CN107330968A - The three-dimensional DOT methods of OT guidings brain function half based on five layers of brain structural model - Google Patents
The three-dimensional DOT methods of OT guidings brain function half based on five layers of brain structural model Download PDFInfo
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Abstract
The invention discloses a kind of three-dimensional DOT methods of OT guidings brain function half based on five layers of brain structural model, it is main to include a kind of five layers of brain structural model of design, transmission of the photon in non-scatter region and scattering region is modeled respectively with radiation transfer equation and diffusion equation and they intercouple the transmission with accurate description photon in whole five layers of brain structural model.Using same group of measurement data, offer a priori location information is first rebuild roughly with OT, then DOT inverse problems are carried out regularization to improve its pathosis, improve the quality of reconstruction image.Assuming that the variable quantity of absorption coefficient in stratum not with change in depth, three-dimensional inverse problem is simplified to a two dimensional image Problems of Reconstruction, i.e. half three-dimensional image reconstruction.The present invention fully combines OT strong robustnesses and DOT spatial resolutions are high, quantitative strong advantage, simplifies measurement process and image reconstruction process, improves image quality.
Description
Technical field
The invention belongs near infrared tissue optical imaging field, and in particular to what five layers of brain feature optical modeling and OT were guided
Half three-dimensional DOT image rebuilding methods.
Background technology
Function near infrared spectrum (functional Near-infrared Spectroscopy, fNIRS) is a kind of dependence
The transportation law of light in biological tissues in infrared window (695-1000nm) is carried out into the variable quantity of absorption coefficient
The technology of picture.Because it has a series of advantages such as noninvasive, wearable, unionized, high time resolution, fNIRS is in healthy human brain work(
It is applied widely in energy and many pathological researchs[1,2,3].This technology from be initially based on amendment lambert Bill
The optical topology imaging technique (Optics Topology, OT) of law and the diffusion optical chromatography based on photon transport model into
As technology (DOT, Diffuse Optical Tomography) develops into a kind of novel optical image technology[4].Much grind
Study carefully group and illustrate fNIRS in positioning motion[5], it is cognitive[6], language[7], vision[8]Deng cortex excitable area, neuropathy is diagnosed
, pathology, psychological abnormality[9]With brain-computer interface technology of the development based on fNIRS[10]In terms of many applications.When brain is emerging
When putting forth energy, the haemodynamic effect at respective specific position can cause local parteriole vasodilation, cerebral blood flow (CBF) to increase in stratum
Plus, total hemoglobin concentration increase, oxyhemoglobin concentration increase and deoxy-hemoglobin concentrations reduce etc. effect.According to suction
Receive the linear relationship between the variable quantity and oxyhemoglobin and the variable quantity of deoxy-hemoglobin concentrations of coefficient, it is only necessary to know
The variable quantity of absorption coefficient of the road under two different wave lengths, with reference to oxyhemoglobin and deoxyhemoglobin in two wavelength
Under molar extinction coefficient be assured that oxyhemoglobin and deoxyhemoglobin concentration variable quantity.In addition, in view of
The variable quantity that brain excitement and head Cerebral blood flow change caused scattering coefficient is sufficiently small so that negligible reason,
Reconstruction of the variable quantity of absorption coefficient under two wavelength is pertained only in the present invention.
OT corrects variable quantity of the langbobier law to the absorption coefficient along tissue surface using mathematics strong robustness
Carry out the two-dimensional topology imaging that light basis weight, low spatial are differentiated.In contrast to this, DOT is using more accurate on physics meaning
Photon transport model the three-dimensional spatial distribution of the absorption coefficient variable quantity in organizer is rebuild.Thus, DOT is expected to pole
The earth improves the quantitative and spatial resolution of imaging.However, experiment shows that DOT imaging effect is largely dependent upon
Regularization to the pathosis of its inverse problem and effective combination to objective body a priori location information.Although in examining for many DOT
In disconnected application[11,12], prior information can pass through such as XCT (X-ray Computed Tomography) and MRI
(Magnetic Resonance Imaging) etc. ripe image mode obtains to improve image quality.However, in fNIRS brains
Multi-modal application is still rare in functional imaging and costliness, because different modalities are applied to different tasks and with not
Same temporal resolution, this causes difficulty to multi-modal fusion.
