CN1792323A - Method and equipment for transcranial cerebral blood flow high-resolution imaging - Google Patents

Abstract

The invention belongs to cerebral blood flow detection technology, and relates to a high-resolution imaging method and device for transcranial cerebral blood flow. The near-infrared collimated laser beam is irradiated on the measured object, and several frames of laser speckle images reflected by the measured object are continuously collected with the same exposure time and frame interval time, and the light intensity on the same pixel is calculated for each frame of image collected The time contrast changes with time, and then use the time contrast to calculate the cerebral blood flow velocity; traverse all the pixels, and finally use the gray value of the blood flow velocity corresponding to each pixel to construct a two-dimensional cerebral blood flow velocity Distribution. The device includes a laser beam, two polarizers, a workbench, a photoelectric imaging system and a computer. The invention can monitor the hemodynamics of the cerebral cortex and the morphological changes of the cerebral blood vessels in real time, dynamically, with high time and space resolution through the complete skull, without performing craniotomy or skull thinning operations on the measured object. The invention has the advantages of non-damage and high spatial resolution without scanning.

Description

A kind of transcranial cerebral blood flow high-resolution imaging method and device thereof

Technical field

The invention belongs to the cerebral blood flow detection technique, be specially a kind of transcranial cerebral blood flow high-resolution imaging method and device thereof, it is particularly suitable for that cortex regional flow under studying physiological and the pathological state distributes and cerebrovascular form etc.

Background technology

Obtain the local two-dimentional blood flow distributed intelligence of high-resolution cortex to the nerve-blood vessel coupling of studying physiological and morbid state hypencephalon and adjusting, cerebral function imaging, medicine Effect Evaluation, and the diagnosis and the pathological study of great disease of brain such as cerebral ischemia, cerebral hemorrhage are significant to cerebrovascular and microcirculation.But traditional cerebral blood flow detection method scarcely possesses imaging capability, promptly there is not spatial resolving power, admittance is traced as volume, rheoencephalogram based on the brain resistance measurement, transcranial doppler, laser-Doppler etc., and the higher micro tv video recording of resolution, scan laser Doppler, the laser speckle imaging space contrasts technology such as analysis and then needs measurand is carried out operation of opening cranium, remove the skull (even cerebral dura mater) of detected part, expose cortex, perhaps with the skull wear down of detected part to pellucidity, thereby be the method for damage, can influence the normal physiological state of measurand, imaging causes adverse effect to cerebral blood flow, and does not possess the clinical expansion potentiality.

Summary of the invention

The object of the present invention is to provide a kind of transcranial cerebral blood flow high-resolution imaging method, this method has solved the problem that existing high-resolution cerebral blood flow formation method need be opened cranium or wear down skull operation to experimental subject, and the high-resolution of having realized the cerebral blood flow Two dimensional Distribution is through the imaging of cranium not damaged; The present invention also provides the device of realizing this method.

The invention provides a kind of transcranial cerebral blood flow high-resolution imaging method, the steps include:

(1) collimated laser beam of wavelength in 700～950nm scope shone on the measurand;

(2) with identical time of exposure and the some frame measurands of frame period time continuous acquisition laser light reflected speckle image;

(3) each two field picture to gathering, it is capable to take out in every two field picture i, and (i j), utilizes N the gray value that is taken out to calculate on this pixel the time dependent time of light intensity and contrasts K the gray value I of j column position place pixel _t(i, j), its computing formula is:

$K_t(i,j)=\frac{{\mathrm{\&sigma;}}_t(i,j)}{<I(i,j)>}=\frac{\sqrt{\frac{1}{N-1}\underset{n=1}{\overset{N}{\mathrm{\&Sigma;}}}{[I_n(i,j)-<I(i,j)>]}^2}}{<I(i,j)>}$

I wherein _n(i j) represents in the n two field picture i capable, and the gray value of j row place pixel, N are the number of image frames of being gathered, σ _t(i, j) represent the N two field picture (i j) locates the standard deviation of pixel gray scale,＜I (i, j)〉be the N two field picture (i j) locates the time average of pixel gray scale, and computing formula is:

