CN110349256B - Vascular reconstruction method and device and computer terminal - Google Patents
Vascular reconstruction method and device and computer terminal Download PDFInfo
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Abstract
The invention discloses a vascular reconstruction method, a vascular reconstruction device and a computer terminal, wherein the vascular reconstruction method comprises the following steps: acquiring at least one layer of ultrasonic blood flow signals and calculating an energy value; mapping all energy values into an energy matrix according to the sampling coordinates, and calculating spatial position information corresponding to the sampling points according to the sampling coordinates and a pre-calculated transformation matrix; calculating sampling coordinates of mapping points corresponding to the sampling points in the ultrasonic blood flow signals of each layer according to the spatial position information of each sampling point and the inverse matrix of the transformation matrix; judging whether the sampling coordinates of the mapping points fall in the subscript range corresponding to the energy matrix, if so, calculating the energy value corresponding to the mapping points in the energy matrix according to the energy values of the coordinate points of the preset number nearest to the sampling coordinates; and reconstructing a blood flow model according to the energy values of all the sampling points and the mapping points. The technical scheme of the invention can convert the acquired ultrasonic blood flow signals into a blood flow model, thereby facilitating the observation and diagnosis of doctors.
Description
Technical Field
The present invention relates to the field of ultrasound image processing technology, and in particular, to a method and apparatus for reconstructing a blood vessel, and a computer terminal.
Background
The cerebral apoplexy is an acute cerebrovascular disease, is a disease of brain tissue injury caused by sudden rupture of cerebral blood vessels or incapability of flowing into the brain due to blood vessel blockage, has the characteristics of high morbidity, high mortality and high disability rate, and is the disease with the highest mortality rate in China. The existing detection means for cerebral apoplexy mainly comprises the steps of scanning brain images of a patient through magnetic resonance blood vessel imaging to obtain medical magnetic resonance images of cerebral blood vessels and extravascular tissue parts, and monitoring cerebral vascular diseases through nuclear magnetic resonance images. Or detecting blood flow conditions of each blood vessel, cerebral arterial blood vessel and branches thereof in and out of the cranium by using the transcranial Doppler ultrasound, judging whether vascular lesions such as sclerosis, stenosis, ischemia, deformity, spasm and the like exist or not, and dynamically monitoring the cerebrovascular diseases.
However, existing techniques for magnetic resonance scanning require the patient to remain stationary on the diagnostic couch for a longer period of time during the scanning process. Unintentional body shaking of the patient is extremely likely to cause imaging artifacts, resulting in the reconstruction of brain models while causing the migration or rupture of fine vascular sites. Meanwhile, the nuclear magnetic resonance scanning needs longer time, the imaging is high in price, the nuclear magnetic resonance scanning is not suitable for preventing and checking by a large number of people, and the cerebral apoplexy patient needs to be checked regularly and for a long time, so that the burden is too great for the patient. Although the transcranial Doppler examination is cheap, the patient can pass through the space (acoustic window) of the skull without wound, the operation is simple and convenient, the repeatability is good, and the patient can be continuously and dynamically observed for a long time. However, since the transcranial Doppler examination can only see blood flow signals, and no surrounding relevant anatomical structure is used as a reference, a doctor is required to have abundant experience on parameters such as the spatial position of blood vessels in the brain, blood flow speed and the like, so that the transcranial Doppler examination is difficult to popularize.
Disclosure of Invention
In view of the foregoing, an objective of an embodiment of the present invention is to provide a method, an apparatus, and a computer terminal for reconstructing a blood vessel, so as to solve the drawbacks of the prior art.
One embodiment of the present invention provides a vascular reconstruction method comprising:
acquiring at least one layer of ultrasonic blood flow signals and calculating the energy value of each sampling point in each layer of ultrasonic blood flow signals;
mapping all energy values into an energy matrix according to the sampling coordinates of the sampling points, and calculating spatial position information corresponding to the sampling points according to the sampling coordinates and a pre-calculated transformation matrix;
calculating the sampling coordinates of the mapping points corresponding to the sampling points in the ultrasonic blood flow signals of each layer according to the spatial position information of each sampling point and the inverse matrix of the transformation matrix;
judging whether the sampling coordinates of the mapping points fall in the subscript range corresponding to the energy matrix or not;
if the sampling coordinate falls in the subscript range, calculating the energy value corresponding to the mapping point in the energy matrix according to the energy value of the coordinate points with the nearest preset number to the sampling coordinate;
and reconstructing a blood flow model according to the energy values of all the sampling points and the mapping points.