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The content of the invention
For above-mentioned prior art, the present invention provides a kind of three-dimensional DOT side of OT guidings half based on five layers of brain structural model
In method, the present invention, OT and DOT belongs to identical image mode, and both use identical measurement data, it is no longer necessary to extra survey
Amount.Brought uncertainty can not be measured simultaneously on same subject avoiding problems different image modes, it also avoid
Though different image modes can simultaneously measure on same subject but measure the different caused uncertainty of duration, Jin Erbao
The uniformity of data in time used in OT and DOT is demonstrate,proved.In addition, also no longer needing the fusion of two kinds of different modalities of progress or matching somebody with somebody
Standard, and then equipment cost is reduced, simplify imaging process.First, with OT rebuild a width low spatial resolution, light basis weight but
It is the two-dimensional distribution of robustness very strong reflection absorption coefficient variable quantity to provide a priori location information of objective body.Then,
Regularization is carried out to the pathosis of DOT inverse problems with reference to this priori location information, and then improves reflection absorption coefficient change spirogram
The quality of picture.In addition, it is contemplated that reflection detection mode limited penetration depth and absorption coefficient change in grey matter along depth
The constant reasonability in direction is spent, is substantially that three-dimensional DOT inverse problems are simplified into two dimensional image Problems of Reconstruction, i.e., one and half
Three-dimensional problem.In addition, in the present invention, devising a kind of five layers of brain structural model (scalp layer, skull layer, cerebrospinal fluid layer, stratum
And lamina albae) with the simulation more fine and true to nature to brain tissue progress.Wherein, it is contemplated that the non-scatter characteristic of cerebrospinal fluid layer,
Radiation transfer equation and diffusion equation are combined (ray-diffusion theory) to enter transmission of the photon in brain tissue by the present invention
The accurate simulation of row[13].The present invention fully combines OT strong robustnesses and DOT spatial resolutions are high, quantitative strong advantage, raising
Systematic function, simplifies measurement process and image reconstruction process, improves image quality, have light in brain function field of detecting
Bright application prospect.
In order to solve the above-mentioned technical problem, a kind of OT based on five layers of brain structural model proposed by the present invention guides brain function
Half three-dimensional DOT methods, comprise the following steps：
Step 1, the measuring system for building the overlapping sample mode of a use, including：
Five layers of brain structural model are designed, i.e.,：Space shared by five layers of brain structural model is in three-dimensional system of coordinate
In, X, Y and Z-direction are respectively 0-100mm, 0-120mm and 0-32mm；Five layers of brain structural model is by cloth successively from outer to inner
Scalp layer, skull layer, cerebrospinal fluid layer, stratum and the lamina albae put are constituted；On the scalp layer of five layers of brain structural model
Cloth is with 20 light source points, and it is in equidistantly latticed arrangement, and line by line from left-hand that 20 light source points are arranged with 20mm distances according to 4 rows 5
Light source point is numbered on the right side, light source point numbering i=1, and 2,3 ..., 20；One is arranged in the geometric center of every 4 adjacent light sources point
Individual detector, so as to form 12 sensing points on scalp layer；Each light source point is selected from 600~1000nm provided with 1 to 3
In the range of different wave length light source, wavelength is expressed as λj, j=1,2,3；
, will be with light source point distance respectively L for each light source point1=14.14mm, L2=31.62mm, L3=
42.40mm and L4The passage formed between=50.99mm all detectors and the light source point is defined as the first neighbour and led to successively
Road, second neighbour's passage, the 3rd neighbour's passage and the 4th neighbour's passage；Which includes 44 second neighbour's passages, all
The midpoint of second neighbour's passage is equipped with a sampled point；
Step 2, the optical transport model for five layers of brain structural model set up according to ray-diffusion theory：
Transmission of the photon in five layers of brain structural model is described using following partial differential equation and boundary condition,
In formula (1), (a) represents that photon needs the diffusion equation met when scattering region is transmitted；(b), (c) and (d) point
Boundary condition is not represented；R representation spaces position；μa(r) with μ 's(r) represent that absorption coefficient and reduction at r positions dissipate respectively
Penetrate coefficient；Represent diffusion coefficient；Φ (r) represents photon density；q0(r) light source is represented；Ω represents whole
Scattering region to be investigated, the scattering region to be investigated include five layers of brain structural model in scalp layer, skull layer,