$<I(i,j)\&GreaterEqual;\frac{1}{N}\underset{n=1}{\overset{N}{\mathrm{\&Sigma;}}}{I}_n(i,j)$

(4) the cerebral blood flow velocity V that utilizes the gained laser speckle time to contrast to calculate this pixel place (i, j), formula is as follows:

$V(i,j)=\frac{c}{K_t^2(i,j)},$ Wherein c is a correction coefficient

(5) set by step all pixels in the traversing graph picture of (2)～(4), the laser speckle time that obtains all pixel correspondences is contrasted value and blood flow rate value;

(6) be gray scale with the blood flow rate value of each pixel correspondence respectively, make up the cerebral blood flow velocity scattergram of two dimension.

Each two field picture time of exposure is 1ms～20ms in the above-mentioned steps (1), and the frame period time is less than 200ms, frame number N 〉=20 of collection.

Carry out dynamic cerebral blood flow distribution monitoring if desired, then comprise step (7), need repeat the step of (2)～(6) to the time point of cerebral blood flow distribution carrying out imaging, obtain difference two-dimensional brain blood flow distributed images constantly at each.

Realize the device of said method, its structure is: laser beam, first linear polarizer and workbench are positioned on the illumination path successively, and first linear polarizer is vertical with the incident laser light beam; Workbench, second polaroid and photo electric imaging system are positioned on the imaging optical path successively, and second linear polarizer is vertical with the photo electric imaging system optical axis direction, and be concentric with photo electric imaging system, and the polarization direction of its polarization direction and first linear polarizer is perpendicular; Computer links to each other with photo electric imaging system by image pick-up card, is used for data acquisition and processing (DAP).

The present invention is based on dynamic laser speckle imaging time domain statistical characteristic analysis, see through skull and directly cortex medium vessels under the skull and intracortical blood flow are carried out the high spatial resolution imaging.Compare with other existing high-resolution cerebral blood flow detection technique, the advantage of transcranial cerebral blood flow high-resolution imaging method provided by the present invention is: need not the operation that cranium or wear down skull are opened in enforcement to measurand, can directly see through blood flow distribution of high-resolution two dimension and cerebrovascular form that complete skull obtains cortex under the skull with no damage, the cortex hemodynamics be changed carry out the monitoring of real-time, dynamic, high time, spatial resolution.The present invention has the advantage of not damaged and high spatial resolution, and need not to scan.Its range of application is can be in order to laboratory animals such as the rat under studying physiological and the pathological state, mice, rabbit, cat, monkeys, and people's cortex regional flow distributes, and the cortex blood flow that neural activity, disease of brain cause changes.The present invention is applicable to the research of cerebral function imaging, neuro physiology, disease of brain pathology and evaluating drug effect.

Description of drawings

Fig. 1 is a transcranial cerebral blood flow high-resolution imaging apparatus structure sketch map.

Fig. 2 is an image acquisition control software flow pattern.

Fig. 3 a is applied to the experimental result of rat through cranium brain blood cortex stream high-resolution imaging with method disclosed by the invention.

Fig. 3 b adopts other existing method (the laser speckle imaging space contrasts analysis) gained experimental result down with the same experiment condition of Fig. 3 a.

The specific embodiment

The reconstruction of cerebral blood flow distributed image need utilize at each need measure the multiframe laser speckle image that the time point of cerebral blood flow is gathered, the laser speckle image sequence of being gathered is carried out laser speckle time domain statistical characteristic analysis, calculate in the laser speckle image the time dependent time statistic of light intensity (being gradation of image) on each pixel, with this statistic reflect this pixel the blood flow rate at corresponding cortex place; So all pixels in the traversing graph picture can obtain high-resolution two-dimensional brain blood flow distributed image.

