CN105005068B - A kind of method and system of pulse classification - Google Patents
A kind of method and system of pulse classification Download PDFInfo
- Publication number
- CN105005068B CN105005068B CN201510471443.3A CN201510471443A CN105005068B CN 105005068 B CN105005068 B CN 105005068B CN 201510471443 A CN201510471443 A CN 201510471443A CN 105005068 B CN105005068 B CN 105005068B
- Authority
- CN
- China
- Prior art keywords
- database
- pulse
- grader
- derivative
- data
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Landscapes
- Measurement Of Radiation (AREA)
Abstract
A kind of method of pulse classification, it includes step：S1：Obtain the non-stacking scintillation pulse database for meeting single event under low counting；S2：Grader is established, and classifier parameters are determined by the empirical data in database；S3：Test and using grader, the original sample of test data set and actual samples is inputted into grader, obtains classification results.Present invention also offers the system for realizing this method.The present invention can effectively be detected in system operation, classified, estimating, predicting the classification digitized belonging to event pulse, effectively it make use of most original information obtained by system, system is directly defined as optimization aim using final application and performs parameter, with extensive use range, from the information processing of most preceding high efficiency empirical formula to the end.
Description
Technical field
The invention belongs to the fields such as Digital Signal Processing, Photoelectric Signal Processing and nuclear detection, and in particular to a kind of pulse point
The method and system of class, the pulse that this method can be effectively to photon or particle event are classified.
Background technology
In the kernel analysis such as positron life spectrometer, Mossbauer spectrometer field, the nuclear detection such as energy disperse spectroscopy, radiation counter neck
Domain, and computer tomography (Computed Tomography, hereinafter referred to as CT), positron emission tomography
(Positron Emission Tomography, hereinafter referred to as PET), single photon emission tomographic imaging (Single Photo
Emission Computed Tomography, hereinafter referred to as SPECT) etc. Medical Imaging, the working machine of explorer portion
Reason is broadly divided into two kinds：A kind of is that high-energy photon is converted into the relatively low optical photon of energy or ultraviolet light light by scintillator
Son, then will be seen that light photon is converted into electric signal by photoelectric device；One kind be by high-energy photon by cadmium-zinc-teiluride (hereinafter referred to as
) etc. CZT semi-conducting material is converted into electric signal.Detector output under both the above working mechanism is electric signal.Often
The corresponding photon of individual time series fragment or particle event.
The high-energy photon or particle kind difference of detector are hit, detector will export the waveform of different attribute.For example, work as
The high-energy photon or particle of different-energy hit detector, and detector will export the electric impulse signal of different amplitudes.Gamma photons
Scintillation detector is hit with neutrino, exports the electric impulse signal of different time constant.Gamma photons hit coupling position sensitivity
During the different crystal bar of the scintillation crystal array of type photomultiplier (abbreviation PMT), from the angle pulse amplitudes of PMT outputs also not
Together.
" A new approach on Transaction on Nuclear Science in 2005 are published in by Q.Xie
For pulse processing in positron emission tomography " and H.Kim are published in for 2009
A multi-threshold on Nuclear Instruments and Methods in Physics Research A
Sampling method for TOF-PET signal processing, it is to be fitted threshold point with impulse model to obtain energy
Measure information.This method is limited by noise in event pulse shape and pulse.This method with the target that is estimated as to energy, and
Energy estimation is accurate and the affiliated classification of estimated energy is different.Among scattering refusal, it should accurately to estimate belonging to event
Energy classification be target.
The present invention is in order to solve the problems, such as pulse classification, there is provided a kind of method and system of pulse classification.
The content of the invention
In view of this, it is an object of the invention to provide a kind of method and system of pulse classification, this method can be effectively
Realize the classification of different attribute pulse.Present invention also offers the system for realizing this method.
To achieve the above object, the present invention provides following technical scheme：
A kind of method of pulse classification, it includes step：
S1：Obtain the non-stacking scintillation pulse database for meeting single event under low counting；
S2：Grader is established, and classifier parameters are determined by the empirical data in database；
S3：Test and using grader, the original sample of test data set and actual samples is inputted into grader, divided
Class result.
Preferably, in the method for above-mentioned pulse classification, used in the step S1 under the low counting of high sampling rate data acquisition
Event pulse database.