Stratum and lamina albae；All borders of scattering region to be investigated are represented,WithFive layers of brain structure is represented respectively
The upper and lower interface of cerebrospinal fluid layer in model；Represent interfaceWithPoint to the normal vector in non-scatter region in place；
Step 3, by optical topology imaging guiding the three-dimensional diffusion optical tomography of brain function half, including：
3-1) it is imaged using optical topology and obtains the two-dimentional rough image that a width reflects absorption coefficient variable quantity：
Each light source point is irradiated with light source, is successively measured under quiescent condition and task status in all second neighbour passages
Detector position at exiting light beam intensity, and be denoted as respectivelyWithWherein, λjRepresent the wavelength that numbering is j；I represents i-th
The numbering of individual second neighbour passage；R and T represent quiescent condition and task status respectively；Calculate second near at i-th with formula (2)
The absorption coefficient variable quantity △ μ of adjacent channel sample pointa；
In formula (2), B represents the differential path factor, and l represents the length of i-th of second neighbour's passages；Then by their interpolation
Within five layers of brain structural model horizontal cross-section on finite element fission grid, so as to obtain width reflection absorption coefficient variable quantity
Two-dimentional rough imageN is represented within the horizontal cross-section of five layers of brain structural model
The number of finite element fission grid node；
3-2) optical topology imaging results are carried out with a priori location information that image segmentation obtains absorption coefficient variable quantity：
Using the 30% of absorption coefficient variable quantity maximum as threshold value, by above-mentioned two-dimentional rough imageMiddle absorption coefficient
The finite element fission grid node that variable quantity is more than the threshold value is entered as 1, and remaining node valuation is 0, obtains a width bianry image；
1 region representation area-of-interest is entered as, ROI is denoted as, 0 region representation regions of non-interest is entered as, is denoted as non-
ROI；
3-3) three-dimensional diffusion optical tomography is rebuild and is simplified to the reconstruction of half three-dimensional diffusion optical tomography：
Three-dimensional diffusion optical tomography inverse problem is simplified to by two dimensional image Problems of Reconstruction by formula (3)
In formula (3), ξd(d=1 ... D, D=12) and ξs(s=1 ... S, S=20) represents upper 12 sensing point of scalp layer respectively
Position and 20 light source point positions；IT(ξd,ζs) and IR(ξd,ζs) be illustrated respectively under task status and quiescent condition in ξdVisit at place
Obtained by survey by ξsLocate the exiting light beam intensity caused by light source excitation；I(ξd,ζs) represent under quiescent condition according to formula (1) direct die
Light source of working as obtained by plan is in ξs, in ξdThe exiting light beam intensity that place's measurement is obtained；G(ξd, r) with Φ (r, ζs) represent to work as light respectively
Source is placed on ξdAnd ζsDuring place, the spatial distribution of corresponding photon density；△μag(x, y) represents that (x, y) place absorbs in stratum
The variable quantity of coefficient；Zg represents scope of the stratum in Z- direction of principal axis；RxyRepresent projection of the grey matter area in X-Y plane；Spread light
Learn tomographic image reconstruction problem and be ultimately expressed as following matrix equality
M=W Δs μa (4)
In formula (4),ΔμaRepresent to absorb in stratum
The Two dimensional Distribution of index variation amount, W is that boundary light changes in flow rate amount becomes relative to each finite element fission grid node absorption coefficient
The weight matrix of change amount, its element is as follows：
In formula (5), s, d and n represent the sequence number of light source, detector and finite element fission grid node respectively；
A priori location information 3-4) is introduced in the reconstruction of half three-dimensional diffusion optical tomography, it is final to obtain reflection stratum
The two dimensional image of interior position absorption coefficient variable quantity：
Using soft priori Regularization Strategy by formula (6) by step 3-2) a priori location information that obtains is incorporated into diffusion light
In the solution for learning tomography inverse problem
In formula (6), L is to rely on ROI Laplace operator type regularization matrix
In formula (7), NbIt is the number of ROI (b=1) and non-ROI (b=0) interior pixel；α is basis
Tikhonov regularization parameter obtained by selection, wherein, { γi| i=1 ... N }, { μi| i=1 ... N } it is the strange of matrix W and L respectively
Different value；Optimization problem shown in formula (6) is reduced to following matrix equality
The matrix equation solved using algebraic reconstruction technique shown in formula (8) be can obtain high-space resolution, it is high quantitative
Reflect the two dimensional image of position absorption coefficient variable quantity in stratum.
