CN107220961A - A kind of fluorescent molecule tomography rebuilding method based on half threshold value tracing algorithm - Google Patents
A kind of fluorescent molecule tomography rebuilding method based on half threshold value tracing algorithm Download PDFInfo
- Publication number
- CN107220961A CN107220961A CN201710448650.6A CN201710448650A CN107220961A CN 107220961 A CN107220961 A CN 107220961A CN 201710448650 A CN201710448650 A CN 201710448650A CN 107220961 A CN107220961 A CN 107220961A
- Authority
- CN
- China
- Prior art keywords
- mrow
- msup
- target
- msub
- threshold value
- 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.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 47
- 238000003325 tomography Methods 0.000 title claims abstract description 29
- 230000003287 optical effect Effects 0.000 claims abstract description 43
- 238000005259 measurement Methods 0.000 claims abstract description 24
- 238000003384 imaging method Methods 0.000 claims description 10
- 238000009792 diffusion process Methods 0.000 claims description 8
- 210000003484 anatomy Anatomy 0.000 claims description 7
- 238000001506 fluorescence spectroscopy Methods 0.000 claims description 6
- 230000008569 process Effects 0.000 claims description 6
- 239000011159 matrix material Substances 0.000 claims description 5
- 238000012804 iterative process Methods 0.000 claims description 4
- 238000002591 computed tomography Methods 0.000 claims description 3
- 230000005284 excitation Effects 0.000 claims description 3
- 230000002452 interceptive effect Effects 0.000 claims description 3
- 230000011218 segmentation Effects 0.000 claims description 3
- 230000005540 biological transmission Effects 0.000 claims description 2
- 201000009310 astigmatism Diseases 0.000 claims 1
- 238000010276 construction Methods 0.000 claims 1
- 230000004907 flux Effects 0.000 claims 1
- 210000001519 tissue Anatomy 0.000 description 6
- 230000008901 benefit Effects 0.000 description 3
- 230000000694 effects Effects 0.000 description 3
- 238000002474 experimental method Methods 0.000 description 3
- 239000000523 sample Substances 0.000 description 3
- 238000010586 diagram Methods 0.000 description 2
- 238000010172 mouse model Methods 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 206010068052 Mosaicism Diseases 0.000 description 1
- 230000004913 activation Effects 0.000 description 1
- 210000004027 cell Anatomy 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 201000010099 disease Diseases 0.000 description 1
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 description 1
- 238000009509 drug development Methods 0.000 description 1
- 238000013399 early diagnosis Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 239000000686 essence Substances 0.000 description 1
- 239000007850 fluorescent dye Substances 0.000 description 1
- 230000004927 fusion Effects 0.000 description 1
- 238000001727 in vivo Methods 0.000 description 1
- 210000003734 kidney Anatomy 0.000 description 1
- 210000004185 liver Anatomy 0.000 description 1
- 210000004072 lung Anatomy 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 230000002969 morbid Effects 0.000 description 1
- 239000002547 new drug Substances 0.000 description 1
- 238000010606 normalization Methods 0.000 description 1
- 210000000056 organ Anatomy 0.000 description 1
- 239000000700 radioactive tracer Substances 0.000 description 1
- 210000003765 sex chromosome Anatomy 0.000 description 1
- 210000002784 stomach Anatomy 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/50—Image enhancement or restoration using two or more images, e.g. averaging or subtraction
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/0033—Features or image-related aspects of imaging apparatus classified in A61B5/00, e.g. for MRI, optical tomography or impedance tomography apparatus; arrangements of imaging apparatus in a room
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/0059—Measuring for diagnostic purposes; Identification of persons using light, e.g. diagnosis by transillumination, diascopy, fluorescence
- A61B5/0071—Measuring for diagnostic purposes; Identification of persons using light, e.g. diagnosis by transillumination, diascopy, fluorescence by measuring fluorescence emission
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/23—Design optimisation, verification or simulation using finite element methods [FEM] or finite difference methods [FDM]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T15/00—3D [Three Dimensional] image rendering
Landscapes
- Engineering & Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Physics & Mathematics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Biophysics (AREA)
- Animal Behavior & Ethology (AREA)
- Veterinary Medicine (AREA)
- Public Health (AREA)
- General Health & Medical Sciences (AREA)
- Surgery (AREA)
- Molecular Biology (AREA)
- Pathology (AREA)
- Biomedical Technology (AREA)
- Heart & Thoracic Surgery (AREA)
- Medical Informatics (AREA)
- Computer Hardware Design (AREA)
- Evolutionary Computation (AREA)
- General Engineering & Computer Science (AREA)
- Radiology & Medical Imaging (AREA)
- Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
- Geometry (AREA)
- Computer Graphics (AREA)
- Investigating, Analyzing Materials By Fluorescence Or Luminescence (AREA)
Abstract
The invention belongs to molecular image technical field, disclose a kind of fluorescent molecule tomography rebuilding method based on half threshold value tracing algorithm, using multi-point shooting, limited angle is measured, build the sparse canonical model of non-convex problem, the linear relationship that the measurement data on surface is distributed with fluorescent target is set up, linear relationship is converted into 1/2 least norm problem solving, the distributed in three dimensions and concentration of the fluorescent target for rebuilding target internal is obtained；By threshold value iteration and matching pursuit algorithm to model solution.The present invention advantageously reduces the pathosis of problem；By the use of optical property parameter and anatomical information as priori, the accuracy of reconstructed results and the quality of reconstruction image are improved；Problems of Reconstruction is converted into 1/2 least norm problem of Prescribed Properties, solved using half threshold value tracing algorithm so that solution ensures robustness and accelerated reconstruction time of the Problems of Reconstruction to parameter while meeting 1/2 Norm minimum.
