CN110946618A - Elastic imaging method based on empirical manifold - Google Patents

Elastic imaging method based on empirical manifold Download PDF

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CN110946618A
CN110946618A CN201911160904.XA CN201911160904A CN110946618A CN 110946618 A CN110946618 A CN 110946618A CN 201911160904 A CN201911160904 A CN 201911160904A CN 110946618 A CN110946618 A CN 110946618A
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陈亚媛
林春漪
金连文
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South China University of Technology SCUT
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Abstract

The invention discloses an elastic imaging method based on empirical manifold, which comprises the following steps: 1) segmenting the RF signal acquired after compressing the tissue, and determining the segment length and the segment distance according to the requirements of resolution and smoothness; 2) selecting a segment of the pre-compression signal, determining the search range and the search step length of the corresponding p post-compression signals, and acquiring the post-compression segmented signals by using a linear interpolation method; 3) calculating the complexity between signal windows after lamination by using the residual complexity RC, calculating the similarity between the signal layers after lamination by using the complexity RC, and using the similarity as the boundary weight of the graph; 4) solving the shortest path of each window in the pre-compression organization and post-compression searching range; 5) and outputting a displacement estimation graph, fitting a curve by using a least square method to obtain strain estimation, and reconstructing an elasticity graph of the tissue by combining distribution conditions. The method can further improve the accuracy of the displacement estimation calculation method in the quasi-static elastography, thereby improving the quality of the finally obtained elastogram.

Description

Elastic imaging method based on empirical manifold
Technical Field
The invention relates to the field of medical elastography, in particular to an elastography method based on empirical manifold.
Background
Elastography is a non-invasive method that uses images of the stiffness or strain of soft tissue to detect or classify tumors. Elastography has been applied to several imaging modalities, such as ultrasound, Magnetic Resonance Elastography (MRE) and computed tomography, and has shown great utility in medical applications. Elastography is that acquired elasticity information of biological materials is converted into visible light images used by doctors, so that doctors can judge the mechanical properties of the materials of tissues through the visible light images, and further judge the possible pathological changes of corresponding tissues or organs and the positions, shapes and sizes of the tissues or organs according to the soft and hard conditions of the tissues.
From the quasi-static elastography technology to date, scholars at home and abroad research and propose a plurality of elastography technologies. According to the core technology, the quasi-static elastography algorithm is divided into time-domain-based and frequency-domain-based elastography algorithms. Studies by british scholars t.shiina, j.c.bamer and m.m.doyley et al in 1996 found and published documents [ Shiina T, Doyley M, Bamber J c.serial imaging combined RF and innovative correlation processing [ C ]// Ultrasonics symposium. ieee,1996 ], indicating that the implementation of both autocorrelation mixing algorithms and delay algorithms depend on the pulse duration or bandwidth of the echo signal. In the case of low signal-to-noise ratio, the autocorrelation mixing algorithm has higher robustness. A.Pesavento, C.Perrey and H.Ermert equal 1999 propose a fast displacement estimation algorithm document [ Pesavento A.A time-efficiency and acquisition evaluation contract for an adaptive displacement using evaluation [ J ]. IEEE Transactions on Ultrasonics Ferroelectrics & frequency control 1999,46(5): 1057-.
A fast shift estimation algorithm was proposed in 1999, which takes the time offset of the window signal into account in the shift estimation in order to solve the problem of phase overlap. Furthermore, they propose a processing method for the envelope signal that achieves an effective reduction of the decorrelation noise without introducing systematic errors by means of a logarithmic compression of the envelope signal.
The first frequency-domain based elastography techniques were spectral centroid shift estimation algorithms, first proposed in 1999 by Konofagou and J.Ophir et al in the literature [ Konofagou E, Varghese T, Ophir J, et al, Power spectral strain estimators in elastomers [ J ]. Ul-transsound in Medicine & biology 1999,25(7):1115 and 1129 ]. This method has proven to be more accurate and robust than conventional time-domain based cross-correlation algorithms. In 2000, Varghese et al in the literature [ Varghese T, Konofagou E, OphirJ, et al direct strain estimation in engineering using specific cross-correlation [ J ]. ultrasounds in Medicine & biology 2000,26(9):1525-1537 ] proposed the use of frequency domain cross-correlation algorithm to directly estimate the strain of tissue, called spectral cross-correlation algorithm. This method was demonstrated to yield a good quality elastogram at a small compression scale. In 2004, Alam, S.K et al [ S Kaisar A, Lizzi F L, TomyV, et al. adaptive spectral estimators for elastic imaging [ J ]. Ultrasonicim. 2004, (26) (3):131-149.] propose an adaptive spectral strain estimation algorithm, which combines the advantages of time-domain-based and spectral-based methods and can achieve the purpose of improving the calculation effect by performing frequency adjustment through iteration.
By analyzing the existing quasi-static medical ultrasound elastography technology, we find that the technical field still faces many challenges, such as: significant error points due to incorrect matching windows, real-time problems with elastography, etc. Aiming at the problems, the invention introduces the characteristics of the graph in the empirical manifold learning, so that the calculation is more efficient, and a more stable similarity result can be obtained.
Disclosure of Invention
The invention aims to optimize the calculation of the displacement of a pre-compression signal and a post-compression signal in the elastography process.
The invention is realized by at least one of the following technical schemes.
An elastography method based on empirical manifold, comprising the steps of:
1) segmenting an RF (ultrasonic radio frequency signal) signal acquired after tissue compression, and determining segment length and segment spacing according to the requirements of resolution and smoothness;
2) selecting a segment of the pre-compression signal, determining the search range and the search step length of the corresponding p post-compression signals, and acquiring the post-compression segmented signals by using a linear interpolation method;
3) calculating the complexity between signal windows after lamination by using the residual complexity RC, calculating the similarity between the signal layers after lamination by using the complexity RC, and using the similarity as the boundary weight of the graph;
4) solving the shortest path of each window in the pre-compression organization and post-compression searching range;
5) and outputting a displacement estimation diagram, fitting a curve by using a least square method to obtain strain estimation, and reconstructing elastic modulus distribution of the tissue distribution, namely an elastic diagram of the tissue by combining distribution conditions after obtaining the strain distribution of the tissue.
Further, step 1) specifically includes segmenting the RF signal with a size of a × b × c acquired after compressing the tissue, and determining the segment length m and the segment interval n according to the requirements of resolution and smoothness.
Further, the step 2) specifically includes selecting a segmented series of the pre-compressed signaliDetermining the search range of the corresponding post-pressing signaliAnd search step widthiAnd calculating post-pressure segmented signals post _ series by using a linear interpolation methodi。