Compared with prior art, the beneficial effects of the invention are as follows：
1. the data for OT and DOT reconstructions are from the data obtained by same one-shot measurement, it is no longer necessary to the survey of other mode
Data are measured, other measurement is also no longer needed, substantially reduces time of measuring, reduce equipment cost, improve the property of equipment
Valency ratio.
2. avoiding different image modes can not be on same subject while the brought uncertainty of measurement, is also avoided
Though different image modes can measure simultaneously on same receptor measure duration it is different caused by uncertainty, Jin Erbao
The uniformity of data in time used in OT and DOT is demonstrate,proved.
3. by the introducing of a priori location information, the spatial resolution of reconstruction image is improved, algorithm for reconstructing is improved
Robustness.
4., can be by the letter of three-dimensional inverse problem by assuming that consistency of the absorption coefficient variable quantity along depth direction in stratum
Turning to half three-dimensional inverse problem reduces the number of parameter to be reconstructed, improves the less qualitative of inverse problem.
Brief description of the drawings
Fig. 1 is that the cloth of light source and detector in measuring system in the present invention matches somebody with somebody schematic diagram.
Fig. 2 is the five layers of brain structural model schematic diagram built in the present invention.
Fig. 3 be in the present invention OT rebuild obtained by reflection absorption coefficient variable quantity two-dimentional rough image.
Fig. 4 be OT is rebuild in the present invention obtained by the two-dimentional rough image of reflection absorption coefficient variable quantity carry out image point
The result cut.
Fig. 5 is the image that the reflection reflection absorption coefficient variable quantity that OT prior informations are obtained is combined in the present invention.
Specific embodiment
Technical solution of the present invention is described in further detail with specific embodiment below in conjunction with the accompanying drawings, described is specific
Only the present invention is explained for embodiment, is not intended to limit the invention.
The present invention mentality of designing be：(1) a kind of five layers of brain structural model are designed, with radiation transfer equation and diffusion equation
Transmission of the photon in non-scatter region and scattering region is modeled respectively and they intercouple to (ray-diffusion is managed
By) transmission with accurate description photon in whole five layers of brain structural model.(2) it is first thick with OT using same group of measurement data
Slightly rebuild and a priori location information is provided, then DOT inverse problems are carried out regularization to improve its pathosis, improve the matter of reconstruction image
Amount.(3) three-dimensional inverse problem is simplified to an X-Y scheme by the variable quantity of hypothesis absorption coefficient not with change in depth in stratum
As Problems of Reconstruction, i.e. half three-dimensional image reconstruction.Particular content is as follows：
Step 1, the measuring system for building the overlapping sample mode of a use：
First, five layers of brain structural model are designed, the space shared by five layers of brain structural model is in three-dimensional coordinate
In system, X, Y and Z-direction are respectively 0-100mm, 0-120mm and 0-32mm；Fig. 2 shows the layering of five layers of brain structural model,
Including the scalp layer being sequentially arranged from outer to inner, skull layer, cerebrospinal fluid layer, stratum and lamina albae；Set the thickness of each layer according to
Secondary is respectively 4mm, 8mm, 2mm, 8mm and 10mm.Optical parametric of each layer under 660 and 830nm wavelength is as shown in table 1.
