CN110301908A - A kind of blood flow velocity monitoring method for contrasting algorithm based on micro- blood flow imaging - Google Patents
A kind of blood flow velocity monitoring method for contrasting algorithm based on micro- blood flow imaging Download PDFInfo
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Abstract
The invention discloses a kind of blood flow velocity monitoring method for contrasting algorithm based on micro- blood flow imaging, include the following steps: that (1) acquires original laser speckle image；(2) K value is contrasted to original laser speckle image linear operator calculating Conventional temporal and obtains the direction for contrasting K value in the smallest space；(3) spin matrix that Gaussian template is obtained further according to direction, obtains corresponding Gaussian convolution template；(4) anisotropy K value is obtained with original laser speckle image convolution, extracts capilary region, obtain capilary flow velocity figure using the direction specificity of brain blood flow.Preferable blood flow information can be presented for capilary when laser power is not strong in the present invention；When using identical time for exposure, gain and acquisition frame number identical with frequency, brain blood flow flow velocity is presented when being able to maintain more visible vessel information；Microvascular blood flow flow velocity can be indicated more accurately.
Description
Technical field
The present invention relates to the monitoring method of blood flow velocity, in particular to a kind of blood flow for contrasting algorithm based on micro- blood flow imaging
Speed monitoring method.
Background technique
Blood flow velocity is one of very important functional metabolism parameter during vital movement.Dynamic monitoring blood flow velocity
For studying biological tissue physiology change procedure, functional activity and evaluating drug effect etc. are of great significance change in time and space.
At present in blood flow laser speckle imaging algorithm, be typically based on space or time convolution algorithm analysis, be all it is each to
Colleague's algorithm, never considered the particularity of blood flow imaging, and can not accurately represent microvascular blood flow flow velocity.Also without special
The micro- blood flow imaging of anisotropy laser speckle based on Gaussian kernel of door contrasts algorithm, the monitoring for blood flow velocity.
Summary of the invention
Goal of the invention: it is an object of the present invention to provide can accurately indicate microvascular blood flow flow velocity based on micro- blood flow imaging
Contrast the blood flow velocity monitoring method of algorithm.
Technical solution: the present invention provides a kind of blood flow velocity monitoring method for contrasting algorithm based on micro- blood flow imaging, including
Following steps:
(1) acquire original laser speckle image, original speckle image refer to laser irradiation at toy brain surface,
CCD camera can receive the granular pattern of intensity random distribution, i.e. Fig. 4 (A) and Fig. 3 (A)；
(2) is contrasted by K value and obtains the smallest space for original laser speckle image linear operator calculating Conventional temporal and served as a contrast
Than the direction of K value；
(3) spin matrix that Gaussian template is obtained further according to direction, obtains corresponding Gaussian convolution template；
(4) anisotropy K value is obtained with original laser speckle image convolution, extracts capilary region, utilizes brain blood flow
Direction specificity obtains capilary flow velocity figure.
Further, original laser speckle image acquisition methods in the step (1) are as follows: build laser speckle optical path system
System, laser beam is radiated on testee, obtains original laser speckle image.
The laser speckle light path system includes HeNe laser, collimating mirror, beam expanding lens, reflective mirror, stereoscope,
CCD camera, computer.Laser forms Homogeneous Circular hot spot via collimating mirror, beam expanding lens, reflective mirror and gets to sample surface, then passes through
Intake CCD camera is finally imaged onto software interface via inventive algorithm after stereoscope amplification.
Further, K value is calculated using time convolution method in the step (2), wherein 30 frame of optional picture, calculates
Formula is as follows:
In above formula, σ is original image standard deviation, and<I>is the average value of original image.
Further, direction calculating the formula such as following formula and Fig. 5 of K value are contrasted in the smallest space of the step (2):
In above formula, KP (i, j)It indicates along target pixel points P (i0, j0) along the time convolution under linear operator direction calculating
K value, P (i, j) refer to P (i in image0, j0) centered on pixel on the different linear directions put, δ refer on linear direction away from
P(i0, j0) distance, value refers to the cumulative of time convolution K value less than 5, D (i, j) here, and more different D (i, j) obtains K
It is worth optimal direction.
Further, Gaussian template matches blood-vessel image in the step (3), calculates blood flow stream using gaussian kernel function
Speed, gaussian kernel function formula are as follows:
In above formula, Gaussian function about origin [0,0] symmetrically, Wi(xi, yi) refer to Gaussian kernel value in Gaussian template, what σ referred to
It is vessel cross-sections gray scale, considers speckle image discreteness, value is 1 in fixed range, and non-stationary ranges do not calculate, and L is
Along the regular length in blood vessel segmentation direction, L increases, and the smooth effect enhancing of blood vessel, noise reduces, but for toy brain
Portion's capillary capilary, blood vessel only may meet direction in a very short segment length, therefore select biggish value result is simultaneously instead
It is bad.xi, yiThe coordinate value of each point respectively in Gaussian template.