Description
Technical field
The invention belongs to molecular image technical field, more particularly to a kind of fluorescence molecule based on half threshold value tracing algorithm are disconnected
Layer imaging reconstruction method.
Background technology
Fluorescent molecular tomography (abbreviation FMT) be developed recently get up it is a kind of have body Research Prospects it is new into
As mode.It makes fluorescence probe launch light using outside near infrared light light source activation fluorescence probe (fluorogen, fluorescent dye etc.)
Son, fluorescence signal is collected using fluorescent collecting device (generally CCD camera), with reference to the mathematical modeling of optical transport model, can be with
Obtain and rebuild the position of fluorescence probe and concentration in target, realize and cell and molecular water are carried out to the bioprocess under condition of living organism
Flat qualitative and quantitative study.It is currently widely used for the fields such as disease early diagnosis, curative effect monitoring, new drug development.Fluorescence point
The mathematical modeling of sub- fault imaging belongs to inverse problem, with Very Ill-conditioned.Basic reason is the strong scattering characteristic of light so that
The transmission in photon portion in vivo passes through a large amount of random scattering processes no longer along straightline propagation.Meanwhile, experiment is adopted
The fluorescence data of collection is confined to imageable target surface, and sampled point limited amount so that inverse problem is typical underdetermined equation
Solve problems, further increase the ill-posedness of Solve problems.Multi-point shooting and limited angle projection can increase measurement number
According to, to a certain extent alleviate problem ill-posedness.But the redundant data in the problem of thus also bringing new, measurement data increases
It is many, increase is consumed during calculating.In order to be accurately positioned target location, it is necessary to reconstruct a small amount of effectively solution from substantial amounts of redundant data；
Then, mathematical modeling is selected, how with more suitably Algorithm for Solving model, it is fluorescent molecular tomography to obtain more accurate solution
The key problem of research.
In summary, the problem of prior art is present be：The redundant data in the measurement data of collection fluorescence increases at present,
Increase is consumed during calculating.
The content of the invention
The problem of existing for prior art, it is disconnected the invention provides a kind of fluorescence molecule based on half threshold value tracing algorithm
Layer imaging reconstruction method.
The present invention is achieved in that a kind of fluorescent molecule tomography rebuilding method based on half threshold value tracing algorithm,
The fluorescent molecule tomography rebuilding method based on half threshold value tracing algorithm uses multi-point shooting, limited angle measurement, structure
The sparse canonical model of non-convex problem is built, the linear relationship that the measurement data on surface is distributed with fluorescent target is set up, by linear relationship
1/2 least norm problem solving is converted into, the distributed in three dimensions and concentration of the fluorescent target for rebuilding target internal is obtained；Pass through threshold
It is worth iteration and matching pursuit algorithm to model solution；
The 1/2 least norm problem representation is：
Wherein, λ is regularization parameter, and A is sytem matrix, and X is the fluorescent target distributed in three dimensions and concentration to be solved, and light leads to
Metric density Φm。
Further, the fluorescent molecule tomography rebuilding method based on half threshold value tracing algorithm comprises the following steps：
Step one, fluorescence measurement data are obtained；
Step 2, obtains the anatomical information and optical property parameter of reconstructed object；
Step 3, discrete acquisition network relevant information is carried out to imageable target using Amira softwares；
Step 4, based on optical transport model and finite element theory, will rebuild the anatomical information and optical characteristics of target
Parameter sets up linear pass of the fluorescence data of surface limited angle with rebuilding the distribution of target internal fluorescent target as prior information
System；
Step 5,1/2 least norm problem is converted into by linear relationship：
λ is regularization parameter；
Step 6, to model, is solved using half threshold value iterative technique, introduces half threshold operator as follows：
Then, half threshold value iterative technique is with being expressed as form：
Wherein, B (X)=X+AT(Φ-AX)；
Step 7, in order to reduce the iterations in step 6, introduces method for tracing, makes B (X) ≈ X, then each iteration
There is following process：
Step 8, by the iterative process in step 7, obtains the reconstructed results X of the n-th stepn.When | | Φ-AXn||/||Φ|
|≤1e-05 or | | Xn-Xn-1| | during≤1e-08, stop iteration；
Step 9, the thinner grid of discrete acquisition size of mesh opening is carried out to imageable target using Amira softwares；
Step 10, shows result, and the anatomical structure of reconstructed results and imageable target is carried out into image co-registration, Tecplot is used
Software is shown.