Table 1：The optical parametric of different brain tissue layers at different wavelengths
Fig. 1 shows distribution of the cloth of the source-detector on scalp layer with schematic diagram and sampled point, and setting is entirely treated
Investigate brain and imitate cuboid of the volume of body for 120mm*100mm*32mm.Cloth matches somebody with somebody 20 on the scalp layer of five layers of brain structural model
Individual light source point (shown in the circle mark in Fig. 1) and 12 detectors (shown in the square mark in Fig. 1), 20 light source points be according to
It is in equidistantly latticed arrangement, and light source point is numbered from left to right line by line, light source point numbering i that 4 rows 5, which are arranged with 20mm distances,
=1,2,3 ..., 20；The arrangement of 12 detectors is geometric center one detector of arrangement in every 4 adjacent light sources point, from
And form 12 sensing points on scalp layer；Each light source point is selected from the range of 600~1000nm not provided with 1 to 3
The light source of co-wavelength, wavelength is expressed as λj, j=1,2,3.In the present invention, it is assumed that every a pair of light source points and detector are formed
One passage, according to the distance between light source and detector by these passages be divided into first neighbour's passage, second neighbour's passage,
3rd neighbour's passage and the 4th neighbour's passage.Different neighbour's passages reflect organizer's information of different depth.That is, for each
Light source point, will be with light source point distance respectively L1=14.14mm, L2=31.62mm, L3=42.40mm and L4=50.99mm
All detectors and the light source point between the passage that is formed be defined as first neighbour's passage, second neighbour's passage, the 3rd successively
Neighbour's passage and the 4th neighbour's passage；Which includes 44 second neighbour's passages, in the midpoint of all second neighbour passages
The triangular marker for the midpoint being equipped with a sampled point, such as Fig. 1 between adjacent light source and detector is neighbouring logical where being
The sampled point in road.
Step 2, the optical transport model for five layers of brain structural model set up according to ray-diffusion theory:
In each layer of brain tissue, cerebrospinal fluid layer does not meet the applicable bar of diffusion equation because belonging to nonscattering medium only
Part, but remaining each layer is satisfied by the applicable elements of diffusion equation.Pin is in this regard, use radiation transfer equation and diffused sheet in the present invention
Journey is modeled to transmission of the photon in non-scatter region and scattering region respectively, and is directed to scattering-non-scatter by introducing
They are coupled (ray-diffusion theory) with accurate description photon in whole five layers of brain structural model by the boundary condition of groud surface
In transmission.Therefore, using the partial differential equation of the combination boundary condition as shown in formula (1) to photon in five layers of brain structural model
In transmission accurately described.
In formula (1), (a) represents that photon needs the diffusion equation met when scattering region is transmitted；(b), (c) and (d) point
Boundary condition is not represented；R representation spaces position；μa(r) with μ 's(r) represent that absorption coefficient and reduction at r positions dissipate respectively
Penetrate coefficient；Represent diffusion coefficient；Φ (r) represents photon density；q0(r) light source is represented；Ω represents whole
Scattering region to be investigated, the scattering region to be investigated include five layers of brain structural model in scalp layer, skull layer,
Stratum and lamina albae；All borders of scattering region to be investigated are represented,WithFive layers of brain structure is represented respectively
The upper and lower interface of cerebrospinal fluid layer in model；Represent interfaceWithPoint to the normal vector in non-scatter region in place.To this partial differential
Equation, can carry out discretization solution to domain using FInite Element, accurately try to achieve the spatial distribution of photon density.
Step 3, by the optical topology imaging guiding three-dimensional diffusion optical tomography of brain function half, basic step is as follows：
3-1) it is imaged using optical topology and obtains the two-dimentional rough image that a width reflects absorption coefficient variable quantity：
Each light source point is irradiated with light source, is successively measured under quiescent condition and task status in all second neighbour passages
Detector position at exiting light beam intensity, and be denoted as respectivelyWithWherein, λjRepresent the wavelength that numbering is j；I is represented i-th
The numbering of second neighbour's passage；R and T represent tranquillization (Rest) state and task (Task) state respectively；Calculated with formula (2) the
The absorption coefficient variable quantity △ μ of i second neighbour's channel sample pointsa；
In formula (2), B represents the differential path factor, and l represents the length of i-th of second neighbour's passages；Then by their interpolation
On finite element fission grid, absorbed within five layers of brain structural model horizontal cross-section so as to obtain the reflection of a width as shown in Figure 3
The two-dimentional rough image of index variation amountN represents five layers of brain structural model
The number of finite element fission grid node within horizontal cross-section；
3-2) optical topology imaging results are carried out with a priori location information that image segmentation obtains absorption coefficient variable quantity：
As shown in figure 4, suitable threshold value is chosen, will be above-mentioned using the 30% of absorption coefficient variable quantity maximum as threshold value
Two-dimentional rough imageThe finite element fission grid node that middle absorption coefficient variable quantity is more than the threshold value is entered as 1, remaining section
Point is entered as 0, obtains a width bianry image.It is entered as 1 region representation area-of-interest (ROI, Region Of
Interest), it is entered as 0 region representation regions of non-interest (non-ROI, non-Region Of No Interest).
