CN109171670B - 3D blood vessel imaging algorithm based on reverse principal component analysis method - Google Patents
3D blood vessel imaging algorithm based on reverse principal component analysis method Download PDFInfo
- Publication number
- CN109171670B CN109171670B CN201810658187.2A CN201810658187A CN109171670B CN 109171670 B CN109171670 B CN 109171670B CN 201810658187 A CN201810658187 A CN 201810658187A CN 109171670 B CN109171670 B CN 109171670B
- Authority
- CN
- China
- Prior art keywords
- blood flow
- algorithm
- component analysis
- image
- blood vessel
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
- A61B5/02007—Evaluating blood vessel condition, e.g. elasticity, compliance
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7203—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7225—Details of analog processing, e.g. isolation amplifier, gain or sensitivity adjustment, filtering, baseline or drift compensation
Landscapes
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Engineering & Computer Science (AREA)
- Signal Processing (AREA)
- Biophysics (AREA)
- General Health & Medical Sciences (AREA)
- Physiology (AREA)
- Pathology (AREA)
- Veterinary Medicine (AREA)
- Biomedical Technology (AREA)
- Heart & Thoracic Surgery (AREA)
- Medical Informatics (AREA)
- Molecular Biology (AREA)
- Surgery (AREA)
- Animal Behavior & Ethology (AREA)
- Physics & Mathematics (AREA)
- Public Health (AREA)
- Artificial Intelligence (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Psychiatry (AREA)
- Cardiology (AREA)
- Vascular Medicine (AREA)
- Power Engineering (AREA)
- Measuring Pulse, Heart Rate, Blood Pressure Or Blood Flow (AREA)
- Measurement Of The Respiration, Hearing Ability, Form, And Blood Characteristics Of Living Organisms (AREA)
Abstract
The invention provides a 3D blood vessel imaging algorithm based on a reverse principal component analysis method, which relates to the technical field of medical blood vessel imaging. Secondly, a complex signal statistical model is constructed, a reverse principal component analysis method is adopted to extract the vascular erythrocyte reflection signals in the complex signal statistical model, and a blood flow image is generated. And finally, obtaining an independent element axis of the blood flow image contour point cluster by adopting an independent component analysis model, calculating the translation amount and the rotation amount of the independent element axis by utilizing a differential search algorithm, and then registering the blood flow image. The technical scheme improves the signal-to-noise ratio of three-dimensional blood vessel imaging, reduces disordered background information generated by biological tissue reflection, improves the imaging image quality, and relieves the technical problems of low blood vessel imaging quality and serious noise in the prior art.
Description
Technical Field
The invention relates to the technical field of medical blood vessel imaging, in particular to a 3D blood vessel imaging algorithm based on a reverse principal component analysis method.
Background
Modern medicine considers that the pathological change state of biological tissues and the vascular morphology of related parts have close relation, wherein Optical Coherence Tomography (OCT) is a novel imaging technology and has the advantages of non-invasiveness, high resolution, non-invasiveness, high imaging depth and the like. However, in the early stage of pathological changes, the scattering characteristics of normal tissues and pathological changes are not obviously different, so that the OCT structural image cannot provide effective information to guide clinical treatment in time. Optical coherence tomography microvascular imaging (OCTA) is a technology for realizing microvascular imaging based on OCT, and can realize rapid, noninvasive, unmarked and high-resolution imaging and obtain three-dimensional angiography of tissue vessels. The OCTA technique has enabled the isolation of blood vessels from the microstructure of tissue, such as phase resolved optical Doppler tomography PRODT, which is based primarily on comparing the phase difference between adjacent A-scans within a B-scan of the OCTA signal, which is directly related to blood flow velocity.
Although now, PRODT is widely used, it is difficult to clearly observe capillary vessels in disease states with flow rates of 0.1-0.9 mm/s or lower due to its low sensitivity to blood flow. In order to increase its sensitivity, researchers have proposed some improved methods, such as using forward and backward B-scans, and using the phase variance between adjacent B-scans. Due to the relatively long time interval between adjacent B-scans (in the order of ms), this method enables imaging of capillaries with slow blood flow, and the phase-resolved optical doppler tomography method requires a long acquisition time (25min) and is very sensitive to object motion artifacts.
In the process of implementing the invention, the inventor finds that at least the following problems exist in the prior art: in the living body imaging process of the OCTA system, the imaging signal-to-noise ratio is reduced and the image quality is damaged due to the existence of inevitable biological jitter such as heartbeat, respiration and the like; in the OCTA data acquisition process, the imaging quality is reduced due to related noise caused by the position offset of adjacent light sequence scanning; in the reconstruction process of the OCTA image, the position deviation of two adjacent frames of images causes the jitter noise in the images. The blood flow imaging algorithm widely used at present has low sensitivity and cannot extract images of tiny blood vessels; existing blood flow image reconstruction algorithms do not efficiently remove cluttered background information caused by biological tissue reflections. Therefore, the prior art has the technical problems of low blood vessel imaging quality and serious noise.
Disclosure of Invention
In view of the above, the present invention provides a 3D vessel imaging algorithm based on an inverse principal component analysis method to alleviate the technical problems of low vessel imaging quality and serious noise in the prior art.