Further, the step one is specifically included：
1) excitation source carries out the transmission-type fault imaging of limited angle to the reconstruction target being fixed in electronically controlled rotary table；
Transmission-type fault imaging, laser and optical detecting instrument is placed on the both sides of imageable target, and laser irradiation is rebuild target and swashed
The target that fluoresces sends fluorescence, and fluorescence penetrates imageable target and detected by the optical detecting instrument on laser opposite；Limited angle
Transmission-type tomoscan, rotates one with computer control electronically controlled rotary table and is more than 90 ° at equal intervals；
2) measurement data is obtained using optical detecting instrument, obtains pharosage Φm。
Further, the step 2 is specifically included：
A) anatomical information of reconstructed object
Three-dimensional reconstruction is carried out to computer tomography data for projection, and imageable target is obtained with the pretreatment of 3DMED softwares
Three-dimensional data；Tissue segmentation is carried out to volume data using the semi-automatic dividing method of man-machine interactive in 3DMED softwares,
Obtain the anatomical structure of imageable target；
B) optical property parameter is obtained
Using anatomical information and application based on the specific optical 3-dimensional method for reconstructing of biological tissue based on region
Diffusion optical tomography algorithm obtain the optical property parameter of each tissue in imageable target.
Further, the step 4 is specifically included：
(1) optical transport model, transmitting procedure of the light in imageable target is described using diffusion approximation equation；
(2) according to finite element theory, and the anatomical information and optical property parameter of converged reconstruction target, diffusion is near
It is discrete like equation, build linear relationship of the measurement data on surface with rebuilding the distribution of target internal fluorescent target：
Φm=AX；
Wherein A is sytem matrix, and X is the fluorescent target distributed in three dimensions and concentration to be solved, and is non-negative.
Another object of the present invention is to provide the fluorescence molecule tomography based on half threshold value tracing algorithm described in a kind of application
The fluorescent molecular tomography system of imaging reconstruction method.
Advantages of the present invention and good effect are：Using multi-point shooting, limited angle measurement.Set up the measurement data on surface
The linear relationship being distributed with fluorescent target, 1/2 least norm problem solving is converted into by linear relationship, is obtained and is rebuild in target
The distributed in three dimensions and concentration of the fluorescent target in portion；1/2 norm has carried out further rarefaction constraint to the solution of problem, sufficiently
The sparse characteristic of the target of problem is make use of, more accurate reconstructed results can be obtained.1/2 norm minimum problem is simultaneously
It is a non-convex sex chromosome mosaicism, it is long to there is reconstruction time, the defect such as unstable result.In order to improve reconstruction quality, we use
Half threshold value tracing algorithm；Target problem is solved by half thresholding algorithm, selects candidate to add when supporting collection in threshold value iterative process
Enter the thought of tracking, reduce iterations；By combining thresholding algorithm to parametric stability and tracing algorithm progressively optimal selection
Advantage, while high-quality reconstruction being realized, increase parametric stability and reduce reconstruction time.
The present invention is based on light transporting theory and finite element method, make use of the priori such as optical property parameter and anatomical structure to believe
Breath, using multi-point shooting, limited angle measurement builds the non-sparse canonical model of convex problem, passes through threshold value iteration and tracing algorithm
To model solution；With reference to threshold value iteration algorithm to stability and the progressively optimal tracing algorithm of parameter the characteristics of, effectively carry
The high reconstructed results of fluorescent molecular tomography, add stability of the algorithm for parameter, accelerate asking for non-convex problem
Solution preocess, has important application value in fields such as optical fault three-dimensional reconstruction algorithms.
The multi-point shooting that the present invention is used, limited angle measurement, adds measurement data, advantageously reduces the morbid state of problem
Property；By the use of optical property parameter and anatomical information as priori, the accuracy and reconstruction figure of reconstructed results are improved
The quality of picture；Problems of Reconstruction is converted into the 1/2- least norm problems of Prescribed Properties, using half threshold value tracing algorithm come
Solve so that solution ensures robustness and accelerated reconstruction time of the Problems of Reconstruction to parameter while meeting 1/2- Norm minimums.
Brief description of the drawings
Fig. 1 is the fluorescent molecule tomography rebuilding method stream provided in an embodiment of the present invention based on half threshold value tracing algorithm
Cheng Tu.
Fig. 2 is the fluorescent molecule tomography rebuilding method provided in an embodiment of the present invention based on half threshold value tracing algorithm
Implementation process figure.
Fig. 3 is the digital mouse model schematic diagram provided in an embodiment of the present invention for emulation experiment.
Fig. 4 is that algorithm for reconstructing provided in an embodiment of the present invention is compared figure with the parameter of other algorithms.
Fig. 5 is the reconstructed results schematic diagram that algorithm for reconstructing provided in an embodiment of the present invention is obtained.
Embodiment
In order to make the purpose , technical scheme and advantage of the present invention be clearer, with reference to embodiments, to the present invention
It is further elaborated.It should be appreciated that the specific embodiments described herein are merely illustrative of the present invention, it is not used to
Limit the present invention.
The application principle of the present invention is explained in detail below in conjunction with the accompanying drawings.
As shown in figure 1, the fluorescent molecule tomography rebuilding provided in an embodiment of the present invention based on half threshold value tracing algorithm
Method comprises the following steps：
S101：Obtain the measurement data of many shot points, limited angle；
S102：Obtain the anatomical information and optical property parameter for rebuilding target；
S103：Grid universe is rebuild by half threshold value tracer technique, the distributed in three dimensions of fluorescent target is realized.
The application principle of the present invention is further described below in conjunction with the accompanying drawings.