3-3) three-dimensional diffusion optical tomography is rebuild and is simplified to the reconstruction of half three-dimensional diffusion optical tomography：
Three-dimensional diffusion optical tomography inverse problem is simplified to by two dimensional image Problems of Reconstruction by formula (3)
In formula (3), ξd(d=1 ... D, D=12) and ξs(s=1 ... S, S=20) represents upper 12 sensing point of scalp layer respectively
Position and 20 light source point positions；IT(ξd,ζs) and IR(ξd,ζs) be illustrated respectively under task status and quiescent condition in ξdVisit at place
Obtained by survey by ξsLocate the exiting light beam intensity caused by light source excitation；I(ξd,ζs) represent under quiescent condition according to formula (1) direct die
Light source of working as obtained by plan is in ξs, in ξdThe exiting light beam intensity that place's measurement is obtained；G(ξd, r) with Φ (r, ζs) represent to work as light respectively
Source is placed on ξdAnd ζsDuring place, the spatial distribution of corresponding photon density；Δμag(x, y) represents that (x, y) place absorbs in stratum
The variable quantity of coefficient；Zg represents scope of the stratum in Z- direction of principal axis；RxyRepresent projection of the grey matter area in X-Y plane；DOT images
Problems of Reconstruction may finally be expressed as matrix equality
M=W Δs μa (4)
In formula (4),ΔμaRepresent to absorb in stratum
The Two dimensional Distribution of index variation amount, W is that boundary light changes in flow rate amount becomes relative to each finite element fission grid node absorption coefficient
The weight matrix of change amount, its element is as follows：
In formula (5), s, d and n represent the sequence number of light source, detector and finite element fission grid node respectively；
A priori location information 3-4) is introduced in the reconstruction of half three-dimensional diffusion optical tomography, it is final to obtain reflection stratum
The two dimensional image of interior position absorption coefficient variable quantity：
In order to effectively using OT prior information, using soft priori Regularization Strategy by formula (6) by step 3-2) obtain
A priori location information be incorporated into diffusion optical tomography inverse problem solution in
In formula (6), L is to rely on ROI Laplace operator type regularization matrix
In formula (7), NbIt is the number of ROI (b=1) and non-ROI (b=0) interior pixel；α is basis
Tikhonov regularization parameter obtained by selection, wherein, { γi| i=1 ... N }, { μi| i=1 ... N } it is the strange of matrix W and L respectively
Different value；Optimization problem shown in formula (6) is reduced to following matrix equality
The matrix equation solved using algebraic reconstruction technique shown in formula (8) be can obtain high-space resolution as shown in Figure 5,
The two dimensional image of position absorption coefficient variable quantity (spatial distribution) in high quantitative reflection stratum.
Although above in conjunction with accompanying drawing, invention has been described, and the invention is not limited in above-mentioned specific implementation
Mode, above-mentioned embodiment is only schematical, rather than restricted, and one of ordinary skill in the art is at this
Under the enlightenment of invention, without deviating from the spirit of the invention, many variations can also be made, these belong to the present invention's
Within protection.