Preferably, in the method for above-mentioned pulse classification, the step S1 specifically includes procedure below：
(1.1) dose of radiation in source or the solid angle of adjustment detector are penetrated by reducing placement, reduces each detector and catch
The high energy light subnumber obtained, the event that each detector receives are a Poisson flows, and its average counter rate is
Wherein, miAnd qiBe respectively weak source dosage and weak source to the solid angle of detector, i is the ordinal number of weak source, and N is weak
The number in source, after the source of penetrating is placed, original pulse data are obtained from high sampling rate data acquisition；
(1.2) raw data base is established, initial data is converted into common floating-point number vector and is stored in database；
(1.3) derivative database is established, attribute interested is derived by the pulse data in raw data base, after calculating
It is stored in derivative database.
Preferably, in the method for above-mentioned pulse classification, the step S2 specifically includes procedure below：
(2.1) form of structural classification device；
(2.2) the purpose of grader being trained by empirical data in original and derivative database, training grader be obtain it is excellent
The classifier parameters of change.
A kind of system of pulse classification, it includes：
Database module, for obtaining event pulse database, including raw data base and derivative database；
Classifier training module, for constructing and training grader；
Test and using classifier modules, for running grader when test data set and real work.
Preferably, in the system of above-mentioned pulse classification, the database module includes：
High sampling rate data acquisition module, for obtaining the pulse data of high sampling rate；
Initial data library module, for by the original pulse data conversion in high sampling rate data acquisition module into floating number
Raw data base is stored as according to simultaneously structuring；
Derivative data library module, the derivative attribute for calculating each event in raw data base spread out as derivative attribute
Raw attribute carries out storage according to event and forms derivative database.
Preferably, in the system of above-mentioned pulse classification, the classifier training module includes：
Structural classification device module, for constructing the form of grader；
Classifier modules are trained, for being responsible for training grader by raw data base and derivative database, are obtained more excellent
Classifier parameters.
It can be seen from the above technical proposal that the present invention is obtained under low counting with high sampling rate data acquisition first
Event pulse database, then establishes grader, and during with the sample training grader in database, test or work, will adopt
The raw information that sample obtains inputs to grader, obtains pulse classification.
Brief description of the drawings
Fig. 1 is the flow chart of the pulse classification method of the present invention；
Fig. 2 is the pulse classification system construction drawing of the present invention；
Fig. 3 is the schematic diagram that the present invention carries out pulse classification according to energy value；
Fig. 4 is the schematic diagram of present invention training grader；
Fig. 5 is that the threshold value in the single attribute MAP graders of the present invention chooses figure；
Fig. 6 is the Posterior probability distribution of the attribute 1 in MAP graders of the present invention；
Fig. 7 is the Posterior probability distribution of the attribute 2 in MAP graders of the present invention；
Fig. 8 is the Posterior probability distribution of the attribute 1,2 in MAP graders of the present invention；
Fig. 9 is the corresponding minimum MER of the present invention threshold value for acting on attribute 1, the voltage threshold of the offer pulse attribute
Travel through 0.1V to 0.6V, ELLD 400keV；
Figure 10 is the corresponding vision response test for the attribute Δ t that present invention traversal 0.1V to 0.6V voltage threshold provides；
The corresponding average mistake that Figure 11 is the attribute Δ t that the voltage threshold between additional 0.1V to the 0.6V of the present invention provides
Rate by mistake；
Figure 12 is the cross validation of vision response test of the present invention；
Figure 13 is a kind of schematic diagram of canonical system of the present invention；
Figure 14 is the signal of another canonical system of the present invention.
Embodiment
The invention discloses a kind of method and system of pulse classification, this method can effectively realize different attribute pulse
Classification.Present invention also offers the system for realizing this method.
The attribute of different events is reflected in the surveying in physical quantity of observation system, is the different attribute of electric pulse.In order to
With the data prediction or the attribute of estimation different event measured in the observation system, the present invention proposes the arteries and veins based on data-driven
Sorting technique and its system are rushed, is characterized in, the input by the use of the most original information that observation system exports as grader, through undue
After class device, the attributes estimation value of event is directly given.It is centrifugal pump for the observable quantity overwhelming majority in digital display circuit, classification
The event attribute estimate of device output is these centrifugal pumps.