The embodiment of the invention provides a 3D blood vessel imaging algorithm based on a reverse principal component analysis method, which comprises the following steps:
a data acquisition step: adopting an electrocardio gate control method to collect electrocardio signals, outputting gate control signals according to the electrocardio signals and transmitting the gate control signals to a spectrometer, generating structural image data according to the output gate control signals, and registering the structural image data;
an image reconstruction step: constructing a complex signal statistical model according to the structural image data, wherein the complex signal statistical model is a linear statistical model comprising tissue reflection signals of non-blood flow tissue components, red blood cell reflection signals of blood flow components and Gaussian white noise; specifically, the complex signal statistical model is I ═ Ic+Ib+ N, where I is the tissue complex signal intensity matrix, IcIs a matrix of intensity of reflected signals from non-blood-flowing tissue, IbThe matrix is the intensity matrix of the blood vessel red cell reflection signal in the blood flow tissue, and N is a Gaussian white noise component;
extracting vascular erythrocyte reflected signals in the complex signal statistical model by adopting a reverse principal component analysis method, and calculating the characteristic value and the characteristic vector of the vascular erythrocyte reflected signals, wherein the extracted vascular erythrocyte reflected signals are IbGenerating a blood flow image, wherein h (w) is a filter signal response function constructed according to eigenvalues and eigenvectors of erythrocyte reflection signals;
3D image registration step: contour point cluster F for constructing any two blood flow imagesiThe contour point cluster is Fi＝[xi,yi,zi]TI ═ 1,2,3,. and n), contour point clusters F of the two blood flow imagesoiAnd FtiAre respectively represented as Foi＝xoie1+yoie2+zoie3And Fti＝xtie1+ytie2+ztie3(ii) a Wherein，FoiContour point clusters representing the blood flow image at the initial time, FtiRepresenting a contour point cluster of the blood flow image at the next moment;
obtaining a contour point cluster F by adopting an independent component analysis modeliThe independent component analysis model is an optimization algorithm model based on an objective function;
and calculating the translation amount and the rotation amount of the independent element axis by using a differential search algorithm, and registering the blood flow image according to the translation amount and the rotation amount.
Further, in the 3D blood vessel imaging algorithm based on the inverse principal component analysis method provided in the embodiment of the present invention, the electrocardiographic gating method specifically includes: and initializing the gate control module, acquiring an electrocardiosignal at the same time, comparing the electrocardiosignal serving as input data with a gate control value preset in the gate control module, judging whether the electrocardiosignal is higher than a threshold value, and if so, outputting the gate control signal corresponding to the current input electrocardiosignal.
Further, in the 3D blood vessel imaging algorithm based on the inverse principal component analysis method provided in the embodiment of the present invention, the acquiring of the electrocardiographic data by using the electrocardiographic gating method specifically further includes: and adding a sensitive displacement compensation model in the acquisition process, and calculating the sensitive motion displacement parameters of the sensitive displacement compensation model by using a frequency domain filtering method and a multi-mode searching method.
Further, in the 3D blood vessel imaging algorithm based on the inverse principal component analysis method provided in the embodiment of the present invention, after extracting the eigenvalue and eigenvector of the vascular red blood cell reflection signal in the complex signal statistical model by using the inverse principal component analysis method, the method further includes: and (3) refining the blood flow red blood cell reflection signals by adopting a superposition mean phase elimination method.
Further, in the 3D vessel imaging algorithm based on the inverse principal component analysis provided in the embodiment of the present invention, the registration of the structural image specifically uses a feature-based registration method to register the structural image, and the feature-based registration method includes: carrying out orthogonal transformation on the pseudo-difference matrix Q; in particular, Q ═ Ii-Ii+1Wherein, Ii、Ii+1Respectively pixel matrixes of two adjacent frame images.
Further, in the 3D blood vessel imaging algorithm based on the inverse principal component analysis method provided in the embodiment of the present invention, the optimization algorithm is any one of an artificial bee colony algorithm, an ant colony algorithm, a differential evolution algorithm, a bat algorithm, and a cuckoo algorithm.
Further, in the 3D blood vessel imaging algorithm based on the inverse principal component analysis method provided in the embodiment of the present invention, the objective function in the optimization algorithm model based on the objective function specifically is:
establishing F ═ AfSfDe-mixing matrix WfSatisfy Yf＝WfF＝WfAfSfWherein S isfIs a set of points, AfIs a point set matrix, F is a point set, YfIs FiEstimation of the separated independent components, WfI.e. the objective function.
Further, in the 3D blood vessel imaging algorithm based on the inverse principal component analysis method provided in the embodiment of the present invention, the calculation of the rotation amount and the translation amount corresponding to the independent axes specifically includes:
the rotation amount is:
translation quantity Δ C ═ Co-CtWherein l iso-first，lo-secondFirst and second axes, l, respectively, of the first blood flow structure imaget-first，lt-secondA first and a second component axis of a second image of the structural flow, respectively, wherein,
and respectively representing the centroids of the contour point models of the two blood flow structure images.
Further, in the 3D blood vessel imaging algorithm based on the inverse principal component analysis method provided by the embodiment of the present invention, the sensitive displacement compensation model is si(n)＝d(n)+ri(n)+ci(n)+hi(n) wherein si(n) is a sensitive motion displacement parameter, d (n) is a respiratory motion component, ri(n) is a cardiac motion component, ci(n) is a translationComponent of motion, hiAnd (n) is a noise component, and each component in the sensitive displacement compensation model is a function of the displacement along with the change of time.
Further, in the 3D blood vessel imaging algorithm based on the inverse principal component analysis method provided in the embodiment of the present invention, the expression of the superposition mean phase elimination method is as follows:
wherein, Ib_clearTo refine the intensity matrix of the blood flow information, Ib_iIntensity matrix of single frame blood flow information for the ith frame, Ib_meanThe matrix is the multi-frame average blood flow information intensity from the ith frame to the Mth frame, and M is the total number of B-scans.
The embodiment of the invention has the following beneficial effects: according to the 3D blood vessel imaging algorithm based on the reverse principal component analysis method, firstly, an electrocardio gating method is adopted for acquiring electrocardio signals, gating signals are output according to the electrocardio signals and transmitted to a spectrometer, structural image data are generated according to the output gating signals, and the structural image data are registered. Secondly, a complex signal statistical model is constructed according to the structural image data, wherein the complex signal statistical model is a linear statistical model comprising tissue reflection signals of non-blood flow tissue components, red blood cell reflection signals of blood flow components and Gaussian white noise. And extracting the vascular erythrocyte reflection signals in the complex signal statistical model by adopting a reverse principal component analysis method, calculating the characteristic values and the characteristic vectors of the vascular erythrocyte reflection signals, extracting the vascular erythrocyte reflection signals, and generating a blood flow image. Finally, constructing contour point clusters of any two blood flow images, and obtaining independent element axes of the contour point clusters by adopting an independent component analysis model, wherein the independent component analysis model is an optimization algorithm model based on a target function; and calculating the translation amount and the rotation amount of the independent element axis by using a differential search algorithm, and registering the blood flow image according to the translation amount and the rotation amount. According to the technical scheme, an electrocardio-gating method is adopted for data acquisition, a complex signal statistical model and a reverse principal component analysis method are combined to generate a blood flow image, and finally 3D image registration is carried out on the blood flow image, so that three-dimensional angiography of tissue blood vessels is realized, the signal to noise ratio of imaging is improved, disordered background information generated by biological tissue reflection is reduced, the influence of biological jitter is weakened, and the quality of the imaging image is improved, so that the technical problems of low blood vessel imaging quality and serious noise in the prior art are solved.
In order to make the aforementioned and other objects, features and advantages of the present invention comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a flowchart of a 3D blood vessel imaging algorithm based on inverse principal component analysis provided in an embodiment of the present invention;
fig. 2 is an effect diagram before an electrocardiographic gating method is adopted in the 3D blood vessel imaging algorithm based on the inverse principal component analysis method provided by the embodiment of the present invention;
fig. 3 is a diagram of an effect obtained after an electrocardiographic gating method is adopted in a 3D blood vessel imaging algorithm based on an inverse principal component analysis method according to an embodiment of the present invention;
fig. 4 is a schematic diagram of a structural image registration effect in a 3D blood vessel imaging algorithm based on an inverse principal component analysis method according to an embodiment of the present invention.
Detailed Description
To make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
At present, in the living body imaging process of an OCTA system, the imaging signal-to-noise ratio is reduced and the image quality is damaged due to inevitable biological jitter such as heartbeat, respiration and the like; in the OCTA data acquisition process, the imaging quality is reduced due to related noise caused by the position offset of adjacent light sequence scanning; in the reconstruction process of the OCTA image, the position deviation of two adjacent frames of images causes the jitter noise in the images. The blood flow imaging algorithm widely used at present has low sensitivity and cannot extract images of tiny blood vessels; based on the fact that the existing blood flow image reconstruction algorithm cannot efficiently remove the disordered background information caused by biological tissue reflection, the 3D blood vessel imaging algorithm based on the reverse principal component analysis method provided by the embodiment of the invention can improve the signal-to-noise ratio of three-dimensional blood vessel imaging, reduce the disordered background information generated by biological tissue reflection and improve the imaging image quality.
Referring to fig. 1, a flowchart of a 3D blood vessel imaging algorithm based on an inverse principal component analysis method according to an embodiment of the present invention is provided. The 3D blood vessel imaging algorithm based on the reverse principal component analysis method provided by the embodiment of the invention comprises the following steps:
a data acquisition step S100: the method comprises the steps of adopting an electrocardio-gating method to collect electrocardiosignals, outputting gating signals according to the electrocardiosignals and transmitting the gating signals to a spectrometer, generating structural image data according to the output gating signals, and registering the structural image data. In the data acquisition process, because the imaging quality is influenced by the heartbeat and the artifact caused by respiratory motion, the electrocardio signal is acquired while the sample data is acquired by adopting an electrocardio gate control mode in the data acquisition process, so that the purpose of removing the imaging artifact caused by the heartbeat and respiratory motion is achieved.
Further, in the 3D blood vessel imaging algorithm based on the inverse principal component analysis method provided in the embodiment of the present invention, the electrocardiographic gating method specifically includes: the gating module is initialized, the electrocardiosignal acquisition module acquires electrocardiosignals at the same time, the electrocardiosignal comparison module compares the electrocardiosignals serving as input data with a gating value preset in the gating module, whether the electrocardiosignals are higher than a threshold value or not is judged, and if yes, gating signals corresponding to the currently input electrocardiosignals are output. Referring to fig. 2 and fig. 3, in the 3D blood vessel imaging algorithm based on the inverse principal component analysis method provided in the embodiment of the present invention, comparison graphs of the effects before and after the cardiac gating method are used.
Further, in the 3D blood vessel imaging algorithm based on the inverse principal component analysis method provided in the embodiment of the present invention, the acquiring of the electrocardiographic data by using the electrocardiographic gating method specifically further includes: and adding a sensitive displacement compensation model in the acquisition process, and calculating the sensitive motion displacement parameters of the sensitive displacement compensation model by using a frequency domain filtering method and a multi-mode searching method. In special cases, for example, some patients have some diseases on their hearts, which results in the heart beating not being periodic, or some non-periodic sensitive movements caused by the patients being young children, the imaging quality is seriously affected, and at this time, the compensation for the non-periodic sensitive displacement can be realized within a certain range by adding a sensitive movement displacement model in the data acquisition process.
Further, in the 3D blood vessel imaging algorithm based on the inverse principal component analysis method provided by the embodiment of the present invention, the sensitive displacement compensation model is si(n)＝d(n)+ri(n)+ci(n)+hi(n) wherein si(n) is a sensitive motion displacement parameter, d (n) is a respiratory motion component, ri(n) is a cardiac motion component, ci(n) is the translational motion component, hiAnd (n) is a noise component, and each component in the sensitive displacement compensation model is a function of the displacement of the corresponding parameter of the sensitive displacement compensation model along with the change of time.
Referring to fig. 4, in the 3D blood vessel imaging algorithm based on the inverse principal component analysis method provided by the embodiment of the present invention, a schematic diagram of a registration effect of a structural image is shown. Furthermore, in the 3D blood vessel imaging algorithm based on the inverse principal component analysis method provided in the embodiment of the present invention, in the data acquisition step, the feature-based registration method is specifically adopted to register the structured image, so as to compensate the position offset between two adjacent B-scans, and remove the artifact, i.e., the noise part, caused by the jitter.The feature-based registration method comprises: carrying out orthogonal transformation on the pseudo-difference matrix Q; in particular, Q ═ Ii-Ii+1Wherein, Ii、Ii+1Respectively pixel matrixes of two adjacent frame structure images. Furthermore, after the step of registering the structural image, a zero padding technology can be added, so that the aim of increasing the registration precision of the structural image is fulfilled.
The 3D blood vessel imaging algorithm based on the reverse principal component analysis method provided by the embodiment of the invention further comprises an image reconstruction step S200, wherein the step S210: constructing a complex signal statistical model of a certain point in a spatial domain according to the structural image data, wherein the complex signal statistical model can ensure the integrity of signals, and the complex signal statistical model is a linear statistical model comprising tissue reflection signals of non-blood flow tissue components, red blood cell reflection signals of blood flow components and additional Gaussian white noise; specifically, the complex signal statistical model is I ═ Ic+Ib+ N, where I is the tissue complex signal intensity matrix, IcIs a matrix of intensity of reflected signals from non-blood-flowing tissue, IbThe intensity matrix of the blood vessel red cell reflection signal in the blood flow tissue, and N is a Gaussian white noise component.
Step S220: extracting a vascular erythrocyte reflection signal in a complex signal statistical model by adopting a reverse principal component analysis method, wherein the vascular erythrocyte reflection signal represents the reflection degree of blood vessels in tissues, the characteristic value and the characteristic vector of the vascular erythrocyte reflection signal are calculated as principal components in the complex signal statistical model, and the extracted vascular erythrocyte reflection signal is IbAnd designing a filtering function according to the characteristic value and the characteristic vector of the blood vessel erythrocyte reflection signal, further designing a PCA inverse filter, filtering non-blood flow tissue components, reserving blood flow tissue components, and generating a blood flow image, wherein H (w) is a filtering signal response function constructed according to the characteristic value and the characteristic vector of the erythrocyte reflection signal.
Further, in the 3D blood vessel imaging algorithm based on the inverse principal component analysis method provided in the embodiment of the present invention, after extracting the eigenvalue and eigenvector of the vascular red blood cell reflection signal in the complex signal statistical model by using the inverse principal component analysis method, the method further includes: and (3) refining the blood flow red blood cell reflection signals by adopting a superposition mean phase elimination method.
Further, in the 3D blood vessel imaging algorithm based on the inverse principal component analysis method provided in the embodiment of the present invention, the expression of the superposition mean phase elimination method is as follows:
wherein, Ib_clearTo refine the intensity matrix of the blood flow information, Ib_iIntensity matrix of single frame blood flow information for the ith frame, Ib_meanThe matrix is the multi-frame average blood flow information intensity from the ith frame to the Mth frame, and M is the total number of B-scans.
The 3D blood vessel imaging algorithm based on the reverse principal component analysis method further comprises a 3D image registration step S300, the 3D image registration technology can effectively calculate the offset and the rotation amount of the acquired images at different time, and reference is provided for doctors to detect the preoperative and postoperative changes of the lesion position, the state of illness and other related aspects. Wherein, in step S310: contour point cluster F for constructing any two blood flow imagesiThe contour point cluster is Fi＝[xi,yi,zi]TN in the formula is a point set of contour points, the contour point cluster data represents position information of the contour points, and contour point clusters F of the two blood flow imagesoiAnd FtiAre respectively represented as Foi＝xoie1+yoie2+zoie3And Fti＝xtie1+ytie2+ztie3(ii) a Wherein, FoiContour point clusters representing the blood flow image at the initial time, FtiRepresenting a contour point cluster of the blood flow image at the next moment; wherein e is1、e2、e3Respectively, unit direction vectors.
Step S320: contour point cluster F is obtained by adopting Independent Component Analysis (ICA) model in unsupervised learningiThe independent component analysis model is an optimization algorithm model based on an objective function. Further, the embodiment of the invention provides a method based on reverse principal componentIn the 3D blood vessel imaging algorithm of the analysis method, the optimization algorithm is any one of an artificial bee colony algorithm, an ant colony algorithm, a differential evolution algorithm, a bat algorithm and a cuckoo algorithm.
Further, in the 3D blood vessel imaging algorithm based on the inverse principal component analysis method provided in the embodiment of the present invention, the objective function in the optimization algorithm model based on the objective function specifically is: establishing F ═ AfSfSpecifically, according to the ICA algorithm principle, there is a set of points SfAnd a point set matrix AfSatisfy F ═ AfSfThe goal of the ICA algorithm is to find a demixing matrix WfSatisfy Yf＝WfF＝WfAfSfWherein S isfIs a set of points, AfIs a point set matrix, F is a point set, YfIs FiEstimation of the separated independent components, WfNamely the objective function in the optimization algorithm model based on the objective function.
Step S330: and calculating the translation amount and the rotation amount of the independent element axis in the maximum statistical correlation direction by using a differential search algorithm, and registering the blood flow image according to the translation amount and the rotation amount. Further, in the 3D blood vessel imaging algorithm based on the inverse principal component analysis method provided in the embodiment of the present invention, the calculation of the rotation amount and the translation amount corresponding to the independent axes specifically includes:
the rotation amount is:
translation quantity Δ C ═ Co-CtWherein l iso-first，lo-secondFirst and second axes, l, respectively, of the first blood flow structure imaget-first，lt-secondA first and a second component axis of a second image of the structural flow, respectively, wherein,
and respectively representing the centroids of the contour point models of the two blood flow structure images.
According to the 3D blood vessel imaging algorithm based on the reverse principal component analysis method, firstly, an electrocardio gating method is adopted for acquiring electrocardio signals, gating signals are output according to the electrocardio signals and transmitted to a spectrometer, structural image data are generated according to the output gating signals, and the structural image data are registered. Secondly, a complex signal statistical model is constructed according to the structural image data, wherein the complex signal statistical model is a linear statistical model comprising tissue reflection signals of non-blood flow tissue components, red blood cell reflection signals of blood flow components and Gaussian white noise. And extracting the vascular erythrocyte reflection signals in the complex signal statistical model by adopting a reverse principal component analysis method, calculating the characteristic values and the characteristic vectors of the vascular erythrocyte reflection signals, extracting the vascular erythrocyte reflection signals, and generating a blood flow image. Finally, constructing contour point clusters of any two blood flow images, and obtaining independent element axes of the contour point clusters by adopting an independent component analysis model, wherein the independent component analysis model is an optimization algorithm model based on a target function; and calculating the translation amount and the rotation amount of the independent element axis by using a differential search algorithm, and registering the blood flow image according to the translation amount and the rotation amount. According to the technical scheme, an electrocardio-gating method is adopted for data acquisition, a complex signal statistical model and a reverse principal component analysis method are combined to generate a blood flow image, and finally 3D image registration is carried out on the blood flow image, so that three-dimensional angiography of tissue blood vessels is realized, the signal to noise ratio of imaging is improved, disordered background information generated by biological tissue reflection is reduced, the influence of biological jitter is weakened, and the quality of the imaging image is improved, so that the technical problems of low blood vessel imaging quality and serious noise in the prior art are solved.
The above-mentioned embodiments are only specific embodiments of the present invention, which are used for illustrating the technical solutions of the present invention and not for limiting the same, and the protection scope of the present invention is not limited thereto, although the present invention is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: any person skilled in the art can modify or easily conceive the technical solutions described in the foregoing embodiments or equivalent substitutes for some technical features within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the embodiments of the present invention, and they should be construed as being included therein. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (10)
1. A3D blood vessel imaging algorithm based on an inverse principal component analysis method is characterized by comprising the following steps:
a data acquisition step: adopting an electrocardio gate control method to collect electrocardio signals, outputting gate control signals according to the electrocardio signals and transmitting the gate control signals to a spectrometer, generating structural image data according to the output gate control signals, and registering the structural image data;
an image reconstruction step: constructing a complex signal statistical model according to the structural image data, wherein the complex signal statistical model is a linear statistical model comprising tissue reflection signals of non-blood flow tissue components, red blood cell reflection signals of blood flow components and Gaussian white noise; specifically, the complex signal statistical model is I ═ Ic+Ib+ N, where I is the tissue complex signal intensity matrix, IcIs a matrix of intensity of reflected signals from non-blood-flowing tissue, IbThe matrix is the intensity matrix of the blood vessel red cell reflection signal in the blood flow tissue, and N is a Gaussian white noise component;
extracting the vascular erythrocyte reflection signal in the complex signal statistical model by adopting a reverse principal component analysis method, and calculating the characteristic value and the characteristic vector of the vascular erythrocyte reflection signal, wherein the extracted vascular erythrocyte reflection signal is IbGenerating a blood flow image, wherein h (w) is a filter signal response function constructed according to eigenvalues and eigenvectors of vascular erythrocyte reflection signals;
3D image registration step: contour point cluster F for constructing any two blood flow imagesiThe contour point cluster is Fi＝[xi,yi,zi]TI ═ 1,2,3,. and n), contour point clusters F of the two blood flow imagesoiAnd FtiAre respectively represented as Foi＝xoie1+yoie2+zoie3And Fti＝xtie1+ytie2+ztie3(ii) a Wherein, FoiContour point clusters representing the blood flow image at the initial time, FtiRepresenting a contour point cluster of the blood flow image at the next moment;
obtaining a contour point cluster F by adopting an independent component analysis modeliThe independent component analysis model is an optimization algorithm model based on an objective function;
and calculating the translation amount and the rotation amount of the independent element axis by using a differential search algorithm, and registering the blood flow image according to the translation amount and the rotation amount.
2. The algorithm according to claim 1, wherein the electrocardiographic gating method is specifically: and initializing the gate control module, acquiring an electrocardiosignal at the same time, comparing the electrocardiosignal serving as input data with a gate control value preset in the gate control module, judging whether the electrocardiosignal is higher than a threshold value, and if so, outputting the gate control signal corresponding to the current input electrocardiosignal.
3. The algorithm of claim 1, wherein the acquiring of the electrocardiographic data by the electrocardiographic gating method further comprises: and adding a sensitive displacement compensation model in the acquisition process, and calculating the sensitive motion displacement parameters of the sensitive displacement compensation model by using a frequency domain filtering method and a multi-mode searching method.
4. The algorithm according to claim 1, further comprising, after the extracting the eigenvalues and eigenvectors of the vascular red cell reflectance signal in the complex signal statistical model by inverse principal component analysis: and (3) refining the blood flow red blood cell reflection signals by adopting a superposition mean phase elimination method.
5. The algorithm of claim 1, wherein the registering the structural image is specific toRegistering the structural image by using a characteristic-based registration method, wherein the characteristic-based registration method comprises the following steps: carrying out orthogonal transformation on the pseudo-difference matrix Q; in particular, Q ═ Ii-Ii+1Wherein, Ii、Ii+1Respectively pixel matrixes of two adjacent frame images.
6. The algorithm of claim 1, wherein the optimization algorithm is any one of an artificial bee colony algorithm, an ant colony algorithm, a differential evolution algorithm, a bat algorithm, and a cuckoo algorithm.
7. The algorithm according to claim 1, wherein the objective function in the objective function-based optimization algorithm model is specifically:
establishing F ═ AfSfDe-mixing matrix WfSatisfy Yf＝WfF＝WfAfSfWherein S isfIs a set of points, AfIs a point set matrix, F is a point set, YfIs FiEstimation of the separated independent components, WfI.e. the objective function.
8. The algorithm of claim 1, wherein the calculating the amount of rotation and translation corresponding to the independent axes is embodied as:
the rotation amount is:
translation quantity Δ C ═ Co-CtWherein l iso-first，lo-secondFirst and second axes, l, respectively, of the first blood flow structure imaget-first，lt-secondA first and a second component axis of a second image of the structural flow, respectively, wherein,
and respectively representing the centroids of the contour point models of the two blood flow structure images.
9. The algorithm of claim 3, wherein the sensitivity displacement compensation model is si(n)＝d(n)+ri(n)+ci(n)+hi(n) wherein si(n) is a sensitive motion displacement parameter, d (n) is a respiratory motion component, ri(n) is a cardiac motion component, ci(n) is the translational motion component, hiAnd (n) is a noise component, and each component in the sensitive displacement compensation model is a function of the displacement along with the change of time.
10. The algorithm of claim 4, wherein the additive mean subtraction expression is:
wherein, Ib_clearTo refine the intensity matrix of the blood flow information, Ib_iIntensity matrix of single frame blood flow information for the ith frame, Ib_meanThe matrix is the multi-frame average blood flow information intensity from the ith frame to the Mth frame, and M is the total number of B-scans.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810658187.2A CN109171670B (en) | 2018-06-25 | 2018-06-25 | 3D blood vessel imaging algorithm based on reverse principal component analysis method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810658187.2A CN109171670B (en) | 2018-06-25 | 2018-06-25 | 3D blood vessel imaging algorithm based on reverse principal component analysis method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109171670A CN109171670A (en) | 2019-01-11 |
| CN109171670B true CN109171670B (en) | 2021-02-05 |
Family
ID=64948449
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810658187.2A Active CN109171670B (en) | 2018-06-25 | 2018-06-25 | 3D blood vessel imaging algorithm based on reverse principal component analysis method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109171670B (en) |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111145092A (en) * | 2019-12-16 | 2020-05-12 | 华中科技大学鄂州工业技术研究院 | Method and device for processing infrared blood vessel image on leg surface |
| CN111493853A (en) * | 2020-04-24 | 2020-08-07 | 天津恒宇医疗科技有限公司 | Blood vessel parameter evaluation method and system for angiodermic diseases |
| CN116563414B (en) * | 2023-07-11 | 2023-09-12 | 天津博霆光电技术有限公司 | OCT-based cardiovascular imaging fibrillation shadow eliminating method and equipment |
Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101246543A (en) * | 2008-03-18 | 2008-08-20 | 苏州纳米技术与纳米仿生研究所 | Examiner identity appraising system based on bionic and biological characteristic recognition |
| CN102077108A (en) * | 2008-04-28 | 2011-05-25 | 康奈尔大学 | Tool for accurate quantification in molecular mri |
| CN103271734A (en) * | 2012-12-10 | 2013-09-04 | 中国人民解放军第一五二中心医院 | Heart rate measuring method based on low-end imaging device |
| CN103679816A (en) * | 2013-12-30 | 2014-03-26 | 北京师范大学 | Criminology-oriented computer-assisted facial reconstruction method for skulls of unknown body sources |
| CN104299216A (en) * | 2014-10-22 | 2015-01-21 | 北京航空航天大学 | Multimodality medical image fusion method based on multiscale anisotropic decomposition and low rank analysis |
| CN105662413A (en) * | 2015-12-31 | 2016-06-15 | 深圳先进技术研究院 | Myocardium T1 quantifying method and device |
| CN106021912A (en) * | 2016-05-17 | 2016-10-12 | 武汉盛世康和健康管理有限公司 | Remote online health management system |
| CN106166058A (en) * | 2016-08-04 | 2016-11-30 | 温州医科大学 | One is applied to optical coherence tomography blood vessel imaging method and OCT system |
| CN206209898U (en) * | 2016-12-06 | 2017-05-31 | 中国科学院深圳先进技术研究院 | Three-dimensional cardiac image re-construction system |
| CN107485366A (en) * | 2017-09-07 | 2017-12-19 | 天津海仁医疗技术有限公司 | A kind of optics Micro flow contrast imaging method based on microvesicle enhancing |
| CN107578381A (en) * | 2017-08-09 | 2018-01-12 | 天津恒宇医疗科技有限公司 | Interference of light fault imaging Color Mapping Approach is peeped in one kind |
| CN107595250A (en) * | 2017-09-30 | 2018-01-19 | 浙江大学 | The blood flow imaging method and system of contrast is mixed with figure based on motion |
| CN107680052A (en) * | 2017-09-18 | 2018-02-09 | 广州慧扬健康科技有限公司 | Angiographic image blood vessel strengthening system based on PCA |
| CN108042125A (en) * | 2017-05-27 | 2018-05-18 | 天津海仁医疗技术有限公司 | A kind of high speed endoscopic optical coherent flow imaging system |
| WO2018094381A1 (en) * | 2016-11-21 | 2018-05-24 | Tecumseh Vision, Llc | System and method for automatic assessment of disease condition using oct scan data |
Family Cites Families (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2012166840A2 (en) * | 2011-06-01 | 2012-12-06 | The Board Of Trustees Of The Leland Stanford Junior University | Learning of image processing pipeline for digital imaging devices |
| CN103077550B (en) * | 2012-12-10 | 2016-04-20 | 华北电力大学（保定） | A kind of four dimensional rebuilding method of non-gate ICUS image sequence medium vessels |
| CN104835151A (en) * | 2015-04-24 | 2015-08-12 | 南京邮电大学 | Improved artificial bee colony algorithm-based image registration method |
| US10909675B2 (en) * | 2015-10-09 | 2021-02-02 | Mayo Foundation For Medical Education And Research | System and method for tissue characterization based on texture information using multi-parametric MRI |
| CN108113647A (en) * | 2016-11-28 | 2018-06-05 | 深圳先进技术研究院 | A kind of electrocardiosignal sorter and method |
| CN107295217B (en) * | 2017-06-30 | 2020-06-12 | 中原智慧城市设计研究院有限公司 | Video noise estimation method based on principal component analysis |
-
2018
- 2018-06-25 CN CN201810658187.2A patent/CN109171670B/en active Active
Patent Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101246543A (en) * | 2008-03-18 | 2008-08-20 | 苏州纳米技术与纳米仿生研究所 | Examiner identity appraising system based on bionic and biological characteristic recognition |
| CN102077108A (en) * | 2008-04-28 | 2011-05-25 | 康奈尔大学 | Tool for accurate quantification in molecular mri |
| CN103271734A (en) * | 2012-12-10 | 2013-09-04 | 中国人民解放军第一五二中心医院 | Heart rate measuring method based on low-end imaging device |
| CN103679816A (en) * | 2013-12-30 | 2014-03-26 | 北京师范大学 | Criminology-oriented computer-assisted facial reconstruction method for skulls of unknown body sources |
| CN104299216A (en) * | 2014-10-22 | 2015-01-21 | 北京航空航天大学 | Multimodality medical image fusion method based on multiscale anisotropic decomposition and low rank analysis |
| CN105662413A (en) * | 2015-12-31 | 2016-06-15 | 深圳先进技术研究院 | Myocardium T1 quantifying method and device |
| CN106021912A (en) * | 2016-05-17 | 2016-10-12 | 武汉盛世康和健康管理有限公司 | Remote online health management system |
| CN106166058A (en) * | 2016-08-04 | 2016-11-30 | 温州医科大学 | One is applied to optical coherence tomography blood vessel imaging method and OCT system |
| WO2018094381A1 (en) * | 2016-11-21 | 2018-05-24 | Tecumseh Vision, Llc | System and method for automatic assessment of disease condition using oct scan data |
| CN206209898U (en) * | 2016-12-06 | 2017-05-31 | 中国科学院深圳先进技术研究院 | Three-dimensional cardiac image re-construction system |
| CN108042125A (en) * | 2017-05-27 | 2018-05-18 | 天津海仁医疗技术有限公司 | A kind of high speed endoscopic optical coherent flow imaging system |
| CN107578381A (en) * | 2017-08-09 | 2018-01-12 | 天津恒宇医疗科技有限公司 | Interference of light fault imaging Color Mapping Approach is peeped in one kind |
| CN107485366A (en) * | 2017-09-07 | 2017-12-19 | 天津海仁医疗技术有限公司 | A kind of optics Micro flow contrast imaging method based on microvesicle enhancing |
| CN107680052A (en) * | 2017-09-18 | 2018-02-09 | 广州慧扬健康科技有限公司 | Angiographic image blood vessel strengthening system based on PCA |
| CN107595250A (en) * | 2017-09-30 | 2018-01-19 | 浙江大学 | The blood flow imaging method and system of contrast is mixed with figure based on motion |
Non-Patent Citations (2)
| Title |
|---|
| "Extracting contrast-filled vessels in X-ray angiography by graduated RPCA with motion coherency constraint";Jin等;《PATTERN RECOGNITION》;20170331;第63卷;第653-666页 * |
| "超微血管成像技术鉴别诊断乳腺肿瘤";王希等;《中国医学影像技术》;20160531(第2016年05期);第659-662页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109171670A (en) | 2019-01-11 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11935241B2 (en) | Image processing apparatus, image processing method and computer-readable medium for improving image quality | |
| Suhling et al. | Myocardial motion analysis from B-mode echocardiograms | |
| Das et al. | Unsupervised super-resolution of OCT images using generative adversarial network for improved age-related macular degeneration diagnosis | |
| CN109171670B (en) | 3D blood vessel imaging algorithm based on reverse principal component analysis method | |
| CN112601487A (en) | Medical image processing apparatus, medical image processing method, and program | |
| Kassinopoulos et al. | Identification of physiological response functions to correct for fluctuations in resting-state fMRI related to heart rate and respiration | |
| EP2512325A2 (en) | Processing physiological sensor data using a physiological model combined with a probabilistic processor | |
| CN108618749B (en) | Retina blood vessel three-dimensional reconstruction method based on portable digital fundus camera | |
| CN107862724B (en) | Improved microvascular blood flow imaging method | |
| CN111275755B (en) | Mitral valve orifice area detection method, system and equipment based on artificial intelligence | |
| CN114748032A (en) | Motion noise compensation method based on OCT blood vessel imaging technology | |
| CN113543695A (en) | Image processing apparatus, image processing method, and program | |
| Hu et al. | LIFE: a generalizable autodidactic pipeline for 3D OCT-A vessel segmentation | |
| CN115587971A (en) | Method and system for monitoring body reaction and hemodynamics based on heart ultrasonic segmental motion | |
| CN110236544B (en) | Stroke perfusion imaging lesion area detection system and method based on correlation coefficient | |
| CN113850804A (en) | Retina image generation system and method based on generation countermeasure network | |
| Hennersperger et al. | Vascular 3D+ T freehand ultrasound using correlation of doppler and pulse-oximetry data | |
| CN116138760A (en) | Self-adaptive enhanced laser speckle contrast blood flow imaging method | |
| WO2023178078A1 (en) | Oct speckle velocimetry | |
| CN115869012A (en) | Ultrasonic imaging method, system and computer readable storage medium | |
| WO2015188279A1 (en) | Non-invasive measurement of choroidal volume change and ocular rigidity through oct imaging | |
| CN111292285A (en) | Automatic screening method for diabetes mellitus based on naive Bayes and support vector machine | |
| Sigit et al. | Heart video tracking system on long axis view | |
| Lassige et al. | Comparison of septal defects in 2D and 3D echocardiography using active contour models | |
| Chen et al. | A novel algorithm for refining cerebral vascular measurements in infants and adults |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20211201 Address after: East area, 3rd floor, No.9 plant, xibadao, Tianjin Binhai New Area pilot free trade zone (Airport Economic Zone) Patentee after: TIANJIN HENGYU MEDICAL TECHNOLOGY Co.,Ltd. Address before: 300457 West Area C, 2nd floor, No.1 Workshop, No.9 West 8th Road, Tianjin pilot free trade zone (Airport Economic Zone), Binhai New Area, Tianjin Patentee before: TIANJIN HAIREN MEDICAL TECHNOLOGY Co.,Ltd. Patentee before: Tianjin Hengyu Medical Technology Co., Ltd |
|
| TR01 | Transfer of patent right |