As shown in Fig. 2 the fluorescent molecule tomography rebuilding provided in an embodiment of the present invention based on half threshold value tracing algorithm
Method specifically includes following steps：
(1) fluorescence measurement data are obtained：
1a) excitation source the reconstruction target that is fixed in electronically controlled rotary table is carried out the transmission-type tomography of limited angle into
Picture；
Transmission-type fault imaging, laser and optical detecting instrument is placed on the both sides of imageable target, laser irradiation weight
Building target excites fluorescent target to send fluorescence, and fluorescence penetrates imageable target and detected by the optical detecting instrument on laser opposite.
Limited angle transmission-type tomoscan, is rotated to an angle at equal intervals with computer control electronically controlled rotary table, general big
In 90 ° (selecting 120 ° in this example), laser transmitting point laser irradiation imageable target typically turns an angle and excited once, adopts
Collect first order fluorescence data.
Measurement data 1b) is obtained using optical detecting instrument, pharosage Φ is obtainedm；
In step 1a) in, by the three-dimensional energy of the organism surface of measurement data application non-contact type optical sectioning imaging method
Measure the three-dimensional fluorescence data distribution that reconstruction technique obtains imageable target body surface face.
(2) anatomical information and optical property parameter of reconstructed object are obtained：
2a) the anatomical information of reconstructed object
Three-dimensional reconstruction is carried out to computer tomography data for projection, and imageable target is obtained with the pretreatment of 3DMED softwares
Three-dimensional data；Tissue segmentation is carried out to volume data using the semi-automatic dividing method of man-machine interactive in 3DMED softwares,
Obtain the anatomical structure of imageable target；
2b) obtain optical property parameter
Using anatomical information and application based on the specific optical 3-dimensional method for reconstructing of biological tissue based on region
Diffusion optical tomography algorithm obtain the optical property parameter of each tissue in imageable target.
(3) discrete acquisition network relevant information is carried out to imageable target using Amira softwares.
(4) optical transport model and finite element theory are based on, the anatomical information and optical property parameter of target will be rebuild
As prior information, linear relationship of the fluorescence data of surface limited angle with rebuilding the distribution of target internal fluorescent target is set up.
4a) optical transport model, transmitting procedure of the light in imageable target is described using diffusion approximation equation；
4b) according to finite element theory, and the anatomical information and optical characteristics of the reconstruction target of fusion steps (2) acquisition
Parameter, diffusion approximation equation is discrete, build linear pass of the measurement data on surface with rebuilding the distribution of target internal fluorescent target
System：
Φm=AX；
Wherein A is sytem matrix, and X is the fluorescent target distributed in three dimensions and concentration to be solved, and is non-negative.
(5) above-mentioned linear relationship is converted into 1/2 least norm problem：
λ is regularization parameter.
(6) to the model in step (5), solved using half threshold value iterative technique, introduce half threshold operator as follows：
Then, half threshold value iterative technique can be with being expressed as form：
Wherein, B (X)=X+AT(Φ-AX)。
(7) in order to reduce the iterations in (6), we introduce method for tracing, make B (X) ≈ X, and then each iteration has
Following process：
(8) by the iterative process in (7), the reconstructed results X of the n-th step is obtainedn.When | | Φ-AXn||/||Φ||≤1e-
05 or | | Xn-Xn-1| | during≤1e-08, stop iteration.
Above-mentioned steps (5)-(8) are coarse grid process of reconstruction A of the invention.
(9) the thinner grid of discrete acquisition size of mesh opening is carried out to imageable target using Amira softwares.
(10) result is shown, the anatomical structure of the reconstructed results of step (9) and imageable target is subjected to image co-registration, is used
Tecplot softwares are shown.
The application effect of the present invention is explained in detail with reference to reconstructed results.
Fig. 3 is used for the digital mouse model of emulation experiment.Wherein figure includes main several organs, such as heart 1, lung 2, liver
3, musculature 4, stomach 5, kidney 6.
Fig. 4 is the reconstructed results based on the present invention.The real center position for rebuilding target is (12,8,18) mm, and algorithm is obtained
Target's center position be (11.62,8.52,17.50) mm.Site error is：
The untrivialo solution percentage of reconstructed results is 0.6%；Reconstruction time is 54.2s；It is 500 times to rebuild convergent iterations number of times；According to returning
One changes the definition of root-mean-square error (Normalized root mean square error, NRMSE)：X in formularAnd x (i)t(i) it is respectivelyiThe fluorescent yield rebuild on individual node
Value and true fluorescent yield value,WithFor the minimum and maximum fluorescent yield value of reconstruction.The normalization of reconstructed results is equal
Square error is 0.048；According to relative noise ratio (Contrast-to-noise ratio) definition：μ in formulaNoIAnd μNoBIt is fluorescent yield interest node (NoI) and fluorescent yield background section
The average of point (NoB), ωNoIAnd ωNoBIt is the weight coefficient of NoI and NoB in whole region,WithIt is standard deviation.Rebuild
As a result relative noise ratio is 14.89.
Based on the reconstruction of the present invention, its site error is small, and untrivialo solution number is few, and reconstruction time is short, and iterations is few, normalizing
Change mean square error small, relative noise, than big, is a kind of effective fluorescent molecule tomography rebuilding for being directed to finite projection angle
Algorithm.
The foregoing is merely illustrative of the preferred embodiments of the present invention, is not intended to limit the invention, all essences in the present invention
Any modifications, equivalent substitutions and improvements made within refreshing and principle etc., should be included in the scope of the protection.