The present invention can estimate the attribute of event, and these attributes can be the position that event is hit or hit spy
The particle or photon species of device are surveyed, can also be it is that non-scatter refusal (is that scattering is 1,0) non-scatter is.
Data acquisition in the present invention can be high sampling rate oscillograph, the sample circuit of multivoltage threshold value, at a high speed
Equidistant AD sample circuits or be low sampling rate analog-digital converter for slowing down etc., analog signal is changed into width
Value and time are all discrete data signals.
So that scattering is refused as an example, for the electronic system of high speed equidistant AD samplings, pulse product is typically calculated
Score value, the estimation to energy is used as by the use of pulse integration value afterwards.When energy is when outside the energy window of definition, i.e., event rejecting.
When AD sample frequency step-downs, the measurement of this energy will become inaccurate, thus will become inaccurate with energy window refusal scattering
Really.When digitizing event pulse using the sampling of MVT forms, energy can not be obtained by integrating by sampling the raw information of acquisition
Value, thus energy value is obtained by waveform fitting, then refusal is scattered by this energy value.
As shown in figure 1, the inventive method comprises the following steps：
(1) the event pulse database under low counting is obtained with high sampling rate data acquisition.It is required that pulse database
In sample number more than 5000.The number of pulse is more, and the statistical noise of pulse attribute is smaller.Comprise the following steps that：
(1.1) dose of radiation in source or the solid angle of adjustment detector are penetrated by reducing placement, reduces each detector and catch
The high energy light subnumber obtained, the event that each detector receives are a Poisson flows, and its average counter rate is
Wherein, miAnd qiBe respectively weak source dosage and weak source to the solid angle of detector, i is the ordinal number of weak source, and N is weak
The number in source, when obtaining pulse database, N=1 can be made.And make miAnd qiIt is sufficiently small.So ensure in pulse database
Event is single event mostly.Then, original pulse data are obtained from high sampling rate data acquisition.
(1.2) raw data base is established, original pulse data are converted into common floating-point number vector is stored in database
In.
(1.3) derivative database is established, attribute interested is derived by the pulse data in raw data base, after calculating
It is stored in derivative database.Even if one kind of the derivative attribute of energy in Fig. 3, derivative attribute can be as the gold mark of classifier training
Standard, can also be as the input variable of grader.
(2) grader is established, and classifier parameters are determined by the empirical data in database.Comprise the following steps that：
(2.1) form of structural classification device, this form can be Bayes graders, neural network classifier, cluster point
The existing grader such as class device, or the combination between them.
(2.2) grader is trained by empirical data in original and derivative database.Train grader the purpose of be obtain it is excellent
The classifier parameters of change.Fig. 4 is the training process of grader.It is feasible by searching for for the training process of goldstandard be present
Classifier parameters, obtain a less vision response test (Mean Error Rate, MER).Fig. 5 is corresponding to single threshold value
The classification thresholds Choice of time difference attribute.WhenWhen, MER is minimum.It is with some for unsupervised study
Specific optimization aim defines, such as least square, maximum likelihood, maximum a posteriori probability etc..
(3) test and using grader, specific implementation is by the input point of the original sample of test data set and actual samples
Class device, obtains classification results.
The system construction drawing of pulse classification disclosed by the invention based on data-driven is as shown in Fig. 2 including database mould
Block 100, classifier training module 200, test and use classifier modules 300, wherein module 100 is used to obtain event pulse number
According to storehouse, including raw data base and derivative database, for establishing more accurate event pulse database.Module 200 is used for
Construction and training grader.Module 300 is used to test and using grader, i.e., for being run when test data set and real work
Grader；
Database module 100 is used to obtain event pulse database, including raw data base and derivative database.The module
Three submodules, respectively high-speed AD data acquisition module 110, initial data library module 120, derivative database mould can be divided into
Block 130.High-speed AD data acquisition module 110 is used for the data for obtaining high-speed AD.Initial data library module 120 is used for high-speed AD
AD data conversions in data acquisition module 110 are stored as raw data base into floating data and structuring.Derivative database
The derivative attribute (such as energy, cross threshold time difference etc.) that module 130 is used to calculate each event in raw data base is used as and spread out
Raw attribute.Derivative attribute carries out storage according to event and forms derivative database.Derivative attribute is shown according to classifying into column hisgram
The posterior probability density of sorting room can be shown.Fig. 6 and Fig. 7 is the posterior probability density of two attributes.Fig. 8 is to include two
The distribution map of the posterior probability function of attribute.Black portions in Fig. 6-8 represent the vision response test of classification.