In the above-mentioned vascular reconstruction method, the "calculating the energy value of each sampling point in the ultrasound blood flow signal of each layer" includes:
and performing short-time Fourier transform on the ultrasonic blood flow signals to obtain energy values corresponding to each sampling point.
In the above vascular reconstruction method, the calculation process of the transformation matrix includes:
determining sampling coordinates of a first sampling point, a second sampling point and a third sampling point in the first layer of ultrasonic blood flow signals according to the acquisition angle of the ultrasonic blood flow signals and the preset edge length of the ultrasonic blood flow signals;
calculating a first vector from the third sampling point to the first sampling point and a second vector from the third sampling point to the second sampling point according to the sampling coordinates;
and calculating the transformation matrix according to the first vector, the second vector, the distance between the equipment for acquiring the ultrasonic blood flow signals and the first layer ultrasonic blood flow signals and the layer number of the ultrasonic blood flow signals.
In the above-mentioned vascular reconstruction method, the calculating the sampling coordinates of the mapping points corresponding to the sampling points in the ultrasound blood flow signals of each layer according to the spatial position information of each sampling point and the inverse matrix of the transformation matrix includes:
taking the sampling coordinates of each sampling point as subscripts, and generating a spatial position matrix according to the spatial position information of the sampling points;
taking equipment coordinates for acquiring the ultrasonic blood flow signals as origin coordinates, and carrying out linear transformation on the space position matrix according to the origin coordinates so as to register the origins of the space position matrix;
traversing each sampling point in the registered space position matrix, and calculating the binary norm of each sampling point according to the space position information of the sampling point to obtain a unit vector of the sampling point;
multiplying the unit vector by the distance between each layer to obtain the spatial position information of the corresponding mapping point of the sampling point in the ultrasonic blood flow signal of each layer;
and multiplying the unit vectors corresponding to the spatial position information of all the mapping points by the inverse matrix of the transformation matrix to obtain the sampling coordinates of the mapping points.
In the above-mentioned vascular reconstruction method, the calculating the energy value corresponding to the mapping point according to the energy value of the coordinate point of the nearest preset number to the sampling coordinate in the energy matrix includes:
searching a preset number of coordinate points closest to the sampling coordinates in the energy matrix and acquiring energy values of the preset number of coordinate points;
and carrying out interpolation operation on the energy values of the coordinate points to obtain the energy value corresponding to the mapping point.
In the above-described vascular reconstruction method, the "reconstructing a blood flow model according to the energy values of the sampling points and the mapping points" includes:
filtering the energy values of each sampling point and each mapping point through a preset filtering threshold to obtain filtering energy values corresponding to each sampling point and each mapping point;
and taking the filtered energy value as a blood flow model matrix, performing Fourier inverse transformation on the blood flow model matrix to obtain a blood flow signal corresponding to the blood flow model matrix, and reconstructing a blood flow model according to the blood flow signal.
The above vascular reconstruction method further comprises:
and superposing the blood flow model into a pre-established standard brain blood vessel model according to the spatial position information of each sampling point and each mapping point in the blood flow model matrix.
Another embodiment of the present invention provides a vascular reconstruction device comprising:
the first energy value calculation module is used for acquiring at least one layer of ultrasonic blood flow signals and calculating the energy value of each sampling point in each layer of ultrasonic blood flow signals;
the spatial position information calculation module is used for mapping all energy values into an energy matrix according to the sampling coordinates of the sampling points and calculating spatial position information corresponding to the sampling points according to the sampling coordinates and a pre-calculated transformation matrix;
the sampling coordinate calculation module is used for calculating the sampling coordinates of the mapping points corresponding to the sampling points in the ultrasonic blood flow signals of each layer according to the spatial position information of each sampling point and the inverse matrix of the transformation matrix;
the judging module is used for judging whether the sampling coordinates of the mapping points fall in the subscript range corresponding to the energy matrix or not;
the second energy value calculation module is used for calculating the energy value corresponding to the mapping point in the energy matrix according to the energy value of the coordinate points with the nearest preset number from the sampling coordinate when the sampling coordinate falls in the subscript range;
and the blood flow model reconstruction module is used for reconstructing a blood flow model according to the energy values of all the sampling points and the mapping points.
Yet another embodiment of the present invention provides a computer terminal including a memory for storing a computer program and a processor that runs the computer program to cause the computer terminal to perform the above-described vascular reconstruction method.
Yet another embodiment of the present invention provides a computer-readable storage medium storing the computer program used in the above-described computer terminal.