Particularly, the step of the inventive method is:

(1) collimated laser beam of wavelength in 700～950nm scope shone on the measurand;

(2) with identical time of exposure and frame period time continuous acquisition N frame measurand laser light reflected speckle image.For reaching better technique effect, each two field picture time of exposure should be 1ms～20ms, and the frame period time should be less than 200ms, and the frame number N of collection generally should be more than or equal to 20;

(3) to collection N two field picture, it is capable to take out in every two field picture i, and (i j), utilizes N the gray value that is taken out to calculate on this pixel the time dependent time of light intensity and contrasts K the gray value I of j column position place pixel _t(i, j), its computing formula is:

$K_t(i,j)=\frac{{\mathrm{\&sigma;}}_t(i,j)}{<I(i,j)>}=\frac{\sqrt{\frac{1}{N-1}\underset{n=1}{\overset{N}{\mathrm{\&Sigma;}}}{[I_n(i,j)-<I(i,j)>]}^2}}{<I(i,j)>},$

I wherein _n(i j) represents in the n two field picture i capable, and the gray value of j row place pixel, N are the number of image frames of being gathered, σ _t(i, j) represent the N two field picture (i j) locates the standard deviation of pixel gray scale,＜I (i, j)〉be the N two field picture (i j) locates the time average of pixel gray scale, and computing formula is:

$<I(i,j)\&GreaterEqual;\frac{1}{N}\underset{n=1}{\overset{N}{\mathrm{\&Sigma;}}}{I}_n(i,j).$

(4) the cerebral blood flow velocity V that utilizes the gained laser speckle time to contrast to calculate this pixel place (i, j), formula is as follows:

$V(i,j)=\frac{c}{K_t^2(i,j)},$ Wherein c is a correction coefficient.

(5) set by step all pixels in the traversing graph picture of (2)～(4), the laser speckle time that obtains all pixel correspondences is contrasted value and blood flow rate value.As every frame laser speckle image size is that M is capable, the K row, then obtaining M * K laser speckle time altogether contrasts value, and M * K blood flow rate value.

(6) be gray scale with the blood flow rate value of each pixel correspondence respectively, can make up the cerebral blood flow velocity scattergram of two dimension.

(7) carry out dynamic cerebral blood flow distribution monitoring if desired, the variation that the different cerebral blood flow constantly of i.e. observation distribute, then need repeat the step of (2)～(6) to the time point of cerebral blood flow distribution carrying out imaging, obtain different high-resolution two-dimensional brain blood flow distributed images constantly at each.

The device of realization said method as shown in Figure 1.Wavelength shines the object to be measured that is placed on the workbench 3 in collimation near-infrared laser light beam 1 warp first polaroid 2 backs of 700～950nm scope, form laser speckle in object surface to be measured, this laser speckle through after second linear polarizer 4 in computer 7 under the control of respective image acquisition software by photo electric imaging system 5 imagings, and pass through image pick-up card 6 with gained laser speckle image input computer 7, wherein photo electric imaging system 5 can be the microscope of electrically charged coupled apparatus camera, the charge-coupled device camera of band photographic lens, analog video camera or DV.It should be noted that the direction that first linear polarizer 2 is placed should be vertical with incident laser light beam 1; Second linear polarizer 4 should vertically be placed along photo electric imaging system 5 optical axis directions, and maintenance and photo electric imaging system 5 is concentric, and should be perpendicular along the polarization direction that makes its polarization direction and first linear polarizer 1, the adverse effect that blood flow detection is caused with the direct reflection of removing region surface to be measured.

The workflow of image acquisition control software as shown in Figure 2 in the computer 7.

Implement one:

Experimental subject is the Wistar rat, is fixed on the workbench 3, and be light source with the semiconductor laser of 780nm, the laser beam 1 behind the collimator and extender is through retread rat head after inciding peeling of first linear polarizer 2.Photo electric imaging system 5 imagings that the laser speckle that rat head reflects to form is made of the charge-coupled device camera of being with the macrovision camera lens after second linear polarizer 4, the optical system amplification is made as 0.5 times.Time of exposure 5ms, frame period time 25ms, continuous acquisition 40 frame laser speckle images.Utilize the 40 frame laser speckle images of being gathered, contrast by the method for the invention calculating laser speckle time, and further rebuilding two-dimensional brain blood flow distributed image, gained result such as Fig. 3 a show that the position of gray value low more (dark more) means that this place's cerebral blood flow is fast more among the figure.The rat skull that sees through that this method can be successful as can be seen from Fig. 3 a obtains the Two dimensional Distribution of cortex blood flow, and can clearly tell the cerebrovascular form of cortex, and resolution is better than 50 microns.For comparing with other method, under identical experiment condition, utilize the laser speckle space to contrast and analyze and carry out blood flow detection, the gained result is shown in Fig. 3 b.From the comparison of Fig. 3 a and Fig. 3 b, the solution of finding out method success provided by the present invention that can be perfectly clear other method can not see through skull and obtain and carry out the problem that the high-resolution cerebral blood flow is scattered in picture under the skull.