Classifier training module 200 is used to constructing and training grader.The module can be divided into two submodules, construction point
Class device module 210, training classifier modules 220.Structural classification device module 210 is used for the form for constructing grader, and Bayes divides
Class device, neural network classifier, Cluster Classification device, k nearest neighbor grader etc. can be the concrete forms of grader.Training classification
Device module 220 is responsible for training grader by raw data base and derivative database, obtains preferably classifier parameters.For having
The training process of supervision, some attribute can be chosen as optimization reference of the goldstandard as training.For unsupervised training
Process, target of some Optimality Criteria as optimization can be chosen.
Test and run grader when being used for test data set and real work using classifier modules 300.
As shown in FIG. 13 and 14, Figure 13 is a kind of schematic diagram of canonical system of the present invention；Figure 14 is another for the present invention
The schematic diagram of canonical system.With reference to Figure 13 and Figure 14, below in conjunction with specific embodiments, to the side of the pulse classification of the present invention
Method and system are illustrated：
Pulse classification method and system proposed by the present invention based on data-driven.The parameter being related to (does not include classification
The input variable of device) need to be adjusted to reach suitable grader operating point according to the characteristics of acquisition data.Arrange herein
Go out the parameter of this application instance processes data：
Real system used in step (1) is to use LYSO crystal and Hamamatsu R9800PMT.Crystalline size is
16.5mm×16.5mm×10.0mm.Crystal and PMT coupling surfaces are 100 faces, except coupling surface outside, other faces are with Teflon bag
Wrap up in.The sample rate of data acquistion system is 50Ghz, bandwidth 16Ghz (such as Figure 13 and Figure 14).The positive electron that source is 511kev is penetrated to fall into oblivion
Go out gammaphoton.The average pulse rise time is about 2ns, after trailing edge exponential fitting, time constant 42.5497ns.It is real
Using the bayes graders based on maximum a posteriori probability (MAP) criterion in example.
Fig. 9 is the corresponding minimum MER of the present invention threshold value for acting on attribute 1, the voltage threshold of the offer pulse attribute
Travel through 0.1V to 0.6V, ELLD 400keV.
Figure 10 is the corresponding vision response test for the attribute Δ t that present invention traversal 0.1V to 0.6V voltage threshold provides.
The corresponding average mistake that Figure 11 is the attribute Δ t that the voltage threshold between additional 0.1V to the 0.6V of the present invention provides
Rate by mistake.
Figure 12 is the cross validation of vision response test of the present invention.
The event category that the method and system of the present invention can be used in various nuclear detection, kernel analysis, Nuclear medical instruments, fit
Together in the event pulse for handling various digitized forms, including equally spaced AD samplings, the AD samplings of unequal interval, multivoltage threshold
Value sampling (or referred to as MVT sampling).The form of grader can be not limited to the Bayes graders in example.Nerve
Network classifier, Cluster Classification device etc. both falls within the protection domain of the present invention and system.
It is obvious to a person skilled in the art that the invention is not restricted to the details of above-mentioned one exemplary embodiment, Er Qie
In the case of without departing substantially from spirit or essential attributes of the invention, the present invention can be realized in other specific forms.Therefore, no matter
From the point of view of which point, embodiment all should be regarded as exemplary, and be nonrestrictive, the scope of the present invention is by appended power
Profit requires rather than described above limits, it is intended that all in the implication and scope of the equivalency of claim by falling
Change is included in the present invention.Any reference in claim should not be considered as to the involved claim of limitation.
Moreover, it will be appreciated that although the present specification is described in terms of embodiments, not each embodiment is only wrapped
Containing an independent technical scheme, this narrating mode of specification is only that those skilled in the art should for clarity
Using specification as an entirety, the technical solutions in the various embodiments may also be suitably combined, forms those skilled in the art
It is appreciated that other embodiment.