According to the vascular reconstruction method, the energy values of all sampling points in at least one layer of collected ultrasonic blood flow signals are calculated through a software method, sampling coordinates of mapping points of the sampling points in each layer of ultrasonic blood flow signals are calculated according to unit vectors of the sampling points and an inverse matrix of a pre-calculated transformation matrix, the energy values of the mapping points are calculated according to the preset number of energy values closest to the sampling coordinates, finally a blood flow model is constructed according to the energy values of all the sampling points and the mapping points, the collected ultrasonic blood flow signals can be directly converted into the blood flow model, a doctor can observe and diagnose conveniently, the dependence of diagnosis results of the cerebrovascular diseases on experience of the doctor is reduced, and the examination success rate is improved.
Drawings
In order to more clearly illustrate the technical solutions of the present invention, the drawings that are required for the embodiments will be briefly described, it being understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope of the present invention.
Fig. 1 shows a schematic flow chart of a vascular reconstruction method according to a first embodiment of the present invention.
Fig. 2 shows a schematic diagram of a sampling path according to a first embodiment of the present invention.
Figure 3 shows a diagrammatic representation of a transcranial doppler scan provided by a first embodiment of the present invention.
Fig. 4 shows a graphical representation of a reconstructed blood flow model provided by a first embodiment of the invention.
Fig. 5 shows a schematic flow chart of a vascular reconstruction method according to a second embodiment of the present invention.
Fig. 6 shows a schematic diagram of a standard brain blood vessel model with superimposed blood flow models according to a second embodiment of the invention.
Fig. 7 is a schematic structural view of a vascular reconstruction device according to a third embodiment of the present invention.
Description of main reference numerals:
300-vascular reconstruction device; 310-a first energy value calculation module; 320-a spatial location information calculation module; 330-a sample coordinate calculation module; 340-a judging module; 350-a second energy value calculation module; 360-a blood flow model reconstruction module;
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. The same or similar reference numerals refer to the same or similar elements or elements having the same or similar functions throughout.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more such feature. In the description of the present invention, the meaning of "a plurality" is two or more, unless explicitly defined otherwise.
The terms "comprising," "including," "having," "containing," or any other variation thereof, as used herein, are intended to cover a non-exclusive inclusion. For example, a composition, step, method, article, or apparatus that comprises a list of elements is not necessarily limited to only those elements but may include other elements not expressly listed or inherent to such composition, step, method, article, or apparatus.
The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the invention, as presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be made by a person skilled in the art without making any inventive effort, are intended to be within the scope of the present invention.
Example 1
Fig. 1 shows a schematic flow chart of a vascular reconstruction method according to a first embodiment of the present invention.
The vascular reconstruction method comprises the following steps:
in step S110, at least one layer of ultrasonic blood flow signals is acquired and energy values of respective sampling points in each layer of ultrasonic blood flow signals are calculated.
In particular, the ultrasound blood flow signals of the intracranial and extracranial blood vessels, cerebral arterial loop vessels, and branches thereof may be scanned by a transcranial Doppler (Transcranial Doppler TCD) device.
In this embodiment, the "calculating the energy value of each sampling point in the ultrasound blood flow signal of each layer" includes:
and performing short-time Fourier transform on the ultrasonic blood flow signals to obtain energy values corresponding to each sampling point.
Specifically, assuming that the ultrasonic blood flow signal is x (n), ω (n) is a window function, and the energy value S of each sampling point i Can be calculated by the following formula:
where i=e×f×g, E denotes a scanning length of each layer, F denotes a scanning width of each layer, and G denotes a total number of layers scanned.
In some other embodiments, the energy values of the sampling points in the ultrasonic blood flow signals of each layer can also be calculated by a sparse matrix method.
In step S120, all energy values are mapped into an energy matrix according to the sampling coordinates of the sampling points, and spatial position information corresponding to the sampling points is calculated according to the sampling coordinates and the pre-calculated transformation matrix.
Specifically, as shown in fig. 2, the track of the probe of the transcranial doppler device when scanning the ultrasonic blood flow signal of the brain is in an S-shaped distribution, for example, in the ultrasonic blood flow signal of the first layer, the probe starts scanning with a point C as a starting point, scans along the S-shaped track to a point a, starts scanning from the point a to a point a along the S-shaped track, scans from the point a to the point A1 along the S-shaped track to the point C1, and sequentially scans along the S-shaped track to an end point K, and at this time, the ultrasonic blood flow signal of the first layer is completely scanned.
The S-shaped track determines the sampling sequence of each sampling point in each layer of ultrasonic blood flow signal, and the sampling coordinates are determined according to the sampling sequence.
Assuming that the probe of the transcranial Doppler device is not at the same scanning depth and the acquisition angles of each scanning rotation are the same, mapping the energy values of the ultrasonic blood flow signals of each layer into an energy matrix, and determining subscripts of the energy values in the energy matrix by sampling coordinates.