Further, in step 3), the similarity between the layers of the post-compression signal is calculated by using RC (residual complexity) as follows:
ith window IiAnd the jth window IjRC similarity d between themijIs defined as:
Figure BDA0002286118930000041
where dct is the discrete cosine transform and α is the parameter used to adjust the residual sparseness.
Further, the shortest path between the pre-compression signal window and the post-compression signal window in step 4) is implemented along a geodesic path on the manifold space, that is: first, find window IiAnd its neighboring node p1Then p is obtained1And p2The shortest distance between the two windows is obtained by analogyA distance; when the shortest distance between two windows requiring deformation, its deformation field is decomposed into small deformation fields between a series of adjacent images on the manifold:
g(Ii，Ij)＝argmin(p1，p2，...，pN)(di，p1+dp1，p2+…+dpN，j) (1)
wherein d isi，p1Is a window IiRC similarity with node p1, dpN，jFor node pN and window IjRC similarity between them, pNRepresenting the nth node.
Equation (1) is optimized using Dijkstra's algorithm (Dikstra algorithm) and from all shortest paths g (I)i，Ij) And selecting the minimum value from the tissue before pressing to the tissue after pressing in the searching range, and recording the distance between the window after pressing and the window before pressing on the shortest path, namely the displacement of the corresponding tissue under different pressures. When the shortest distance between two windows with larger deformations is desired, their deformation fields can be decomposed into small deformation fields between a series of adjacent images on the manifold.
Compared with the prior art, the method has the following advantages and beneficial effects:
1. the method has the advantages that the RC similarity measure is used for composition, certain robustness is achieved for gray value change, meanwhile, the calculation efficiency is high, and compared with the existing method, the similarity result is more stable.
2. The shortest path is obtained along the geodesic path in manifold space, and compared with the existing method, the displacement estimation algorithm is more accurate, and the quality of the strain diagram result is higher.
Drawings
FIG. 1 is a flowchart illustrating a method for elastography based on empirical manifold according to the present embodiment;
FIG. 2 is a diagram of residual complexity similarity measure used in the present embodiment;
FIG. 3 is a diagram showing the elasticity obtained in this example.
Detailed Description
The present invention will be described in further detail with reference to examples and drawings, but the present invention is not limited thereto.
An elastography method based on empirical manifold as shown in fig. 1, comprising the steps of:
1) a phantom (Model 049 phantom manufactured by CIRS corporation, usa) was pressed, and the RF signal collected after compression was segmented into 1040 × 256 × 387 pieces, and the piece length m and the piece pitch n were determined.
2) And then selecting a subsection series of the pre-pressing signal, determining a search range and a search step width of the post-pressing signal corresponding to the subsection series, and calculating a post-pressing subsection signal post _ series by using a linear interpolation method.
3) And calculating the similarity between post-lamination signals post _ series by using the residual complexity. And similarly, calculating the similarity between post _ series layers by using the RC, and taking the similarity d as the boundary weight of the graph.
Ith window IiAnd the jth window IjRC similarity d between themijIs defined as:
Figure BDA0002286118930000051
where dct is the discrete cosine transform and α is a parameter for adjusting the residual sparsity, set here to 0.05.
4) Then, the shortest path g of each window in the search range of the pre-stress signal series and the post-stress signal post _ series is obtained, that is, as shown in fig. 2, the shortest path g of each window is obtained firstiAnd its neighboring node p1Then p is obtained1And p2The distance of the window is analogized, and finally the shortest distance between the two windows is obtained; when the shortest distance between two windows requiring deformation, its deformation field is decomposed into small deformation fields between a series of adjacent images on the manifold:
g(Ii，Ij)＝argmin(p1，p2，...，pN)(di，p1+dp1，p2+…+dpN，j) (1)
wherein d ispN，jFor node pN and window IjAnd pN represents the nth node.
Equation (1) is optimized using Dijkstra's algorithm (Dikstra algorithm) and from all shortest paths g (I)i，Ij) The minimum value g in the search range from the prepressure tissue series to the post _ series is selectedminAnd recording. At the value of gminOn the shortest path of (3), the distance dp between the window after pressing and the window before pressing is the displacement of the corresponding tissue under different pressures.
5) And outputting a displacement estimation graph, fitting a curve by using a least square method to obtain strain estimation, and reconstructing an elasticity graph g of the tissue by combining distribution conditions, wherein the elasticity graph g is shown in fig. 3. Data were collected from Model049 phantom manufactured by CIRS corporation, usa. The method uses the RC similarity measure composition, has certain robustness on gray value change, has higher calculation efficiency, and has more stable similarity result compared with the prior method. The shortest path is obtained along the geodesic path in manifold space, and compared with the existing method, the displacement estimation algorithm is more accurate, and the quality of the strain diagram result is higher.
The above embodiments are preferred embodiments of the present invention, but the present invention is not limited to the above embodiments, and any other changes, modifications, substitutions, combinations, and simplifications which do not depart from the spirit and principle of the present invention should be construed as equivalents thereof, and all such changes, modifications, substitutions, combinations, and simplifications are intended to be included in the scope of the present invention.