Claims (6)
1. a kind of fluorescent molecule tomography rebuilding method based on half threshold value tracing algorithm, it is characterised in that described based on half
The fluorescent molecule tomography rebuilding method of threshold value tracing algorithm uses multi-point shooting, and limited angle measurement builds non-convex problem
Sparse canonical model, sets up the linear relationship that the measurement data on surface is distributed with fluorescent target, linear relationship is converted into 1/2 model
Number minimization problem is solved, and obtains the distributed in three dimensions and concentration for the fluorescent target for rebuilding target internal；By threshold value iteration and
With tracing algorithm to model solution；
The 1/2 least norm problem representation is：
<mrow>
<munder>
<mrow>
<mi>m</mi>
<mi>i</mi>
<mi>n</mi>
</mrow>
<mi>X</mi>
</munder>
<mo>{</mo>
<mo>|</mo>
<mo>|</mo>
<mi>A</mi>
<mi>X</mi>
<mo>-</mo>
<msub>
<mi>&Phi;</mi>
<mi>m</mi>
</msub>
<mo>|</mo>
<msubsup>
<mo>|</mo>
<mn>2</mn>
<mn>2</mn>
</msubsup>
<mo>+</mo>
<mi>&lambda;</mi>
<mo>|</mo>
<mo>|</mo>
<mi>X</mi>
<mo>|</mo>
<msub>
<mo>|</mo>
<mrow>
<mn>1</mn>
<mo>/</mo>
<mn>2</mn>
</mrow>
</msub>
<mo>}</mo>
<mo>;</mo>
</mrow>
Wherein, λ is regularization parameter, and A is sytem matrix, and X is the fluorescent target distributed in three dimensions and concentration to be solved, and luminous flux is close
Spend Φm。
2. the fluorescent molecule tomography rebuilding method as claimed in claim 1 based on half threshold value tracing algorithm, its feature exists
In the fluorescent molecule tomography rebuilding method based on half threshold value tracing algorithm comprises the following steps：
Step one, fluorescence measurement data are obtained；
Step 2, obtains the anatomical information and optical property parameter of reconstructed object；
Step 3, discrete acquisition network relevant information is carried out to imageable target using Amira softwares；
Step 4, based on optical transport model and finite element theory, will rebuild the anatomical information and optical property parameter of target
As prior information, linear relationship of the fluorescence data of surface limited angle with rebuilding the distribution of target internal fluorescent target is set up；
Step 5,1/2 least norm problem is converted into by linear relationship：
<mrow>
<munder>
<mrow>
<mi>m</mi>
<mi>i</mi>
<mi>n</mi>
</mrow>
<mi>X</mi>
</munder>
<mo>{</mo>
<mo>|</mo>
<mo>|</mo>
<mi>A</mi>
<mi>X</mi>
<mo>-</mo>
<msub>
<mi>&Phi;</mi>
<mi>m</mi>
</msub>
<mo>|</mo>
<msubsup>
<mo>|</mo>
<mn>2</mn>
<mn>2</mn>
</msubsup>
<mo>+</mo>
<mi>&lambda;</mi>
<mo>|</mo>
<mo>|</mo>
<mi>X</mi>
<mo>|</mo>
<msub>
<mo>|</mo>
<mrow>
<mn>1</mn>
<mo>/</mo>
<mn>2</mn>
</mrow>
</msub>
<mo>}</mo>
<mo>;</mo>
</mrow>
λ is regularization parameter；
Step 6, to model, is solved using half threshold value iterative technique, introduces half threshold operator as follows：
<mrow>
<msub>
<mi>R</mi>
<mrow>
<mi>&lambda;</mi>
<mo>,</mo>
<mn>1</mn>
<mo>/</mo>
<mn>2</mn>
</mrow>
</msub>
<mrow>
<mo>(</mo>
<mo>&CenterDot;</mo>
<mo>)</mo>
</mrow>
<mo>=</mo>
<msup>
<mrow>
<mo>(</mo>
<mi>I</mi>
<mo>+</mo>
<mfrac>
<mi>&lambda;</mi>
<mn>2</mn>
</mfrac>
<mo>&dtri;</mo>
<mo>(</mo>
<mrow>
<mo>|</mo>
<mo>|</mo>