For example, suppose that the track segment between the point C and the point a includes four sampling points P1, P2, P3, and P4, which are sequentially ordered according to the sampling order: p1, P2, P3 and P4. The sampling coordinate of the first sampling point P1 may be set to P1 (1, 1), indicating that the sampling point P1 is the sampling point of the first row and the first column of the first layer; setting the sampling coordinate of the second sampling point P2 as P2 (1, 2), wherein the sampling point P2 is the sampling point of the first row and the second column of the first layer; setting the sampling coordinate of the third sampling point P3 as P3 (1, 3), wherein the sampling point P3 is the sampling point of the third column of the first row of the first layer; the sampling coordinate of the fourth sampling point is set to P4 (1, 4), which means that the sampling point P4 is the sampling point of the fourth column of the first row of the first layer.
And filling the position corresponding to the sampling coordinate in the energy matrix according to the energy value of each sampling point to obtain the energy matrix representing the energy value of each sampling point, wherein the subscript range of the energy matrix is also E, F and G.
Further, the calculation process of the transformation matrix includes:
determining sampling coordinates of a first sampling point, a second sampling point and a third sampling point in the first layer of ultrasonic blood flow signals according to the acquisition angle of the ultrasonic blood flow signals and the preset edge length of the ultrasonic blood flow signals; calculating a first vector from the third sampling point to the first sampling point and a second vector from the third sampling point to the second sampling point according to the sampling coordinates; and calculating the transformation matrix according to the first vector, the second vector, the distance between the equipment for acquiring the ultrasonic blood flow signals and the first layer ultrasonic blood flow signals and the layer number of the ultrasonic blood flow signals.
As an example, as shown in fig. 3, assuming that the acquisition angle is α, the edge length of the predetermined ultrasonic blood flow signal is r+m, where the distance between the probe of the transcranial doppler device and the curved surface L1 where the first layer ultrasonic blood flow signal is located is R, the distance between the curved surface L1 where the first layer ultrasonic blood flow signal is located and the curved surface where the nth layer ultrasonic blood flow signal is located is M, and there is a curved surface corresponding to the n-2 layer ultrasonic blood flow signal between the curved surface L1 where the first layer ultrasonic blood flow signal is located and the curved surface Ln where the nth layer ultrasonic blood flow signal is located.
It should be noted that, in the present embodiment, the curved surface where each layer of ultrasonic blood flow signal is located refers to the curved surface where each layer of ultrasonic blood flow signal is located, and the definition of the following embodiment is the same as that of the present embodiment, and no further description is given.
Then from the acquisition angle α and the edge length R, M it is known that:
u＝R*cosα，v＝R*sinα，w＝(R+M)*cosα
the curved surface where the first layer ultrasonic blood flow signal is located is a plane shown in fig. 2 after being unfolded, and the upper vertex a of the S-track is assumed to be a first sampling point, the lower vertex B is assumed to be a second sampling point, the starting point C is a third sampling point, and sampling coordinates of the first sampling point a, the second sampling point B and the third sampling point C can be calculated according to u, v and w.
For the purpose of the description, the description will be given by taking the example of calculating the sampling coordinates of the third sampling point C.
For example, as shown in fig. 2, since the curved surface where each layer of ultrasonic blood flow signal is located is a diamond after being mapped to a plane, assuming that the coordinates of the probe of the transcranial doppler device are the origin O (0, 0), then:
C x ＝R*sinα
C y ＝0
C z ＝R*cosα
therefore, the sampling coordinate of the third sampling point C is C (C x ,C y ,C z )。
After calculating the sampling coordinate A (A x ,A y ,A z ) The sampling coordinates B (B x ,B y ,B z ) And the sampling coordinate of the third sampling point C is C (C x ,C y ,C z ) Then, a vector from the third sampling point C to the first sampling point A is calculated
Vector from third sampling point C to second sampling point B
The transformation matrix R is obtained by:
assuming that the energy matrix is J, the spatial position information D corresponding to each sampling point i The corresponding spatial position matrix is D, and then the conversion relationship between the energy matrix J and the spatial position matrix D is:
D＝C+J*R
in step S130, sampling coordinates of mapping points corresponding to the sampling points in the ultrasound blood flow signals of each layer are calculated according to the spatial position information of each sampling point and the inverse matrix of the transformation matrix.