Claims (4)

1. A high-resolution imaging method for transcranial cerebral blood flow, the steps of which are: (1) Irradiate a collimated laser beam with a wavelength in the range of 700-950nm onto the object to be measured; (2) Continuously collect several frames of laser speckle images reflected by the measured object with the same exposure time and frame interval; (3) For each frame of image collected, take out the gray value I(i, j) of the pixel at the i-th row and j-th column in each frame of the image, and use the N gray-scale values taken out to calculate the pixel The time contrast ratio K _t (i, j) of the upper light intensity changing with time, its calculation formula is: ${\mathrm{<span\; class="notranslate">\; K</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\frac{{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; )</span>}{<span\; class="notranslate">\; <</span>\mathrm{<span\; class="notranslate">\; I</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; ></span>}<span\; class="notranslate">\; =</span>\frac{\sqrt{\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; N</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}\underset{\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; N</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{<span\; class="notranslate">\; [</span>{\mathrm{<span\; class="notranslate">\; I</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; <</span>\mathrm{<span\; class="notranslate">\; I</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; ></span><span\; class="notranslate">\; ]</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}}{<span\; class="notranslate">\; <</span>\mathrm{<span\; class="notranslate">\; I</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; ></span>}<span\; class="notranslate">\; ,</span>$ Among them, _In (i, j) represents the i-th row in the n-th frame image, the gray value of the pixel at the j-th column, N is the number of image frames collected, and σ _t (i, j) represents the N-frame image ( i, the standard deviation of the pixel gray level at j), <I (i, j)> is the time average value of the pixel gray level at the N frame image (i, j), and the calculation formula is: $<span\; class="notranslate">\; <</span>\mathrm{<span\; class="notranslate">\; I</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; ></span><span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; N</span>}}\underset{\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; N</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; I</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; )</span>$ (4) Use the obtained laser speckle time contrast to calculate the cerebral blood flow velocity V(i, j) at the pixel, the formula is as follows: $V(i,j)=\frac{c}{K_t^2(i,j)},$ where c is the correction coefficient, (5) traverse all the pixels in the image according to steps (2) to (4), and obtain the laser speckle time contrast value and the blood flow velocity value corresponding to all pixels; (6) Construct a two-dimensional cerebral blood flow velocity distribution map with the blood flow velocity value corresponding to each pixel in grayscale. 2. The method according to claim 1, characterized in that: in step (1), the exposure time of each frame of image is 1 ms-20 ms, the interval between frames is less than 200 ms, and the number of frames collected is N≥20. 3. The method according to claim 1 or 2, characterized in that it further comprises step (7), repeating the steps (2) to (6) at each time point when the cerebral blood flow distribution needs to be imaged, Obtain two-dimensional images of cerebral blood flow distribution at different moments. 4. A high-resolution imaging device for transcranial blood flow, the structure of which is: the laser beam (1), the first linear polarizer (2) and the workbench (3) are sequentially located on the illumination optical path, and the first linear polarizer (2) perpendicular to the incident laser beam (1); the workbench (3), the second polarizer (4) and the photoelectric imaging system (5) are located on the imaging optical path in turn, and the second linear polarizer (4) and the photoelectric imaging system ( 5) The direction of the optical axis is vertical, concentric with the photoelectric imaging system (5), and its polarization direction is perpendicular to the polarization direction of the first linear polarizer (2); the computer (7) communicates with the photoelectric imaging system ( 5) Connected for data acquisition and processing.

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