Claims (5)

1. An elastography method based on empirical manifold is characterized in that: the method comprises the following steps:
1) segmenting an ultrasonic radio frequency signal (namely an RF signal) acquired after tissue compression, and determining segment length and segment spacing according to the requirements of resolution and smoothness;
2) selecting a segment of the pre-compression signal, determining the search range and the search step length of the corresponding p post-compression signals, and acquiring the post-compression segmented signals by using a linear interpolation method;
3) calculating the complexity between signal windows after lamination by using the residual complexity RC, calculating the similarity between the signal layers after lamination by using the complexity RC, and using the similarity as the boundary weight of the graph;
4) solving the shortest path of each window in the pre-compression organization and post-compression searching range;
5) and outputting a displacement estimation diagram, fitting a curve by using a least square method to obtain strain estimation, and reconstructing elastic modulus distribution of the tissue distribution, namely an elastic diagram of the tissue by combining distribution conditions after obtaining the strain distribution of the tissue.
2. The elastography method based on empirical manifold according to claim 1, wherein: the step 1) specifically comprises the steps of segmenting the RF signals with the size of a multiplied by b multiplied by c acquired after the tissues are compressed, and determining the segment length m and the segment spacing n according to the requirements of resolution and smoothness.
3. The elastography method based on empirical manifold according to claim 1, wherein: step 2) specifically comprises selecting a segmented series of pre-press signalsiDetermining the search range of the corresponding post-pressing signaliAnd search step widthiAnd calculating post-pressure segmented signals post _ series by using a linear interpolation methodi。
4. The elastography method based on empirical manifold according to claim 1, wherein: step 3), calculating the similarity between the layers of the post-compression signal by using Residual Complexity (RC) as follows:
ith window IiAnd the jth window IjRC similarity d between themijIs defined as:
Figure FDA0002286118920000021
where dct is the discrete cosine transform and α is the parameter used to adjust the residual sparseness.
5. The elastography method based on empirical manifold according to claim 1, wherein: between the pre-compression signal window and the post-compression signal window in the step 4)The shortest path is implemented along a geodesic path over the manifold space, i.e.: first, find window IiAnd its neighboring node p1Then p is obtained1And p2The distance of the window is analogized, and finally the shortest distance between the two windows is obtained; when the shortest distance between two windows requiring deformation, its deformation field is decomposed into small deformation fields between a series of adjacent images on the manifold:
g(Ii，Ij)＝argmin(p1，p2，...，pN)(di，p1+dp1，p2+…+dpN，j) (1)
wherein d isi，p1Is a window IiRC similarity with node p1, dpN，jFor node pN and window IjRC similarity between them, pN denotes the nth node;
equation (1) is optimized using Dijkstra's algorithm (Dikstra algorithm) and from all shortest paths g (I)i，Ij) And selecting the minimum value from the tissue before pressing to the tissue after pressing in the searching range, and recording the distance between the window after pressing and the window before pressing on the shortest path, namely the displacement of the corresponding tissue under different pressures.
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Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1586409A (en) * 2004-08-20 2005-03-02 清华大学 Biological tissue displacement evaluating method using two kinds of size
KR20080028658A (en) * 2006-09-27 2008-04-01 주식회사 메디슨 Method for forming ultrasound image by decreasing decorrelation of elasticity signal
CN102525568A (en) * 2012-01-17 2012-07-04 北京索瑞特医学技术有限公司 Subtraction elastography method
CN102764141A (en) * 2012-07-20 2012-11-07 中国科学院深圳先进技术研究院 Elastography method, elastography system, and biological tissue displacement estimation method and biological tissue displacement estimation system in elastography
CN103654865A (en) * 2013-12-26 2014-03-26 华南理工大学 Ultrasonic elasticity imaging tissue displacement estimation method based on maximum mutual information
CN104605891A (en) * 2014-12-31 2015-05-13 中国科学院苏州生物医学工程技术研究所 Method for detecting transmission speed of shear wave in biological tissue, method for detecting elasticity of biological tissue and method for biological tissue elasticity imaging
US20150327835A1 (en) * 2012-07-03 2015-11-19 University Of Pittsburgh - Of The Commonwealth System Of Higher Education Method and apparatus to detect lipid contents in tissues using ultrasound

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1586409A (en) * 2004-08-20 2005-03-02 清华大学 Biological tissue displacement evaluating method using two kinds of size
KR20080028658A (en) * 2006-09-27 2008-04-01 주식회사 메디슨 Method for forming ultrasound image by decreasing decorrelation of elasticity signal
CN102525568A (en) * 2012-01-17 2012-07-04 北京索瑞特医学技术有限公司 Subtraction elastography method
US20150327835A1 (en) * 2012-07-03 2015-11-19 University Of Pittsburgh - Of The Commonwealth System Of Higher Education Method and apparatus to detect lipid contents in tissues using ultrasound
CN102764141A (en) * 2012-07-20 2012-11-07 中国科学院深圳先进技术研究院 Elastography method, elastography system, and biological tissue displacement estimation method and biological tissue displacement estimation system in elastography
CN103654865A (en) * 2013-12-26 2014-03-26 华南理工大学 Ultrasonic elasticity imaging tissue displacement estimation method based on maximum mutual information
CN104605891A (en) * 2014-12-31 2015-05-13 中国科学院苏州生物医学工程技术研究所 Method for detecting transmission speed of shear wave in biological tissue, method for detecting elasticity of biological tissue and method for biological tissue elasticity imaging

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