Further, the calculating the sampling coordinates of the mapping points corresponding to the sampling points in the ultrasonic blood flow signals of each layer according to the spatial position information of each sampling point and the inverse matrix of the transformation matrix includes:
taking the sampling coordinates of each sampling point as subscripts, and generating a spatial position matrix according to the spatial position information of the sampling points; taking equipment coordinates for acquiring the ultrasonic blood flow signals as origin coordinates, and carrying out linear transformation on the space position matrix according to the origin coordinates so as to register the origins of the space position matrix; traversing each sampling point in the registered space position matrix, and calculating the binary norm of each sampling point according to the space position information of the sampling point to obtain a unit vector of the sampling point; multiplying the unit vector by the distance between each layer to obtain the spatial position information of the corresponding mapping point of the sampling point in the ultrasonic blood flow signal of each layer; and multiplying the unit vectors corresponding to the spatial position information of all the mapping points by the inverse matrix of the transformation matrix to obtain the sampling coordinates of the mapping points.
Specifically, the sampling coordinates of each sampling point are used as subscripts, and the spatial position information of each sampling point is mapped into a spatial position matrix Y, wherein the size of the spatial position matrix Y is t1 x t2 x t3, t1 and t2 are absolute values larger than 2*v, and t3 is larger than 2*w. Since the spatial position matrix Y is actually based on the coordinates of the probe as the origin O (0, 0), the spatial position of the entire sampling point is reconstructed such that the spatial position is located on the negative plane of the Z axis, that is to say the spatial position matrix Y contains at least the spatial position information of the sampling points.
In order to more accurately calculate the real spatial position information of each sampling point, the coordinate of the probe of the transcranial Doppler device is taken as an original point O (0, 0), the spatial position matrix Y is subjected to linear transformation, namely, the central points (t 1/2, t2/2 and t 3/2) of the spatial position matrix Y are moved to (0, 0), the positions of all coordinate points in the corresponding spatial position matrix Y are moved as described above, and after the positions of all coordinate points in the spatial position matrix Y are moved, the calibrated spatial position matrix Y1 is obtained.
As shown in fig. 3, since the surface on which each layer of ultrasonic blood flow signal is located is a curved surface, and the length and width of the plane obtained by mapping the curved surface on which each layer of ultrasonic blood flow signal is located into a plane are different, for example, the curved surface L1 on which the first layer of ultrasonic blood flow signal is located and the curved surface Ln on which the last layer of ultrasonic blood flow signal is located are different, the number of sampling points collected in each layer of ultrasonic blood flow signal is the same, so that the sampling step sizes of the sampling points in each layer of ultrasonic blood flow signal (assuming that the distance between one sampling point and the next sampling point is the sampling step size) are different, that is, the deeper the layer number is, the greater the sampling step size is.
Thus, each sampling point in the calibrated spatial position matrix Y1 is traversed to obtain spatial position information D of each sampling point i (D ix ,D iy ,D iz ) And calculating a unit vector of rays emitted from the origin O to the sampling point according to the spatial position information of the sampling point and the origin O (0, 0), and multiplying the unit vector by the distance between the origin O and the curved surface where the ultrasonic blood flow signals of each layer are positioned to obtain the spatial position information of the mapping point of the sampling point on the curved surface where the ultrasonic blood flow signals of each layer are positioned. For example, the first sampling point of the first layer ultrasonic blood flow signal and the first sampling point of the second layer ultrasonic blood flow signal are all on the same ray, and the first sampling point of the nth layer ultrasonic blood flow signal of … ….
After the spatial position information of each mapping point is obtained through calculation, the spatial position information of each mapping point is added into a calibrated spatial position matrix Y1, and the spatial position information is multiplied by the inverse matrix point of the transformation matrix R to obtain the sampling coordinate corresponding to the mapping point, namely, the calibrated spatial position matrix Y1 is converted into an energy matrix J.
In step S140, it is determined whether the sampling coordinates of the mapping points fall within the subscript range corresponding to the energy matrix.
If the sampling coordinates fall within the subscript range corresponding to the energy matrix, proceeding to step S150, otherwise, returning to the value step S130, and continuing to execute the step of calculating the sampling coordinates of the mapping points.
Specifically, because sampling steps of sampling points in the ultrasonic blood flow signals of each layer are different, no energy value is obtained by sampling coordinates corresponding to each mapping point in the calibrated spatial position matrix Y1. Therefore, when the sampling coordinates of the mapping points fall within the subscript range of the energy matrix, the energy values of the mapping points are calculated by the following steps.
In step S150, in the energy matrix, the energy value corresponding to the mapping point is calculated according to the energy value of the coordinate point of the nearest preset number of coordinate points from the sampling coordinate.
Further, the "calculating, in the energy matrix, the energy value corresponding to the mapping point according to the energy value of the coordinate point with the nearest preset number of coordinate points from the sampling coordinate" includes:
searching a preset number of coordinate points closest to the sampling coordinates in the energy matrix and acquiring energy values of the preset number of coordinate points; and carrying out interpolation operation on the energy values of the coordinate points to obtain the energy value corresponding to the mapping point.