<mo>&CenterDot;</mo>
<mo>|</mo>
<msub>
<mo>|</mo>
<mrow>
<mn>1</mn>
<mo>/</mo>
<mn>2</mn>
</mrow>
</msub>
</mrow>
<mo>)</mo>
<mo>)</mo>
</mrow>
<mrow>
<mo>-</mo>
<mn>1</mn>
</mrow>
</msup>
<mo>;</mo>
</mrow>
Then, half threshold value iterative technique is with being expressed as form：
<mrow>
<mi>X</mi>
<mo>=</mo>
<mfenced open = "{" close = "">
<mtable>
<mtr>
<mtd>
<mrow>
<msub>
<mi>R</mi>
<mrow>
<mi>&lambda;</mi>
<mo>,</mo>
<mn>1</mn>
<mo>/</mo>
<mn>2</mn>
</mrow>
</msub>
<mrow>
<mo>(</mo>
<mi>B</mi>
<mo>(</mo>
<mi>X</mi>
<mo>)</mo>
<mo>)</mo>
</mrow>
<mo>,</mo>
</mrow>
</mtd>
<mtd>
<mrow>
<mrow>
<mo>|</mo>
<msub>
<mrow>
<mo>&lsqb;</mo>
<mi>B</mi>
<mrow>
<mo>(</mo>
<mi>X</mi>
<mo>)</mo>
</mrow>
<mo>&rsqb;</mo>
</mrow>
<mi>i</mi>
</msub>
<mo>|</mo>
</mrow>
<mo>></mo>
<mfrac>
<mroot>
<mn>54</mn>
<mn>3</mn>
</mroot>
<mn>4</mn>
</mfrac>
<msup>
<mi>&lambda;</mi>
<mfrac>
<mn>2</mn>
<mn>3</mn>
</mfrac>
</msup>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<mn>0</mn>
<mo>,</mo>
</mrow>
</mtd>
<mtd>
<mrow>
<mi>o</mi>
<mi>h</mi>
<mi>e</mi>
<mi>r</mi>
<mi>w</mi>
<mi>s</mi>
<mi>e</mi>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
<mo>;</mo>
</mrow>
Wherein, B (X)=X+AT(Φ-AX)；
Step 7, in order to reduce the iterations in step 6, introduces method for tracing, makes B (X) ≈ X, then each iteration just like
Lower process：
<mrow>
<mtable>
<mtr>
<mtd>
<mrow>
<msup>
<mi>S</mi>
<mrow>
<mi>n</mi>
<mo>+</mo>
<mn>1</mn>
</mrow>
</msup>
<mo>=</mo>
<mi>sup</mi>
<mrow>
<mo>(</mo>
<msup>
<mi>u</mi>
<mrow>
<mi>n</mi>
<mo>+</mo>
<mn>1</mn>
<mo>,</mo>
<mn>1</mn>
</mrow>
</msup>
<mo>)</mo>
</mrow>
<mo>,</mo>
<msup>
<mi>u</mi>
<mrow>
<mi>n</mi>
<mo>+</mo>
<mn>1</mn>
<mo>,</mo>
<mn>1</mn>
</mrow>
</msup>
<mo>=</mo>
<mi>R</mi>
<mrow>
<mo>(</mo>
<mi>B</mi>
<mo>(</mo>
<msup>
<mi>X</mi>
<mi>n</mi>
</msup>
<mo>)</mo>
<mo>)</mo>
</mrow>
<mo>;</mo>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msup>
<mi>X</mi>
<mrow>
<mi>n</mi>
<mo>+</mo>
<mn>1</mn>
</mrow>
</msup>
<mo>=</mo>
<msup>
<mi>u</mi>
<mrow>
<mi>n</mi>
<mo>+</mo>
<mn>1</mn>
<mo>,</mo>
<mi>k</mi>
<mo>+</mo>
<mn>1</mn>
</mrow>
</msup>
<mo>,</mo>
<msup>
<mi>u</mi>
<mrow>
<mi>n</mi>
<mo>+</mo>
<mn>1</mn>
<mo>,</mo>
<mi>k</mi>
<mo>+</mo>
<mn>1</mn>
</mrow>
</msup>
<mo>=</mo>
<msub>
<mrow>
<mo>(</mo>
<msup>
<mi>u</mi>
<mrow>
<mi>n</mi>
<mo>+</mo>
<mn>1</mn>
<mo>,</mo>
<mi>k</mi>
</mrow>
</msup>
<mo>+</mo>
<msub>
<mi>t</mi>
<mrow>
<mi>n</mi>
<mo>+</mo>
<mn>1</mn>
<mo>,</mo>
<mi>k</mi>
</mrow>
</msub>
<msup>
<mi>A</mi>
<mi>T</mi>
</msup>
<mo>(</mo>
<mrow>
<mi>&Phi;</mi>
<mo>-</mo>
<msup>
<mi>Au</mi>
<mrow>
<mi>n</mi>
<mo>+</mo>
<mn>1</mn>
<mo>,</mo>
<mi>k</mi>
</mrow>
</msup>
</mrow>
<mo>)</mo>
<mo>)</mo>
</mrow>
<msup>
<mi>S</mi>
<mrow>
<mi>n</mi>
<mo>+</mo>
<mn>1</mn>
</mrow>
</msup>
</msub>
</mrow>
</mtd>
</mtr>
</mtable>
<mo>;</mo>
</mrow>
Step 8, by the iterative process in step 7, obtains the reconstructed results X of the n-th stepn, when | | Φ-AXn||/||Φ||≤
1e-05 or | | Xn-Xn-1| | during≤1e-08, stop iteration；
Step 9, the thinner grid of discrete acquisition size of mesh opening is carried out to imageable target using Amira softwares；
Step 10, shows result, and the anatomical structure of reconstructed results and imageable target is carried out into image co-registration, Tecplot softwares are used
Shown.