Claims (2)
- A kind of 1. method of pulse classification, it is characterised in that：Including step：S1：Obtain the non-stacking scintillation pulse database for meeting single event under low counting；The step S1 specifically includes procedure below：(1.1) dose of radiation in source or the solid angle of adjustment detector are penetrated by reducing placement, reduces each detector capture High energy light subnumber, the event that each detector receives are a Poisson flows, and its average counter rate is<mrow> <mi>R</mi> <mo>=</mo> <munderover> <mo>&Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>N</mi> </munderover> <msub> <mi>m</mi> <mi>i</mi> </msub> <msub> <mi>q</mi> <mi>i</mi> </msub> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>1</mn> <mo>)</mo> </mrow> </mrow>Wherein, miAnd qiBe respectively weak source dosage and weak source to the solid angle of detector, i is the ordinal number of weak source, and N is weak source Number, after the source of penetrating is placed, original pulse data are obtained with 50GHz high sampling rate data acquisition；(1.2) raw data base is established, original pulse data are converted into common floating-point number vector is stored in database；(1.3) derivative database is established, attribute interested, the sense are derived by the original pulse data in raw data base The attribute of interest was that threshold time is poor, and derivative database is stored in after calculating；S2：Grader is established, and classifier parameters are determined by the empirical data in database；The step S2 specifically includes procedure below：(2.1) form of structural classification device；(2.2) grader is trained by the empirical data in raw data base and derivative database, the grader ginseng optimized Number；S3：Test and using grader, the original sample of test data set and actual samples is inputted into grader, obtains classification knot Fruit.
- 2. the system of the pulse classification of a kind of method of pulse classification using described in claim 1, it is characterised in that：Including：Database module, for obtaining event pulse database, including raw data base and derivative database；The database mould Block includes：High sampling rate data acquisition module, for obtaining the data of high sampling rate, the high sampling rate is 50GHz；Original number According to library module, for the pulse data in high sampling rate data acquisition module to be converted into being stored as floating data and structuring Raw data base；Derivative data library module, the derivative attribute for calculating each event in raw data base are used as derivative attribute, Derivative attribute carries out storage according to event and forms derivative database, and the derivative attribute was that threshold time is poor；Classifier training module, for constructing and training grader；The classifier training module includes：Structural classification device mould Block, for constructing the form of grader；Classifier modules are trained, for being responsible for instructing by raw data base and derivative database Practice grader, obtain preferably classifier parameters；Test and using classifier modules, for running grader when test data set and real work.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510471443.3A CN105005068B (en) | 2015-06-25 | 2015-08-04 | A kind of method and system of pulse classification |
Applications Claiming Priority (3)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2015103672842 | 2015-06-25 | ||
| CN201510367284 | 2015-06-25 | ||
| CN201510471443.3A CN105005068B (en) | 2015-06-25 | 2015-08-04 | A kind of method and system of pulse classification |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN105005068A CN105005068A (en) | 2015-10-28 |
| CN105005068B true CN105005068B (en) | 2018-04-06 |
Family
ID=54377802
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201510471443.3A Active CN105005068B (en) | 2015-06-25 | 2015-08-04 | A kind of method and system of pulse classification |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN105005068B (en) |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105277964B (en) * | 2015-10-30 | 2018-05-18 | 中国船舶重工集团公司第七一九研究所 | A kind of computational methods of pulse count signal rate |
| CN105842544B (en) * | 2016-03-18 | 2018-09-18 | 南京瑞派宁信息科技有限公司 | A kind of the scintillation pulse time label and its cross validation method of iteration |
| CN109669206A (en) * | 2019-03-03 | 2019-04-23 | 南昌华亮光电有限责任公司 | Circulating type liquid scintillator intelligence energy disperse spectroscopy system and its signal processing method |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101226133A (en) * | 2008-01-28 | 2008-07-23 | 宁波大学 | Method for specification and recognition of hemocyte pulse signal |
| WO2008110182A1 (en) * | 2007-03-09 | 2008-09-18 | Cern - European Organization For Nuclear Research | Method, apparatus and computer program for measuring the dose, dose rate or composition of radiation |