In this embodiment, the interpolation operation may be a tri-linear difference operation.
For example, the energy matrix J stores the energy values of all sampling points of the ultrasonic blood flow signals of each layer, and assuming that n layers of ultrasonic blood flow signals are total, if X1, 1 is the sampling point of the first row and the first column of the first layer, the value thereof is the energy value of the sampling point of the first row and the first column of the first layer, and the spatial position information of X1, 1 in the calibrated spatial position matrix Y1 is assumed to be Y10,10,10, the spatial position information of X1, 2,1 in the calibrated spatial position matrix Y1 is assumed to be Y10,12,10, Y10,11,10 (can be regarded as a mapping point) is set between the two spatial position information, and the corresponding sampling coordinates are obtained by multiplying Y10,11,10 by the inverse matrix of the transformation matrix R to obtain X1.2,1.5,1.7, and since the energy matrix J has only integer positions, the energy values of X1.2,1.5,1.7 can be obtained by three-linear interpolation of the energy values of the eight vertexes of a small cube centered on X1.2,1.5,1.7 by three-linear interpolation.
Of course, in combination with X [1.2,1.5,1.7] in the above example, in some other embodiments, the energy value of X [1.2,1.5,1.7] described above may also be obtained by bilinear interpolation or nearest-neighbor interpolation.
The spatial position information of the corrected spatial position matrix Y1 can be converted into energy values entirely in the above manner.
In step S160, a blood flow model is reconstructed from the energy values of all the sampling points and the mapping points.
Further, the "reconstructing a blood flow model from the energy values of the sampling points and the mapping points" includes:
filtering the energy values of each sampling point and each mapping point through a preset filtering threshold to obtain filtering energy values corresponding to each sampling point and each mapping point; and taking the filtered energy value as a blood flow model matrix, performing Fourier inverse transformation on the blood flow model matrix to obtain a blood flow signal corresponding to the blood flow model matrix, and reconstructing a blood flow model according to the blood flow signal.
Specifically, after obtaining the energy values corresponding to the coordinate points in the calibrated spatial position matrix Y1, in order to avoid abrupt change or noise of the generated blood flow model, filtering the energy values corresponding to the coordinate points in the calibrated spatial position matrix Y1 through preset filtering thresholds respectively to obtain filtering energy values corresponding to the coordinate points, performing inverse fourier transform on the filtering energy values corresponding to the coordinate points to obtain a blood flow signal of the blood flow model matrix corresponding to the filtering energy values, and reconstructing the blood flow model according to the blood flow signal.
A reconstructed blood flow model is shown in fig. 4, in which dark areas represent areas of high energy values.
Example 2
Fig. 5 shows a schematic flow chart of a vascular reconstruction method according to a second embodiment of the present invention.
In step S210, at least one layer of ultrasonic blood flow signals is acquired and energy values of respective sampling points in each layer of ultrasonic blood flow signals are calculated.
This step is the same as step S110 and will not be described here again.
In step S220, all energy values are mapped into an energy matrix according to the sampling coordinates of the sampling points, and spatial position information corresponding to the sampling points is calculated according to the sampling coordinates and the pre-calculated transformation matrix.
This step is the same as step S120 and will not be described here again.
In step S230, sampling coordinates of mapping points corresponding to the sampling points in the ultrasound blood flow signals of each layer are calculated according to the spatial position information of each sampling point and the inverse matrix of the transformation matrix.
This step is the same as step S130 and will not be described here again.
In step S240, it is determined whether the sampling coordinates of the mapping points fall within the subscript range corresponding to the energy matrix.
This step is the same as step S140 and will not be described here again.
In step S250, in the energy matrix, the energy value corresponding to the mapping point is calculated according to the energy value of the coordinate point of the nearest preset number of coordinate points from the sampling coordinate.
This step is the same as step S150 and will not be described here again.
In step S260, a blood flow model is reconstructed from the energy values of all the sampling points and the mapping points.
This step is the same as step S160 and will not be described here again.
In step S270, the blood flow model is superimposed on the pre-established standard brain blood vessel model according to the spatial position information of each sampling point and each mapping point in the blood flow model matrix.
Further, by using a large number of ultrasonic blood flow signals, the blood vessel models of different parts of the brain can be reconstructed, the blood vessel models are registered into the standard brain model to form the standard brain blood vessel model, the blood vessel models are superimposed to corresponding positions in the pre-established standard brain blood vessel model according to the corresponding spatial position information to obtain a superimposed model, and the superimposed model is the generated superimposed model as shown in fig. 6. Is very convenient for doctors to observe and diagnose.