3. the fluorescent molecule tomography rebuilding method as claimed in claim 2 based on half threshold value tracing algorithm, its feature exists
In the step one is specifically included：
1) excitation source carries out the transmission-type fault imaging of limited angle to the reconstruction target being fixed in electronically controlled rotary table；Transmission
Formula fault imaging, laser and optical detecting instrument is placed on the both sides of imageable target, and laser irradiation reconstruction target excites glimmering
Optical target sends fluorescence, and fluorescence penetrates imageable target and detected by the optical detecting instrument on laser opposite；Limited angle is transmitted
Formula tomoscan, rotates one with computer control electronically controlled rotary table and is more than 90 ° at equal intervals；
2) measurement data is obtained using optical detecting instrument, obtains pharosage Φm。
4. the fluorescent molecule tomography rebuilding method as claimed in claim 2 based on half threshold value tracing algorithm, its feature exists
In the step 2 is specifically included：
A) anatomical information of reconstructed object
Three-dimensional reconstruction is carried out to computer tomography data for projection, and the three of acquisition imageable target are pre-processed with 3DMED softwares
Tie up volume data；Tissue segmentation is carried out to volume data using the semi-automatic dividing method of man-machine interactive in 3DMED softwares, obtained
The anatomical structure of imageable target；
B) optical property parameter is obtained
Using anatomical information and using the expansion based on region based on the specific optical 3-dimensional method for reconstructing of biological tissue
Astigmatism tomography algorithm obtains the optical property parameter of each tissue in imageable target.
5. the fluorescent molecule tomography rebuilding method as claimed in claim 2 based on half threshold value tracing algorithm, its feature exists
In the step 4 is specifically included：
(1) optical transport model, transmitting procedure of the light in imageable target is described using diffusion approximation equation；
(2) according to finite element theory, and the anatomical information and optical property parameter of converged reconstruction target, by diffusion approximation side
Journey is discrete, builds linear relationship of the measurement data on surface with rebuilding the distribution of target internal fluorescent target：
Φm=AX；
Wherein A is sytem matrix, and X is the fluorescent target distributed in three dimensions and concentration to be solved, and is non-negative.
6. the fluorescent molecular tomography weight based on half threshold value tracing algorithm described in a kind of application Claims 1 to 5 any one
The fluorescent molecular tomography system of construction method.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710448650.6A CN107220961A (en) | 2017-06-14 | 2017-06-14 | A kind of fluorescent molecule tomography rebuilding method based on half threshold value tracing algorithm |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710448650.6A CN107220961A (en) | 2017-06-14 | 2017-06-14 | A kind of fluorescent molecule tomography rebuilding method based on half threshold value tracing algorithm |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN107220961A true CN107220961A (en) | 2017-09-29 |
Family
ID=59948462
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710448650.6A Pending CN107220961A (en) | 2017-06-14 | 2017-06-14 | A kind of fluorescent molecule tomography rebuilding method based on half threshold value tracing algorithm |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107220961A (en) |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107713995A (en) * | 2017-11-15 | 2018-02-23 | 陕西师范大学 | A kind of bioluminescence fault imaging light source reconstruction method based on punishment algorithm |
| CN108095686A (en) * | 2017-11-06 | 2018-06-01 | 西北大学 | A kind of fluorescent molecular tomography target feasible zone choosing method |
| CN109146987A (en) * | 2018-06-15 | 2019-01-04 | 西北大学 | A kind of fast cone-beam computed tomography reconstruction method based on GPU |
| CN110429938A (en) * | 2019-06-25 | 2019-11-08 | 四川轻化工大学 | A kind of compression sampling and method for reconstructing of high speed nuclear signal |
| CN112037300A (en) * | 2020-08-21 | 2020-12-04 | 西北大学 | Optical reconstruction method and device based on alternative direction multiplier network |
| CN112699581A (en) * | 2020-12-26 | 2021-04-23 | 西北大学 | New target feasible region extraction method, system, medium, equipment and application |
| CN114521866A (en) * | 2021-12-31 | 2022-05-24 | 西北大学 | Bioluminescence tomography reconstruction method based on self-adaptive Newton hard threshold tracking method |
| CN115607108A (en) * | 2022-09-30 | 2023-01-17 | 西北大学 | Multi-target reconstruction method based on surface measurement signal blind source separation |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20120302880A1 (en) * | 2010-11-30 | 2012-11-29 | Institute Of Automation, Chinese Academy Of Sciences | System and method for specificity-based multimodality three- dimensional optical tomography imaging |
| CN103271723A (en) * | 2013-06-26 | 2013-09-04 | 西安电子科技大学 | Bioluminescence tomography reconstruction method |
| CN105455780A (en) * | 2015-11-17 | 2016-04-06 | 西北大学 | Fluorescent molecular tomography imaging reconstruction method based on limited projection of double grids |
| CN105791186A (en) * | 2016-04-28 | 2016-07-20 | 西安交通大学 | Sparse low rank channel combination estimation method in large scale MIMO system |
| CN106725347A (en) * | 2017-02-16 | 2017-05-31 | 西北大学 | Based on stochastic variable alternating direction multiplier method fluorescent molecule tomography rebuilding method |
-
2017