Further, the spin matrix r of step (3) Gaussian templateiSuch as following formula is calculated, here by first direction definition
For y-axis, thereafter along rotating clockwise；
In above formula, θiIt is the direction of rotation of Gaussian kernel.According to the minimum direction being calculated in formula (2), θ can be determinedi
Direction, then by spin matrix in conjunction with the weight of Gaussian kernel, seeking K value with original image convolution.
Further, the weighted value of the Gaussian kernel value calculates such as following formula:
In above formula,It refers to that the Gaussian kernel window of no weighted value, σ refer to vessel cross-sections gray scale, considers to dissipate
Spot image particularity, value is 1, L for along the regular length in blood vessel segmentation direction, x in fixed rangei, yiRespectively Gauss
The coordinate value of each point in template.
Further, the Gaussian kernel value after the weighting calculates such as following formula, and it is mark which, which is based on laser speckle value,
The ratio of quasi- difference and mean value convolution window, and balance weighted value:
W′i(xi, yi)=Wi(xi, yi)·mi(xi, yi) (6)
Wi(xi, yi) refer to Gaussian kernel value in Gaussian template, mi(xi, yi) refer to Gaussian template core weighted value, will determine
Have the right Gaussian kernel template and the original image convolution in good direction, obtain the capilary flow velocity figure finally needed.
Due to having many advantages, such as that, without fluorescein stain, fast imaging, laser speckle imaging technology is highly suitable for microcirculation
The measurement of blood flow.Blood vessels caliber can be measured using laser speckle technique, vessel density, velocity of blood flow and blood perfusion amount etc. are micro-
Loop parameter can be used to study the rheology of blood, lymph and tissue fluid in conjunction with the physiology monitorings instrument such as blood pressure, vim and vigour
Characteristic.By investigating the structure of microcirculatory vascular, microcirculation function and metabolic activity, can study inflammation, oedema, bleeding,
The rule of microcirculation change and its pathology machine during the basic pathologies such as allergy, shock, tumour, burn, frostbite, radiation insult
System.It is combined with specificity of the anisotropy to vessel directions, the contrast between blood vessel and background can be improved, it is especially right
It is imaged in toy brain capillary capilary, has and significantly improve effect, and blood vessel has the edge gray scale of belt-like zone gradually
Become characteristic, using dynamic weighting Gaussian kernel, preferably convolution can go out shape of blood vessel information, more accurate can represent brain
Portion's microvascular blood flow flow velocity.
The utility model has the advantages that the present invention is compared to isotropic laser speckle algorithm, when laser power is not strong for micro- blood
Preferable blood flow information can be presented in pipe；When using identical time for exposure, gain and acquisition frame number identical with frequency, it is able to maintain
Brain blood flow flow velocity is presented when more visible vessel information；The present invention is for the inhibition of diffusivity cortex or cerebral microvascular bleeding etc.
Brain blood flow disease has preferable imaging effect, compared to the method for space-time convolution, spatial discrimination can be improved and reduce noise；
Microvascular blood flow flow velocity can be indicated more accurately.
Detailed description of the invention
Fig. 1 is flow chart of the invention；
Fig. 2 is laser speckle system light path figure of the present invention；
Fig. 3 is the simulation results figure of invention；
Fig. 4 is that the present invention is applied to mouse brain vascular effects figure；
Fig. 5 is that the direction calculating of K value is contrasted as a result, wherein figure A linear operator is 15 degree of 12 directions in the smallest space,
15 degree of B of figure, 30 degree, 60 degree, the approximate template schematic diagram on 75 degree of linear directions；
Fig. 6 is image pixel between 1024-2048,5 × 5 bulk schematic diagram.
Specific embodiment
As shown in Figure 1, obtaining original speckle image first, Conventional temporal then is calculated using linear operator and contrasts K value simultaneously
Obtain the direction θ that K value is contrasted in the smallest spacei；The spin matrix of Gaussian template is obtained further according to direction calculating, is obtained corresponding
Gaussian convolution template and Gauss Weighted Kernel；Anisotropy K value is obtained with original image convolution, capilary region is extracted, utilizes brain
What the direction specificity of blood flow obtained micro- blood flow contrasts value.
K value is calculated using time convolution method, wherein 30 frame of optional picture, K value calculation such as following formula:
In above formula, σ is original image standard deviation, and<I>is the average value of original image.