In summary, the energy values of all sampling points in at least one layer of collected ultrasonic blood flow signals can be calculated by a software method, the sampling coordinates of the mapping points of the sampling points in each layer of ultrasonic blood flow signals are calculated according to the unit vector of the sampling points and the inverse matrix of the pre-calculated transformation matrix, the energy values of the mapping points are calculated according to the energy values of the preset number nearest to the sampling coordinates, finally, a blood flow model is constructed according to the energy values of all the sampling points and the mapping points, the collected ultrasonic blood flow signals can be directly converted into the blood flow model, and the blood flow model is superimposed into a pre-established standard brain blood vessel model, so that doctors can observe and diagnose the blood flow signals in the blood flow model by combining the standard brain blood vessel model more intuitively, the dependence of diagnosis results of vascular diseases on experience of doctors is reduced, the hardware consumption and examination cost caused by reconstructing blood vessels through a hardware system are avoided, and the examination success rate is improved.
Example 3
Fig. 7 shows a schematic structural diagram of a vascular reconstruction device according to a third embodiment of the present invention. The vascular reconstruction device 300 corresponds to the vascular reconstruction method of embodiment 1, and any of the options of embodiment 1 are applicable to this embodiment, and will not be described in detail here.
The vascular reconstruction device 300 includes: the system comprises a first energy value calculation module 310, a spatial position information calculation module 320, a sampling coordinate calculation module 330, a judgment module 340, a second energy value calculation module 350 and a blood flow model reconstruction module 360.
The first energy value calculating module 310 is configured to obtain at least one layer of ultrasonic blood flow signals and calculate energy values of sampling points in each layer of ultrasonic blood flow signals.
The spatial position information calculating module 320 is configured to map all energy values into an energy matrix according to the sampling coordinates of the sampling points, and calculate spatial position information corresponding to the sampling points according to the sampling coordinates and a pre-calculated transformation matrix.
The sampling coordinate calculation module 330 is configured to calculate sampling coordinates of mapping points corresponding to the sampling points in the ultrasound blood flow signals of each layer according to the spatial position information of each sampling point and the inverse matrix of the transformation matrix.
The determining module 340 is configured to determine whether the sampling coordinates of the mapping points fall within a subscript range corresponding to the energy matrix.
And a second energy value calculating module 350, configured to calculate, in the energy matrix, an energy value corresponding to the mapping point according to the energy value of the coordinate point with the nearest preset number to the sampling coordinate when the sampling coordinate falls within the subscript range.
The blood flow model reconstructing module 360 is configured to reconstruct a blood flow model according to the energy values of all the sampling points and the mapping points.
Yet another embodiment of the present invention provides a computer terminal including a memory for storing a computer program and a processor that runs the computer program to cause the computer terminal to perform the functions of all the modules in the above-described revascularization method or revascularization device.
The memory module may include a memory program area and a memory data area, wherein the memory program area may store an operating system and at least one application program required for a function; the storage data area may store images, data, etc. required for the vascular reconstruction method and the vascular reconstruction device. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid-state storage device.
The present embodiment also provides a computer-readable storage medium storing instructions for use in the above-described computer terminal, which when executed, implement the above-described vascular reconstruction method.
In the several embodiments provided in the present invention, it should be understood that the disclosed apparatus and method may be implemented in other manners. The apparatus embodiments described above are merely illustrative, for example, of the flow diagrams and block diagrams in the figures, which illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s).
It should also be noted that, in alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, functional modules or units in various embodiments of the invention may be integrated together to form a single part, or the modules may exist alone, or two or more modules may be integrated to form a single part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a smart phone, a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The foregoing is merely illustrative of the present invention, and the present invention is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present invention.
Claims (10)
1. A method of revascularization, comprising:
acquiring at least one layer of ultrasonic blood flow signals and calculating the energy value of each sampling point in each layer of ultrasonic blood flow signals;
mapping all energy values into an energy matrix according to the sampling coordinates of the sampling points, and calculating spatial position information corresponding to the sampling points according to the sampling coordinates and a pre-calculated transformation matrix;
calculating the sampling coordinates of the mapping points corresponding to the sampling points in the ultrasonic blood flow signals of each layer according to the spatial position information of each sampling point and the inverse matrix of the transformation matrix;
judging whether the sampling coordinates of the mapping points fall in the subscript range corresponding to the energy matrix or not;
if the sampling coordinate falls in the subscript range, calculating the energy value corresponding to the mapping point in the energy matrix according to the energy value of the coordinate points with the nearest preset number to the sampling coordinate;
and reconstructing a blood flow model according to the energy values of all the sampling points and the mapping points.