| CN101669828A (en) * | 2009-09-24 | 2010-03-17 | 复旦大学 | System for detecting pulmonary malignant tumour and benign protuberance based on PET/CT image texture characteristics |
| CN102576087A (en) * | 2009-10-07 | 2012-07-11 | 法国原子能及替代能源委员会 | Method for processing data derived from an ionizing radiation detector |
| CN103381095A (en) * | 2012-05-03 | 2013-11-06 | 三星电子株式会社 | Apparatus and method for generating image in positron emission tomography |
| CN104656119A (en) * | 2013-11-19 | 2015-05-27 | 苏州瑞派宁科技有限公司 | Method and system for restoring flash pulse information |
| CN104777508A (en) * | 2015-03-30 | 2015-07-15 | 东南大学 | Digital pulse overlapping peak separation algorithm based on model base |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20060157655A1 (en) * | 2005-01-19 | 2006-07-20 | Richard Mammone | System and method for detecting hazardous materials |
-
2015
- 2015-08-04 CN CN201510471443.3A patent/CN105005068B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2008110182A1 (en) * | 2007-03-09 | 2008-09-18 | Cern - European Organization For Nuclear Research | Method, apparatus and computer program for measuring the dose, dose rate or composition of radiation |
| CN101226133A (en) * | 2008-01-28 | 2008-07-23 | 宁波大学 | Method for specification and recognition of hemocyte pulse signal |
| CN101669828A (en) * | 2009-09-24 | 2010-03-17 | 复旦大学 | System for detecting pulmonary malignant tumour and benign protuberance based on PET/CT image texture characteristics |
| CN102576087A (en) * | 2009-10-07 | 2012-07-11 | 法国原子能及替代能源委员会 | Method for processing data derived from an ionizing radiation detector |
| CN103381095A (en) * | 2012-05-03 | 2013-11-06 | 三星电子株式会社 | Apparatus and method for generating image in positron emission tomography |
| CN104656119A (en) * | 2013-11-19 | 2015-05-27 | 苏州瑞派宁科技有限公司 | Method and system for restoring flash pulse information |
| CN104777508A (en) * | 2015-03-30 | 2015-07-15 | 东南大学 | Digital pulse overlapping peak separation algorithm based on model base |
Also Published As
| Publication number | Publication date |
|---|---|
| CN105005068A (en) | 2015-10-28 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Müller et al. | A novel DOI positioning algorithm for monolithic scintillator crystals in PET based on gradient tree boosting | |
| Müller et al. | Gradient tree boosting-based positioning method for monolithic scintillator crystals in positron emission tomography | |
| Raczyński et al. | Novel method for hit-position reconstruction using voltage signals in plastic scintillators and its application to Positron Emission Tomography | |
| CN105607111B (en) | A kind of γ nuclide identification method | |
| US10061043B2 (en) | Apparatus and method for the evaluation of gamma radiation events | |
| JP5800983B2 (en) | Method and apparatus for scintillation pulse information acquisition | |
| CN104656115B (en) | A kind of method and system of time mark combination | |
| Hunter et al. | Calibration method for ML estimation of 3D interaction position in a thick gamma-ray detector | |
| CN104656119B (en) | The method and system that a kind of scintillation pulse information restores | |
| CN105005068B (en) | A kind of method and system of pulse classification | |
| Mohammadian-Behbahani et al. | A comparison study of the pile-up correction algorithms | |
| CN106706127B (en) | Multi-photon detection method based on SiPM | |
| US12032108B2 (en) | Calibration method and system for photon or particle counting detectors | |
| Deng et al. | Quadratic programming time pickoff method for multivoltage threshold digitizer in PET | |
| Maebe et al. | Simulation study on 3D convolutional neural networks for time-of-flight prediction in monolithic PET detectors using digitized waveforms | |
| JP2005043104A (en) | Method for calibrating radiation position detector | |
| Torres-Espallardo et al. | Effect of inter-crystal scatter on estimation methods for random coincidences and subsequent correction | |
| Hachem et al. | Labeling strategy to improve neutron/gamma discrimination with organic scintillator | |
| US8676744B2 (en) | Physics-based, Bayesian sequential detection method and system for radioactive contraband | |
| Maebe et al. | Effect of detector geometry and surface finish on cerenkov based time estimation in monolithic BGO detectors | |
| CN105842544B (en) | A kind of the scintillation pulse time label and its cross validation method of iteration | |
| EP3676640B1 (en) | Methods and systems for calibration of particle detectors | |
| Maj et al. | FPGA simulations of charge sharing effect compensation algorithms for implementation in deep sub-micron technologies | |
| Morelock et al. | Characterization and description of a spectrum unfolding method for the CATRiNA neutron detector array | |
| Zepeda-Fernández et al. | Electric charge estimation using a SensL SiPM |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |