An intracardiac ultrasound imaging method and system

Abstract

This application discloses an intracardiac ultrasound imaging method and system: An intracardiac ultrasound transducer is used to acquire ultrasound echo signals of the target tissue, and time delay correction and signal analytical transformation are performed on the ultrasound echo signals to obtain complex analytical signals; complex analytical signals within the imaging aperture range are selected at each imaging depth, and spatial smoothing and diagonal loading processing are performed on the complex analytical signals within the aperture range to calculate the sample covariance matrix; based on the minimum variance constraint criterion and the sample covariance matrix, adaptive weighting coefficients are calculated, and the complex analytical signals within the aperture range are weighted and superimposed using the adaptive weighting coefficients to calculate the first image data; based on the phase consistency of the complex analytical signals within the aperture range, a phase coherence factor is calculated, and the first image data is weighted using the phase coherence factor to calculate the second image data; post-processing is performed on the second image data to output an ultrasound image. This application improves spatial resolution.

Description

Technical Field

[0001] This application relates to the field of ultrasound imaging, and more specifically, to a method and system for intracardiac ultrasound imaging. Background Technology

[0002] With the rapid development of interventional cardiology and minimally invasive treatment techniques for structural heart disease, intracardiac echocardiography (ICE) has become widely used in clinical settings such as atrial fibrillation ablation, transcatheter valve replacement, and cardiac structure guidance, thanks to its advantages of real-time imaging and flexible operation, which are achieved by inserting a transducer catheter into the heart via the femoral vein. However, due to limitations such as the small aperture of the intracardiac probe, the limited number of array elements, and the complex working environment, the classic time-delay summation beamforming method still faces insufficient resolution in intracardiac imaging.

[0003] Existing related patents are mostly concentrated on general ultrasound systems or surface imaging scenarios, lacking specificity for 2D intracardiac ultrasound imaging. For example, the invention entitled "Intracardiac Two-Dimensional Ultrasound Imaging System and Method" (application date: November 29, 2022; application number: CN202211508642.3) proposes an intracardiac two-dimensional ultrasound imaging system and method that can switch or combine multiple imaging modes, but fails to solve the problem of low lateral resolution. The invention entitled "An Adaptive Focusing Broadband Beamforming Method and System" (application date: June 17, 2021; application number: CN202110674858.6) utilizes the Capon spatial spectrum to construct the interference plus noise covariance matrix at the reference frequency, but it is mainly geared towards linear arrays and is difficult to directly adapt to the geometry and imaging requirements of the micro phased array for intracardiac ultrasound imaging.

[0004] To address the aforementioned issues, this invention proposes an intracardiac ultrasound imaging method based on a combined weighting of minimum variance and phase coherence factor. This algorithm, taking into account the structural characteristics of the intracardiac ultrasound probe array, effectively suppresses off-axis echoes while maintaining the main valve direction gain, thereby improving the spatial resolution of intracardiac ultrasound imaging. This method requires no increase in the number of hardware array elements or modification of the probe structure, providing a new technical solution for high-quality intracardiac ultrasound imaging. Summary of the Invention

[0005] To address the limited spatial resolution of existing intracardiac ultrasound imaging techniques, this application provides an intracardiac ultrasound imaging method and system that improves spatial resolution.

[0006] One aspect of this application provides an intracardiac ultrasound imaging method, comprising: acquiring ultrasound echo signals of a target tissue using an intracardiac ultrasound transducer, and performing time delay correction and signal analytical transformation on the ultrasound echo signals to obtain complex analytical signals; selecting complex analytical signals within the imaging aperture range at each imaging depth, performing spatial smoothing and diagonal loading processing on the complex analytical signals within the aperture range, and calculating the sample covariance matrix; calculating adaptive weighting coefficients based on the minimum variance constraint criterion and the sample covariance matrix, and using the adaptive weighting coefficients to perform weighted superposition of the complex analytical signals within the aperture range to calculate first image data; calculating a phase coherence factor based on the phase consistency of the complex analytical signals within the aperture range, and using the phase coherence factor to perform weighted processing on the first image data to calculate second image data; performing image post-processing on the second image data to output an ultrasound image.

[0007] Among them, time delay correction refers to calculating the difference in the propagation time of ultrasonic waves from transmission to reception based on the spatial positional relationship between transducer array elements, transmission focus and imaging image point, and compensating for the time delay of the echo signals received by each array element so that the echo signals from the same imaging image point are aligned in time.

[0008] Signal analytical transformation refers to the mathematical transformation process of converting real ultrasonic echo signals into complex analytic signals. Through Hilbert transform or orthogonal detection processing, a complex signal containing real and imaginary parts is obtained, thereby simultaneously preserving the amplitude and phase information of the signal.

[0009] Imaging aperture range refers to the spatial range or number of transducer array elements participating in beamforming calculations at a specific imaging depth. The aperture size L represents the number of consecutive array elements participating in imaging. This range determines the spatial resolution and signal-to-noise ratio characteristics of the imaging system.

[0010] Spatial smoothing refers to extracting multiple complex analytic signal vector subarrays with a certain degree of overlap within a spatial neighborhood, calculating the covariance matrix of each subarray, and then averaging them to reduce the statistical error of covariance matrix estimation and improve the robustness of the adaptive beamforming algorithm in coherent signal environments.

[0011] Diagonal loading refers to adding a positive diagonal loading amount to the main diagonal elements of the initial sample covariance matrix, which is equivalent to adding a proportional identity matrix to the covariance matrix. This processing can improve the condition number of the covariance matrix, enhance the numerical stability of matrix inversion operations, and strengthen the robustness of the algorithm under small sample or low signal-to-noise ratio conditions.

[0012] The minimum variance constraint criterion is an adaptive beamforming criterion that minimizes the total power of the beamforming output by optimizing the weighting coefficients under the constraint of ensuring a constant gain in the direction of the desired signal. This achieves distortion-free reception of the signal in the desired direction while suppressing interference and noise from other directions to the greatest extent.

[0013] The phase coherence factor is a statistical index used to quantify the phase consistency between complex analytical signals of each array element within the aperture range. It is obtained by calculating the ratio of the amplitude of coherent superposition to incoherent superposition of complex signals. Its value range is usually between 0 and 1. The closer the value is to 1, the better the phase consistency of the signal. It can be used to identify and suppress clutter signals with inconsistent phases and enhance imaging contrast.

[0014] Furthermore, time delay correction is performed on the ultrasound echo signal, including: acquiring the spatial coordinates of the transducer elements, the spatial coordinates of the transmission focus, and the spatial coordinates of the imaging image points in the intracardiac ultrasound transducer; calculating the transmission focusing delay and the reception focusing delay based on the transducer element spatial coordinates, the transmission focus spatial coordinates, and the imaging image point spatial coordinates; and performing time delay correction on the ultrasound echo signal based on the transmission focusing delay and the reception focusing delay.

[0015] Furthermore, calculate the launch focusing delay. :

[0016] Where n = 1, 2, ..., N represents the transducer element number and N represents the total number of transducer elements; m = 1, 2, ..., M represents the emission focus number and M represents the total number of emission focuses; c represents the speed of sound in biological tissue. This represents the x-coordinate in the transducer array element space; Represents the x-coordinate of the launch focus space; This represents the y-coordinate in the transducer array element space; Represents the y-coordinate of the launch focus space; This represents the z-coordinate of the transducer array element space; Represents the z-coordinate of the launch focus space; Furthermore, calculate the receiving focus delay. : Where k = 1, 2, ..., K represents the image point number, and K represents the total number of image points; This represents the x-coordinate in space of the nth transducer element. This represents the y-coordinate in space of the nth transducer element. This represents the z-coordinate of the nth transducer element in space; This represents the spatial x-coordinate of the nth image point. This represents the spatial y-coordinate of the nth image point. This represents the spatial z-coordinate of the nth image point.

[0017] Furthermore, the signal analytical transformation employs Hilbert transform or orthogonal detection processing.

[0018] Furthermore, spatial smoothing and diagonal loading processing are performed on the complex analytical signals within the aperture range, including: selecting complex analytical signals within the aperture range of imaging aperture size L, and constructing a complex analytical signal vector. Set the spatial neighborhood range and extract multiple complex analytic signal vectors within the spatial neighborhood range; perform covariance operations on the multiple complex analytic signal vectors respectively, and calculate the initial sample covariance matrix. Based on the initial sample covariance matrix Calculate diagonal loading ; Utilizing diagonal loading For the initial sample covariance matrix Diagonal loading is performed to obtain the sample covariance matrix R.

[0019] Furthermore, the initial sample covariance matrix is ​​calculated. The following formula is used: Wherein, the superscript H indicates the conjugate transpose of the vector; It represents half the length of the spatial neighborhood; N represents the number of transducer array elements; L represents the subarray length; n represents the transducer array element number. represents the complex analytic signal within the aperture; k represents the image point number; i represents the spatial neighborhood point number.

[0020] Furthermore, the diagonal loading amount ε is calculated using the following formula: Where α represents the diagonal loading parameter, tr{ } represents calculating the trace of a matrix.

[0021] Furthermore, the adaptive weighting coefficients are calculated using the following formula: ; where a represents a vector whose values ​​are all 1.

[0022] Furthermore, the first image data is calculated using the following formula: ;in, Represents the weight vector; This represents the complex analytic signal within the aperture.

[0023] The second image data is calculated using the following formula: ;in, Indicates the phase coherence factor; This represents the first image data.

[0024] Another aspect of this application provides an intracardiac ultrasound imaging system, comprising: an intracardiac ultrasound transducer for acquiring ultrasound echo signals from a target tissue; a signal preprocessing module for performing time delay correction and signal analytical transformation on the ultrasound echo signals to obtain complex analytical signals; the time delay correction includes calculating the transmission focusing time delay and the reception focusing time delay based on the spatial coordinates of the transducer array elements, the spatial coordinates of the transmission focus, and the spatial coordinates of the imaging image points; and a covariance matrix calculation module for selecting complex analytical signals within the imaging aperture range at each imaging depth and performing spatial smoothing on the complex analytical signals within the aperture range. The system performs diagonal loading processing to calculate the sample covariance matrix; the adaptive beamforming module calculates adaptive weighting coefficients based on the minimum variance constraint criterion and the sample covariance matrix, and uses these adaptive weighting coefficients to weight and superimpose complex analytic signals within the aperture range to calculate the first image data; the phase coherence weighting module calculates the phase coherence factor based on the phase consistency of the complex analytic signals within the aperture range, and uses the phase coherence factor to weight the first image data to calculate the second image data; the image post-processing module performs image post-processing on the second image data and outputs an ultrasound image.

[0025] Compared to existing technologies, the advantages of this application are: To address the limitations of existing intracardiac ultrasound imaging technologies, such as limited spatial resolution and insufficient suppression of off-axis interference and coherent clutter, this application provides an intracardiac ultrasound imaging method based on joint weighting of minimum variance and phase coherence factor. This method optimizes the calculation of the sample covariance matrix by performing spatial smoothing and diagonal loading on the complex analytical signal within the aperture range. Adaptive weighting coefficients are calculated based on the minimum variance constraint criterion to enhance the echo signal in the desired imaging direction and suppress interference signals in the undesired direction. Furthermore, the preliminary imaging results are further weighted using the phase coherence factor to suppress coherent clutter, thereby improving the spatial resolution and imaging quality of intracardiac ultrasound imaging without increasing hardware complexity. Attached Figure Description

[0026] Figure 1 This is a schematic diagram of an intracardiac ultrasound imaging process based on a combined weighting of minimum variance and phase coherence factor. Figure 2 To synthesize ultrasonic imaging results of point scatterers using the classical time-delay superposition algorithm; Figure 3 To synthesize the ultrasonic imaging results of a point scatterer using minimum variance beamforming; Figure 4 For the corresponding Figure 2 The amplitude curve of the ultrasound image distributed laterally at a depth of 30 mm in the middle; Figure 5 For the corresponding Figure 3The amplitude curve of the ultrasound image distributed laterally at a depth of 30 mm in the middle; Figure 6 To obtain the ultrasonic imaging results of a group of scattering points using beamforming with the classical time-delay superposition algorithm; Figure 7 To utilize minimum variance beamforming to synthesize the ultrasonic imaging results of a group of scattering points; Figure 8 For the corresponding Figure 6 The amplitude curve of the ultrasound image distributed laterally at a depth of 30 mm in the middle; Figure 9 For the corresponding Figure 7 The amplitude curve of the ultrasound image distributed laterally at a depth of 30 mm. Detailed Implementation

[0027] The present application will now be described in detail with reference to the accompanying drawings and specific embodiments.

[0028] Example 1 like Figure 1 The diagram shown is a schematic representation of the method flow of an embodiment. An intracardiac ultrasound imaging method based on joint weighting of minimum variance and phase coherence factor according to the present invention includes the following steps: 1. Set ultrasound imaging parameters and acquire ultrasound echo signals: Specifically, ultrasound imaging parameters are set. Further, these parameters include the spatial orientation and geometric dimensions of the intracardiac ultrasound transducer; the transmission center frequency, scanning angle range, linear density, imaging depth, sound velocity, transmission focusing depth, receiving focusing depth, and imaging aperture size of the imaging system; and the diagonal loading factor. Further, this embodiment uses a 64-element phased array probe, a linear density of 128, and an imaging aperture of L=32. It employs a classic linear scanning method, successively transmitting focused ultrasound pulses within a 90° spatial angle range and receiving their reflected echoes to ultimately obtain an ultrasound image of the target tissue.

[0029] Specifically, an intracardiac ultrasound transducer is used to acquire ultrasound echo signals of the target tissue. Further, the intracardiac ultrasound transducer is a catheter-type ultrasound transducer, which is inserted into the heart cavity via the inferior vena cava. The spatial orientation of the catheter-type ultrasound transducer is adjusted under the control of a rotating handle so that it enters the right atrium or right ventricle. Based on the scanning angle range, ultrasound pulses are emitted to the target tissue and its echo signals are acquired.

[0030] 2. Perform delay correction and construct a complex analytic signal: Specifically, the focusing delay is calculated based on the scanning angle range, focusing depth, and imaging depth; the ultrasonic echo signal is then time-delay corrected and a complex analytic signal is constructed. This process includes the following steps: S01. Based on the spatial orientation and geometric dimensions of the intracardiac ultrasound transducer, determine the spatial coordinates (xe, ye, ze) of each element of the transducer, where x, y, and z represent three-dimensional spatial coordinates, and the subscript e represents the element. S02. Based on the scanning angle range, linear density, and emission focusing depth, determine the spatial coordinates of the emission focus as (xtx, ytx, ztx), where the subscript tx represents the emission focus; S03. Calculate the transmission focusing delay τtx,n based on the spatial coordinates of each transducer element and the transmission focus. ; Where n = 1, 2, …, N represents the transducer element number, N represents the total number of transducer elements; m = 1, 2, …, M represents the emission focus number, M represents the total number of emission focuses, and c represents the speed of sound in biological tissue. S04. Based on the scanning angle range, linear density, and receiving focusing depth, determine the spatial coordinates of the ultrasound image point as (xp, yp, zp), where the subscript p represents the image point; S05. Calculate the receiving focusing delay τp,n based on the spatial coordinates of each transducer array element and image point. ; Where k = 1, 2, …, K represents the image point index, and K represents the total number of image points; S06. The total focusing delay τ = τtx + τp is obtained by adding the transmission focusing delay and the reception focusing delay, and the focusing delay is applied to the ultrasonic echo signal to perform time-domain shifting on the ultrasonic echo signal in order to achieve focusing delay correction. S07. The delayed-corrected ultrasonic echo signal is processed by Hilbert transform or orthogonal detection to obtain its corresponding complex analytic signal s(n, k).