2. The method of claim 1, wherein calculating the energy value of each sampling point in the ultrasound blood flow signal comprises:
and performing short-time Fourier transform on the ultrasonic blood flow signals to obtain energy values corresponding to each sampling point.
3. The vessel reconstruction method according to claim 1, wherein the calculation process of the transformation matrix includes:
determining sampling coordinates of a first sampling point, a second sampling point and a third sampling point in the first layer of ultrasonic blood flow signals according to the acquisition angle of the ultrasonic blood flow signals and the preset edge length of the ultrasonic blood flow signals;
calculating a first vector from the third sampling point to the first sampling point and a second vector from the third sampling point to the second sampling point according to the sampling coordinates;
and calculating the transformation matrix according to the first vector, the second vector, the distance between the equipment for acquiring the ultrasonic blood flow signals and the first layer ultrasonic blood flow signals and the layer number of the ultrasonic blood flow signals.
4. The method according to claim 1, wherein calculating the sampling coordinates of the mapping points corresponding to the sampling points in the ultrasound blood flow signals of each layer according to the spatial position information of each sampling point and the inverse matrix of the transformation matrix comprises:
taking the sampling coordinates of each sampling point as subscripts, and generating a spatial position matrix according to the spatial position information of the sampling points;
taking equipment coordinates for acquiring the ultrasonic blood flow signals as origin coordinates, and carrying out linear transformation on the space position matrix according to the origin coordinates so as to register the origins of the space position matrix;
traversing each sampling point in the registered space position matrix, and calculating the binary norm of each sampling point according to the space position information of the sampling point to obtain a unit vector of the sampling point;
multiplying the unit vector by the distance between each layer to obtain the spatial position information of the corresponding mapping point of the sampling point in the ultrasonic blood flow signal of each layer;
and multiplying the unit vectors corresponding to the spatial position information of all the mapping points by the inverse matrix of the transformation matrix to obtain the sampling coordinates of the mapping points.
5. The method according to claim 1, wherein calculating the energy value corresponding to the mapping point from the energy values of the coordinate points of the nearest preset number from the sampling coordinates in the energy matrix comprises:
searching a preset number of coordinate points closest to the sampling coordinates in the energy matrix and acquiring energy values of the preset number of coordinate points;
and carrying out interpolation operation on the energy values of the coordinate points to obtain the energy value corresponding to the mapping point.
6. The method of claim 3, wherein reconstructing a blood flow model from the energy values of the sampling points and the mapping points comprises:
filtering the energy values of each sampling point and each mapping point through a preset filtering threshold to obtain filtering energy values corresponding to each sampling point and each mapping point;
and taking the filtered energy value as a blood flow model matrix, performing Fourier inverse transformation on the blood flow model matrix to obtain a blood flow signal corresponding to the blood flow model matrix, and reconstructing a blood flow model according to the blood flow signal.
7. The method of revascularization defined in claim 6, further comprising:
and superposing the blood flow model into a pre-established standard brain blood vessel model according to the spatial position information of each sampling point and each mapping point in the blood flow model matrix.
8. A vascular reconstruction device, the device comprising:
the first energy value calculation module is used for acquiring at least one layer of ultrasonic blood flow signals and calculating the energy value of each sampling point in each layer of ultrasonic blood flow signals;
the spatial position information calculation module is used for mapping all energy values into an energy matrix according to the sampling coordinates of the sampling points and calculating spatial position information corresponding to the sampling points according to the sampling coordinates and a pre-calculated transformation matrix;
the sampling coordinate calculation module is used for calculating the sampling coordinates of the mapping points corresponding to the sampling points in the ultrasonic blood flow signals of each layer according to the spatial position information of each sampling point and the inverse matrix of the transformation matrix;
the judging module is used for judging whether the sampling coordinates of the mapping points fall in the subscript range corresponding to the energy matrix or not;
the second energy value calculation module is used for calculating the energy value corresponding to the mapping point in the energy matrix according to the energy value of the coordinate points with the nearest preset number from the sampling coordinate when the sampling coordinate falls in the subscript range;
and the blood flow model reconstruction module is used for reconstructing a blood flow model according to the energy values of all the sampling points and the mapping points.
9. A computer terminal, characterized in that the computer terminal comprises a memory for storing a computer program and a processor that runs the computer program to cause the computer terminal to perform the vascular reconstruction method according to any one of claims 1 to 7.
10. A computer readable storage medium, characterized in that the computer readable storage medium stores the computer program for use in the computer terminal according to claim 9, the computer program being for implementing the vascular reconstruction method according to any one of claims 1 to 7.
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