;

t14: solving beta 2 again by using the test data Y, the updated dictionary X2 and the LARS algorithm;

t15: calculating an error between test data Y and X2 × β 2;

t16: updating X3 by using the test data and the solved beta 2, and solving beta 3 by using an LARS algorithm;

t17: repeating step T16 until there is no error between the test sample and XM × β M, wherein:

XM＝(x1，x2，...，xn)^T∈R^N×M。

as a further improvement of the present invention, in step S3, the detector electronics module extracts information of coincidence events, including time, energy and position information, from the different analog electrical pulse signals.

As a further improvement of the present invention, in step S4, the FPGA data processing module processes the sampled data and sends the processed data to the image reconstruction module, and the image reconstruction module restores the received data to an image again.

Compared with the prior art, the gamma case processing machine and the gamma case processing method have better performance, and can effectively improve the signal-to-noise ratio, the time resolution, the imaging spatial resolution and the sensitivity of a system.

Drawings

FIG. 1 is a flow chart of a gamma case processor with two material case separation and continuous operation according to the present invention.

FIG. 2 is a schematic diagram of the signal transmission of a gamma case handler module capable of separating two material cases and operating continuously according to an embodiment of the present invention.

Fig. 3 is a schematic diagram of a crystal arrangement according to the present invention.

Detailed Description

Various exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be noted that: the relative arrangement of the components and steps, the numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present invention unless specifically stated otherwise.

The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the invention, its application, or uses.

Techniques and apparatus known to those of ordinary skill in the relevant art may not be discussed in detail, but are intended to be considered a part of the specification where appropriate.

In all examples shown and discussed herein, any particular value should be construed as merely illustrative, and not limiting. Thus, other examples of the exemplary embodiments may have different values.

It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, further discussion thereof is not required in subsequent figures.

Referring to fig. 1 to 3, a gamma case processor capable of separating two material cases and continuously operating includes a photoelectric detection module, a gamma case separation module, a detector electronics module, and a data processing and imaging module.

The photoelectric detection module comprises a crystal array module and a photoelectric converter module;

a gamma case separation module, which comprises a D1 dictionary module and a D2 dictionary module;

the detector electronics module comprises a pulse processing module and a coincidence processing module;

and the data processing and imaging module comprises a sampling module, an FPGA data processing module and an image reconstruction module.

Furthermore, the photoelectric detection module is used for detecting external gamma rays and converting the external gamma rays into analog electric signals and is used for detecting the external gamma raysThe block includes a crystal array module and a photoelectric converter module. The crystal array module is used for receiving external gamma rays and converting the external gamma rays into a certain number of visible light photons and soft ultraviolet light photons. The crystal array module comprises bismuth germanate (Bi)₄Ge₃O₁₂BGO crystal array and lutetium silicate (Lu)₂SiO₅LSO) crystal array, gamma rays are incident to the crystal array module to be ionized and excited, atomic de-excitation generates fluorescence photons, and the quantity of generated visible light photons is related to the energy of the ray photons. The photoelectric converter module is used for converting optical signals into analog electric signals, the optical signals generated by the crystal array module are transmitted to the photoelectric converter module, the photoelectric converter module comprises an amplifier module and is used for amplifying the converted electronic signals, subsequent processing is facilitated, the optical signals are converted into voltage or current pulse signals, the voltage or current pulse signals are multiplied through electronics, and the signal size capable of being processed by the back-end circuit is output.

Further, the gamma case separation module is used for separating analog electric pulse signals generated by different material crystals, and comprises a D1 dictionary module and a D2 dictionary module, wherein the D1 dictionary module and the D2 dictionary module are trained from a large number of real PET data blocks. The D1 dictionary module is used for acquiring signal data generated by the BGO crystal array and training the signal data generated by the BGO crystal array, and the D2 dictionary module is used for acquiring signal data generated by the LSO crystal array and training the signal data generated by the LSO crystal array.

Furthermore, the detector electronics module is used for extracting coincidence event information from different analog electric pulse signals, and comprises a pulse processing module and a coincidence processing module, wherein the pulse processing module is used for extracting information of single pulse events, including time, energy and position information, and the coincidence processing module classifies the single pulse events into paired coincidence events according to the information of the single pulse events.

Furthermore, the data processing and imaging module is used for processing and imaging the signals, and comprises a sampling module, an FPGA data processing module and an image reconstruction module. The sampling module is used for sampling data and collecting the data and comprises an ADC (analog to digital converter) sampling module and a TDC (time to digital converter) sampling module. The ADC sampling module is used for ADC sampling acquisition data, and the TDC sampling module is used for TDC sampling acquisition data. The FPGA data processing module is used for processing the data sampled by the sampling module and sending the data to the image reconstruction module. The image reconstruction module is used for restoring the received data into an image.

A gamma case processing method for separating two material cases and capable of continuously running comprises the following steps:

s1: detecting external gamma rays and converting the gamma rays into analog electric signals (scintillation pulses);

s2: separating pulse signals generated by different material crystals from the scintillation pulse signals by utilizing a dictionary learning method;

s3: extracting information conforming to the event from the scintillation pulse signal;

s4: the signals are processed and imaged.

In step S1, the photo detection module is used to detect external gamma rays and convert the gamma rays into analog electrical signals, wherein the crystal array for detecting gamma rays includes a BGO crystal array and an LSO crystal array.

In the step S2, the D1 dictionary module and the D2 dictionary module are used to separate the analog electrical pulse signals generated by different material crystals, and the specific steps of dictionary learning are as follows:

t11: when training sample data Y is known, initializing a dictionary X1 into a random matrix with each element in the matrix being close to zero;

t12: beta 1 is solved by using an LARS algorithm, and the LARS algorithm is expected to find a regression coefficientMake regression prediction

T13: updating the dictionary X2 by the solved beta, wherein Y is X beta, Y is training sample data, X is the dictionary, and beta is the solution of the algorithm;

t14: solving beta 2 again by using the test data Y, the updated dictionary X2 and the LARS algorithm;

t15: calculating an error between test data Y and X2 × β 2;

t16: updating X3 by using the test data and the solved beta 2, and solving beta 3 by using an LARS algorithm;

t17: repeating step T16 until there is no error between the test sample and XM × β M, wherein:

XM＝(x1，x2，...，xn)^T∈R^N×M。

in step S3, the detector electronics module extracts information of coincidence events, including time, energy, and location information, from the different analog electrical pulse signals.

In step S4, the data is sampled and collected by the sampling module and then transmitted to the FPGA for data processing, and the FPGA data processing module processes the sampled data and transmits the processed data to the image reconstruction module. The image reconstruction module restores the received data to an image again.

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the term "or/and" includes any and all combinations of one or more of the associated listed items.

The technical features of the embodiments described above may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments described above are not described, but should be considered as being within the scope of the present specification as long as there is no contradiction between the combinations of the technical features.

The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.