- 2017-06-14 CN CN201710448650.6A patent/CN107220961A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20120302880A1 (en) * | 2010-11-30 | 2012-11-29 | Institute Of Automation, Chinese Academy Of Sciences | System and method for specificity-based multimodality three- dimensional optical tomography imaging |
| CN103271723A (en) * | 2013-06-26 | 2013-09-04 | 西安电子科技大学 | Bioluminescence tomography reconstruction method |
| CN105455780A (en) * | 2015-11-17 | 2016-04-06 | 西北大学 | Fluorescent molecular tomography imaging reconstruction method based on limited projection of double grids |
| CN105791186A (en) * | 2016-04-28 | 2016-07-20 | 西安交通大学 | Sparse low rank channel combination estimation method in large scale MIMO system |
| CN106725347A (en) * | 2017-02-16 | 2017-05-31 | 西北大学 | Based on stochastic variable alternating direction multiplier method fluorescent molecule tomography rebuilding method |
Non-Patent Citations (4)
| Title |
|---|
| SHIXIN JIANG 等: "Novel l2,1-norm optimization method for fluorescence molecular tomography reconstruction", 《BIOMEDICAL OPTICS EXPRESS》 * |
| XUELI CHEN 等: "L1/2 regularization based numerical method for effective reconstruction of bioluminescence tomography", 《JOURNAL OF APPLIED PHYSICS》 * |
| ZONGBEN XU 等: "L1/2 Regularization: A Thresholding Representation Theory and a Fast Solver", 《IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS》 * |
| 郭红波 等: "基于非凸稀疏正则的荧光分子断层成像", 《光学学报》 * |
Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108095686A (en) * | 2017-11-06 | 2018-06-01 | 西北大学 | A kind of fluorescent molecular tomography target feasible zone choosing method |
| CN108095686B (en) * | 2017-11-06 | 2020-09-11 | 西北大学 | Fluorescence molecular tomography target feasible region selection method |
| CN107713995A (en) * | 2017-11-15 | 2018-02-23 | 陕西师范大学 | A kind of bioluminescence fault imaging light source reconstruction method based on punishment algorithm |
| CN109146987A (en) * | 2018-06-15 | 2019-01-04 | 西北大学 | A kind of fast cone-beam computed tomography reconstruction method based on GPU |
| CN110429938A (en) * | 2019-06-25 | 2019-11-08 | 四川轻化工大学 | A kind of compression sampling and method for reconstructing of high speed nuclear signal |
| CN110429938B (en) * | 2019-06-25 | 2022-07-26 | 四川轻化工大学 | Compressed sampling and reconstruction method of high-speed nuclear signal |
| CN112037300A (en) * | 2020-08-21 | 2020-12-04 | 西北大学 | Optical reconstruction method and device based on alternative direction multiplier network |
| CN112037300B (en) * | 2020-08-21 | 2023-08-01 | 西北大学 | Optical reconstruction method and device based on alternate direction multiplier subnetwork |
| CN112699581A (en) * | 2020-12-26 | 2021-04-23 | 西北大学 | New target feasible region extraction method, system, medium, equipment and application |
| CN114521866A (en) * | 2021-12-31 | 2022-05-24 | 西北大学 | Bioluminescence tomography reconstruction method based on self-adaptive Newton hard threshold tracking method |
| CN115607108A (en) * | 2022-09-30 | 2023-01-17 | 西北大学 | Multi-target reconstruction method based on surface measurement signal blind source separation |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107220961A (en) | A kind of fluorescent molecule tomography rebuilding method based on half threshold value tracing algorithm | |
| CN103239255B (en) | Cone-beam X-ray luminescence computed tomography method | |
| CN103271723B (en) | Bioluminescence tomography reconstruction method | |
| CN101342075B (en) | Multi-optical spectrum autofluorescence dislocation imaging reconstruction method based on single view | |
| CN105455780A (en) | Fluorescent molecular tomography imaging reconstruction method based on limited projection of double grids | |
| CN102334979B (en) | Bimodal fusion tomography method based on iterative shrinkage | |
| US20120302880A1 (en) | System and method for specificity-based multimodality three- dimensional optical tomography imaging | |
| CN107592935A (en) | The automatic system calibration method of X ray CT | |
| CN108451508B (en) | Biological autofluorescence three-dimensional imaging method based on multilayer perceptron | |
| CN107392977A (en) | Single-view Cherenkov lights tomography rebuilding method | |
| Liu et al. | Image reconstruction from limited angle projections collected by multisource interior x-ray imaging systems | |
| CN105825547A (en) | Optical three-dimensional imaging method based on voxel and adaptive optical transmission model | |
| CN103393410B (en) | Fluorescence molecular tomography reconstruction method based on alternative iterative operation | |
| CN106097441A (en) | Compound regularization Bioluminescence tomography reconstruction method based on L1 norm Yu TV norm | |
| US11776174B1 (en) | Fluorescence moleculartomography reconstruction method based on prior guidance of magnetic particle imaging | |
| Ma et al. | An encoder-decoder network for direct image reconstruction on sinograms of a long axial field of view PET | |
| Jiang et al. | 3D in vivo dose verification in prostate proton therapy with deep learning-based proton-acoustic imaging | |
| CN105374060A (en) | PET image reconstruction method based on structural dictionary constraint | |
| Wang et al. | A review of methods for solving the optical molecular tomography | |
| Sun et al. | An iterative gradient convolutional neural network and its application in endoscopic photoacoustic image formation from incomplete acoustic measurement | |
| CN108564636A (en) | A kind of fluorescent target reconstructed results post-processing approach based on three decisions | |
| CN113951835B (en) | Three-dimensional fluorescence microscopic imaging method based on optical fault reconstruction strategy | |
| CN101324529B (en) | Autofluorescence imaging method and system | |
| CN110292392A (en) | A kind of fluorescence CT sparse angular projection Gamma spectrum analysis method | |
| Chen et al. | Feasibility study of endoscopic X-ray luminescence computed tomography: simulation demonstration and phantom application |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20170929 |
|
| RJ01 | Rejection of invention patent application after publication |