Claims (1)
1. a kind of three-dimensional DOT methods of OT guidings brain function half based on five layers of brain structural model, it comprises the following steps：
Step 1, the measuring system for building the overlapping sample mode of a use, including：
Five layers of brain structural model are designed, i.e.,：Space shared by five layers of brain structural model be in three-dimensional system of coordinate, X,
Y and Z-direction are respectively 0-100mm, 0-120mm and 0-32mm；Five layers of brain structural model by being sequentially arranged from outer to inner
Scalp layer, skull layer, cerebrospinal fluid layer, stratum and lamina albae are constituted；Cloth is matched somebody with somebody on the scalp layer of five layers of brain structural model
20 light source points, it is in equidistantly latticed arrangement that 20 light source points are arranged with 20mm distances according to 4 rows 5, and right from left to right line by line
Light source point is numbered, light source point numbering i=1, and 2,3 ..., 20；A spy is arranged in the geometric center of every 4 adjacent light sources point
Device is surveyed, so as to form 12 sensing points on scalp layer；Each light source point is selected from 600~1000nm scopes provided with 1 to 3
The light source of interior different wave length, wavelength is expressed as λj, j=1,2,3；
, will be with light source point distance respectively L for each light source point1=14.14mm, L2=31.62mm, L3=42.40mm and
L4The passage formed between=50.99mm all detectors and the light source point is defined as first neighbour's passage successively, second near
Adjacent passage, the 3rd neighbour's passage and the 4th neighbour's passage；It is logical in all second neighbours which includes 44 second neighbour's passages
The midpoint in road is equipped with a sampled point；
Step 2, the optical transport model for five layers of brain structural model set up according to ray-diffusion theory：
Transmission of the photon in five layers of brain structural model is described using following partial differential equation and boundary condition,
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In formula (1), (a) represents the diffused sheet for needing to meet when photon is transmitted in scalp layer, skull layer, stratum and lamina albae
Journey；(b), (c) and (d) represents boundary condition respectively；R representation spaces position；μa(r) with μ 's(r) represent respectively at r positions
Absorption coefficient and reduced scattering coefficient；Represent the diffusion coefficient and photon at r positions respectively with Φ (r)
Density；q0(r) light source is represented；Ω represents whole scattering region to be investigated, including the scalp in five layers of brain structural model
Layer, skull layer, stratum and lamina albae；All borders of scattering region to be investigated are represented,WithRepresent respectively described
The upper and lower interface of cerebrospinal fluid layer in five layers of brain structural model；Represent interfaceWithPoint to the normal vector in non-scatter region in place；
Step 3, by optical topology imaging guiding the three-dimensional diffusion optical tomography of brain function half, including：
3-1) it is imaged using optical topology and obtains the two-dimentional rough image that a width reflects absorption coefficient variable quantity：
Each light source point is irradiated with light source, the spy in all second neighbour passages is successively measured under quiescent condition and task status
Exiting light beam intensity at device position is surveyed, and is denoted as respectivelyWithWherein, λjRepresent the wavelength that numbering is j；I is represented i-th
The numbering of two neighbour's passages；R and T represent quiescent condition and task status respectively；Calculated and led in i-th of second neighbours with formula (2)
The absorption coefficient variable quantity △ μ of road sampled pointa；
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Within layer brain structural model horizontal cross-section on finite element fission grid, so as to obtain the two dimension that a width reflects absorption coefficient variable quantity
Rough imageN represents limited within the horizontal cross-section of five layers of brain structural model
The number of first subdivision grid node；
3-2) optical topology imaging results are carried out with a priori location information that image segmentation obtains absorption coefficient variable quantity：
Using the 30% of absorption coefficient variable quantity maximum as threshold value, by above-mentioned two-dimentional rough imageMiddle absorption coefficient change
Amount is entered as 1 more than the finite element fission grid node of the threshold value, and remaining node valuation is 0, obtains a width bianry image；Assignment
For 1 region representation area-of-interest, ROI is denoted as, 0 region representation regions of non-interest is entered as, is denoted as non-ROI；