The K value of different linear directions is calculated, Linear Operators are rotated with 15 °, share 12 directions, calculate the smallest K
Value.Such as following formula and Fig. 5:
In above formula, KP (i, j)It indicates along target pixel points P (i0, j0) along the time convolution under linear operator direction calculating
K value, P (i, j) refer to P (i in image0, j0) centered on pixel on the different linear directions put, δ refer on linear direction away from
P(i0, j0) distance, value refers to the cumulative of time convolution K value less than 5, D (i, j) here, and more different D (i, j) obtains K
It is worth optimal direction.
Usually in medical image, blood optical cross-section is often Gaussian Profile, therefore, in laser speckle algorithm, is used
Gaussian template matches blood-vessel image, can obtain better blood flow information, such as following formula:
In above formula, Gaussian function about origin [0,0] symmetrically, Wi(xi, yi) refer to Gaussian kernel value in Gaussian template, what σ referred to
It is vessel cross-sections gray scale, considers speckle image discreteness, value is 1 in fixed range, and non-stationary ranges do not calculate, and L is
Along the regular length in blood vessel segmentation direction, L increases, and the smooth effect enhancing of blood vessel, noise reduces, but for toy brain
Portion's capillary capilary, blood vessel only may meet direction in a very short segment length, therefore select biggish value result is simultaneously instead
It is bad.xi, yiThe coordinate value of each point respectively in Gaussian template.
The spin matrix of gaussian kernel function is defined as follows, and is here y-axis by first direction definition, thereafter along rotation clockwise
Turn, such as following formula；
Here θiIt is the direction of rotation of Gaussian kernel, according to the minimum direction being calculated in formula (2), can determines θi's
Direction, then by spin matrix in conjunction with the weight of Gaussian kernel, K value is being sought with original image convolution.
Gaussian kernel function length of a curve is indefinite extension, therefore in carrying out convolution matching, can be given up in distance
The farther away point of the heart, by experimental verification, if image pixel less than 1024, can choose 3 × 3 bulk, if image
Pixel can choose 5 × 5 bulk, such as Fig. 6 between 1024-2048.
Weight computing formula in Gaussian kernel is
In above formula,For the Gaussian kernel value of no weighted value, σ is vessel cross-sections gray scale, considers speckle image
Particularity, value is 1, L for along the regular length in blood vessel segmentation direction, x in fixed rangei, yiRespectively Gaussian template
The coordinate value of middle each point.
In addition, Wi(xi, yi) refer to the Gaussian kernel window of no weighted value, it is contemplated that laser speckle value be standard deviation with
The ratio of mean value convolution window, so needing exist for balance weighted value, therefore W ' herei(xi, yi) be final template weighting it is public
Formula is as follows,
W′i(xi, yi)=Wi(xi, yi)·mi(xi, yi)
(6)
Wi(xi, yi) refer to Gaussian kernel value in Gaussian template, mi(xi, yi) refer to Gaussian template core weighted value, will determine
Have the right Gaussian kernel template and the original image convolution in good direction, obtain the capilary flow velocity figure finally needed.
It is a kind of micro- blood flow imaging based on anisotropy laser speckle provided by the embodiment of the present invention as shown in Figure 2
Contrast the laser speckle system light path figure of algorithm, wherein 1 is laser, 2 be collimating mirror, and 3 be beam expanding lens, and 4 be reflective mirror, and 5 are
Stereoscope, 6 be CCD camera, and 7 be computer.
It is that the embodiment of the present invention provides a kind of micro- blood flow imaging lining based on anisotropy laser speckle as shown in Figure 3
Than the simulation results of algorithm.Used analogous diagram to verify the reliability of this algorithm, based on the algorithm obtain result figure,
Contrast compares space or time convolution algorithm is considerably higher, and it is assumed that the flow velocity of angiosomes is more uniform.Wherein A is imitative
True scatterer surface, dark areas are blood vessels, and gray area is tissue；B spatial convoluted algorithm K value figure；C time convolution algorithm
K value figure；D Gaussian kernel convolution algorithm K value figure.
It is a kind of micro- blood flow imaging based on anisotropy laser speckle provided by the embodiment of the present invention as shown in Figure 4
Contrast algorithm applied to mouse brain vascular effects.The mouse cerebrovascular is each original as shown, the visual field is about 2.5 × 2.5mm
Speckle image is 1024 × 1024 pixels, and image resolution ratio is 4.88 μm, the results showed that under the conditions of low flow velocity, the algorithm is to micro-
The distinguishing of blood vessel is higher.Wherein A animal cerebral microvascular figure；B time convolution algorithm K value figure；C spatial convoluted algorithm K value figure；D
Gaussian kernel convolution algorithm K value figure.