Three-dimensional diffusion optical tomography 3-3) is simplified to half three-dimensional diffusion optical tomography：
Three-dimensional diffusion optical tomography inverse problem is simplified to by two dimensional image Problems of Reconstruction by formula (3)
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In formula (3), ξd(d=1 ... D, D=12) and ξs(s=1 ... S, S=20) represents the upper 12 sensing point position of scalp layer respectively
With 20 light source point positions；IT(ξd,ζs) and IR(ξd,ζs) be illustrated respectively under task status and quiescent condition in ξdDetection institute of place
By ξsLocate the exiting light beam intensity caused by light source excitation；I(ξd,ζs) represent under quiescent condition according to formula (1) forward direction simulation institute
The light source of working as obtained is in ξs, in ξdThe exiting light beam intensity that place's measurement is obtained；G(ξd, r) with Φ (r, ζs) represent respectively when light source is put
In ξdAnd ζsDuring place, the spatial distribution of corresponding photon density；△μag(x, y) represents (x, y) place absorption coefficient in stratum
Variable quantity；Zg represents scope of the stratum in Z- direction of principal axis；RxyRepresent projection of the grey matter area in X-Y plane；Spread optical layer
Analysis image Problems of Reconstruction is ultimately expressed as following matrix equality
M=W △ μa (4)
In formula (4),△μaRepresent the absorption coefficient in stratum
The Two dimensional Distribution of variable quantity, W is boundary light changes in flow rate amount relative to each finite element fission grid node absorption coefficient variable quantity
Weight matrix, its element is as follows：
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In formula (5), s, d and n represent the sequence number of light source, detector and finite element fission grid node respectively；
A priori location information 3-4) is introduced in half three-dimensional diffusion optical tomography, it is final to obtain each position in reflection stratum
Locate the two dimensional image of absorption coefficient variable quantity：
Using soft priori Regularization Strategy by formula (6) by step 3-2) a priori location information that obtains is incorporated into diffusion optical layer
In the solution of analysis imaging inverse problem
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In formula (7), NbIt is the number of ROI (b=1) and non-ROI (b=0) interior pixel；α is basisSelection
The Tikhonov regularization parameter of gained, wherein, { γi| i=1 ... N }, { μi| i=1 ... N } it is the unusual of matrix W and L respectively
Value；Optimization problem shown in formula (6) is reduced to following matrix equality
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</mtr>
<mtr>
<mtd>
<msqrt>
<mi>&alpha;</mi>
</msqrt>
<mi>L</mi>
</mtd>
</mtr>
</mtable>
</mfenced>
<msub>
<mi>&Delta;&mu;</mi>
<mi>a</mi>
</msub>
<mo>=</mo>
<mfenced open = "[" close = "]">
<mtable>
<mtr>
<mtd>
<mi>M</mi>
</mtd>
</mtr>
<mtr>
<mtd>
<mn>0</mn>
</mtd>
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<mo>-</mo>
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<mo>(</mo>
<mn>8</mn>
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</mrow>
</mrow>
The matrix equation solved using algebraic reconstruction technique shown in formula (8) is that can obtain high-space resolution, high quantitative reflection
The two dimensional image of position absorption coefficient variable quantity in stratum.
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| CN109568795A (en) * | 2018-11-23 | 2019-04-05 | 中国医学科学院生物医学工程研究所 | It is traced to the source and linearly related brain deep Neural stem cell target point positioning method based on brain electricity |
| CN110215187A (en) * | 2019-03-08 | 2019-09-10 | 天津大学 | A method of oxygen-containing hemoglobin concentration is calculated based on partial photonic path length |
| CN113274010A (en) * | 2021-04-26 | 2021-08-20 | 山西医科大学第一医院 | Near-infrared brain function imager 3D analysis system based on HCT |
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN109568795A (en) * | 2018-11-23 | 2019-04-05 | 中国医学科学院生物医学工程研究所 | It is traced to the source and linearly related brain deep Neural stem cell target point positioning method based on brain electricity |
| CN109568795B (en) * | 2018-11-23 | 2023-01-24 | 中国医学科学院生物医学工程研究所 | Brain deep magnetic stimulation target positioning method based on electroencephalogram tracing and linear correlation |
| CN110215187A (en) * | 2019-03-08 | 2019-09-10 | 天津大学 | A method of oxygen-containing hemoglobin concentration is calculated based on partial photonic path length |
| CN110215187B (en) * | 2019-03-08 | 2021-12-07 | 天津大学 | Method for calculating concentration of oxygenated hemoglobin based on partial photon path length |
| CN113274010A (en) * | 2021-04-26 | 2021-08-20 | 山西医科大学第一医院 | Near-infrared brain function imager 3D analysis system based on HCT |
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