Claims (8)
1. a kind of blood flow velocity monitoring method for contrasting algorithm based on micro- blood flow imaging, characterized by the following steps:
(1) original laser speckle image is acquired；
(2) is contrasted by K value and obtains the smallest space for original laser speckle image linear operator calculating Conventional temporal and contrast K value
Direction；
(3) spin matrix that Gaussian template is obtained further according to direction, obtains corresponding Gaussian convolution template；
(4) anisotropy K value is obtained with original laser speckle image convolution, extracts capilary region, utilizes the direction of brain blood flow
Specificity obtains capilary flow velocity figure.
2. the blood flow velocity monitoring method according to claim 1 for contrasting algorithm based on micro- blood flow imaging, it is characterised in that:
Original laser speckle image acquisition methods in the step (1) are as follows: build laser speckle light path system, by laser beam be radiated at by
It surveys on object, obtains original laser speckle image.
3. the blood flow velocity monitoring method according to claim 1 for contrasting algorithm based on micro- blood flow imaging, it is characterised in that:
K value is calculated using time convolution method in the step (2), wherein 30 frame of optional picture, calculation formula are as follows:
In above formula, σ is original image standard deviation, and<I>is the average value of original image.
4. the blood flow velocity monitoring method according to claim 1 for contrasting algorithm based on micro- blood flow imaging, it is characterised in that:
The direction calculating formula that K value is contrasted in the smallest space of the step (2) is as follows:
In above formula, KP(i, j)It indicates along target pixel points P (i0, j0) along the time convolution k value under linear operator direction calculating,
P (i, j) refers to P (i in image0, j0) centered on pixel on the different linear directions put, δ refer on linear direction away from P
(i0, j0) distance, value is the cumulative of time convolution K value less than 5, D (i, j) here, and more different D (i, j) obtains K value
Optimal direction.
5. the blood flow velocity monitoring method according to claim 1 for contrasting algorithm based on micro- blood flow imaging, it is characterised in that:
Gaussian template matches blood-vessel image in the step (3), calculates blood flow velocity using gaussian kernel function, gaussian kernel function formula is such as
Under:
In above formula, Gaussian function about origin [0,0] symmetrically, Wi(xi, yi) it is Gaussian kernel value in Gaussian template, σ is that blood vessel is horizontal
Section gray scale considers speckle image discreteness, and value is 1 in fixed range, and non-stationary ranges do not calculate, and L is along blood vessel
Divide the regular length in direction, L increases, and the smooth effect enhancing of blood vessel, noise reduces, xi, yiRespectively each point in Gaussian template
Coordinate value.
6. the blood flow velocity monitoring method according to claim 5 for contrasting algorithm based on micro- blood flow imaging, it is characterised in that:
The weighted value of the Gaussian kernel value calculates such as following formula:
In above formula,For the Gaussian kernel value of no weighted value, σ is vessel cross-sections gray scale, considers that speckle image is special
Property, value is 1, L for along the regular length in blood vessel segmentation direction, x in fixed rangei, yiRespectively each point in Gaussian template
Coordinate value, mi(xi, yi) be Gaussian kernel value weighted value.
7. the blood flow velocity monitoring method according to claim 6 for contrasting algorithm based on micro- blood flow imaging, it is characterised in that:
The weighted value of the Gaussian kernel value is calculate by the following formula the Gaussian kernel value after being weighted:
Wi(xi, yi)=Wi(xi, yi)·mi(xi, yi)
The calculation formula based on laser speckle value is the ratio of standard deviation Yu mean value convolution window, and balances weighted value, wherein mi
(xi, yi) refer to Gaussian template core value weighted value, have the right Gaussian kernel template and the original image convolution in direction will be determined, obtained
Obtain the capilary flow velocity figure finally needed, Wi(xi, yi) be Gaussian template in Gaussian kernel value, Wi(xi, yi) be weighting after height
This core value.
8. the blood flow velocity monitoring method according to claim 1 for contrasting algorithm based on micro- blood flow imaging, it is characterised in that:
The spin matrix r of step (3) Gaussian templateiSuch as following formula is calculated, is here y-axis by first direction definition, thereafter along suitable
Hour hands rotation；
In above formula, θiIt is the direction of rotation of Gaussian kernel.
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| CN111568407A (en) * | 2020-05-28 | 2020-08-25 | 上海理工大学 | Method for judging shock development stage of patient based on laser speckle blood flow instrument |
| US11206991B2 (en) | 2020-02-14 | 2021-12-28 | Activ Surgical, Inc. | Systems and methods for processing laser speckle signals |
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
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| US11206991B2 (en) | 2020-02-14 | 2021-12-28 | Activ Surgical, Inc. | Systems and methods for processing laser speckle signals |
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| CN111568407A (en) * | 2020-05-28 | 2020-08-25 | 上海理工大学 | Method for judging shock development stage of patient based on laser speckle blood flow instrument |
| CN111568407B (en) * | 2020-05-28 | 2023-04-25 | 上海理工大学 | System for judging patient shock development stage based on laser speckle blood flow meter |
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