[0031] 3. Calculate the sample covariance matrix: Specifically, based on the imaging aperture size, complex analytical signals within the corresponding aperture range are selected at each imaging depth, and spatial smoothing and diagonal loading are used to calculate the sample covariance matrix. Specific steps include: S01. The imaging aperture size is L. A complex analytic signal within the aperture is selected, and its vector form sn is constructed. ; S02. Based on the spatial neighborhood range [-L0, L0], calculate the initial sample covariance matrix R0. ; Wherein, the superscript H indicates the conjugate transpose of the vector; S03. Calculate the diagonal loading amount ε based on the aforementioned initial sample covariance matrix R0. ; Where α represents the diagonal loading parameter, tr{ } represents calculating the trace of a matrix; S04. Apply a diagonal loading amount ε to the initial sample covariance matrix R0 to obtain the updated sample covariance matrix R. ; Where I represents the identity matrix.

[0032] 4. Solve for the adaptive weighting coefficients and perform weighted superposition of the signals: Specifically, based on the minimum variance constraint criterion, the adaptive weighting coefficients are solved using the sample covariance matrix; further, the adaptive weighting coefficients w are calculated according to the following formula. ; Where 'a' represents a vector whose values ​​are all 1, and the superscript -1 indicates the inverse operation on the matrix; Specifically, based on the adaptive weighting coefficient, the analytical signals within the aperture range are weighted and superimposed to form an initial ultrasound image; further, for each image point k, the adaptive weighting coefficient w is weighted and summed with the complex analytical signal sn within the corresponding aperture range according to the following formula to obtain the initial ultrasound image z0 at the corresponding image point k.

[0033] ; Based on the above formula, by traversing all image points k=1,…,K, a complete initial ultrasound image is obtained.

[0034] 5. Calculate the phase coherence factor and apply it to the initial ultrasound image: The phase coherence factor is calculated based on the phase consistency of the analytical signal within the aperture range, and the phase coherence factor is used as a weight to apply to the initial ultrasound image, outputting the final ultrasound image; the specific steps include, S01. Calculate the phase standard deviation σ(k) of the aperture signal sn(k) for each image point and then calculate the phase coherence factor for that image point using the following formula: ; Where γ is a constant in (0,1], and σ0 is a normalization factor; S02. Apply the phase coherence factor PCF(k) to the aforementioned initial ultrasound image signal z0(k) to obtain the updated ultrasound image signal z(k): ; 6. After post-processing, the final ultrasound image is output: Specifically, the ultrasound image after applying the phase coherence factor is subjected to envelope extraction, low-pass filtering, and logarithmic compression to obtain an ultrasound grayscale image reflecting the target tissue within the cardiac chamber.

[0035] Example 2 In this example, point scatterers distributed at equal intervals along the depth direction are used as ultrasound imaging targets to verify the imaging performance of the proposed intracardiac ultrasound imaging method based on the joint weighting of minimum variance and phase coherence factor. Specifically, a pair of point scatterers with a lateral distance of 3 mm are set every 10 mm in the depth direction [10, 80] mm interval.

[0036] Figure 2 and Figure 3 The ultrasonic imaging results of point scatterers by the classical time-delay summation beamforming method and the method proposed in this invention are compared. Both methods can form speckle patterns at the corresponding depth of the point scatterer. The bright spot formed by the classical time-delay summation beamforming method has a significantly larger lateral width, indicating lower lateral resolution, making it difficult to distinguish two point scatterers spaced 3 mm apart at the same depth; furthermore, its lateral resolution gradually decreases with increasing distance from the emission focal point. In contrast, the method proposed in this invention exhibits lower sidelobe artifacts and higher lateral resolution throughout the entire depth range, clearly distinguishing different point scatterers at the same depth.

[0037] Figure 4 and Figure 5 They are respectively the corresponding Figure 2 and Figure 3 The amplitude curves of the ultrasound image distributed laterally at a depth of 30 mm were analyzed. The results show that the classical time-delay summation beamforming method has a wide main lobe width (approximately 0.79 mm) and high side lobe amplitude (approximately 5 dB), limiting its lateral resolution and contrast. In contrast, the joint weighting method proposed in this invention significantly narrows the main lobe width to approximately 0.24 mm, while reducing the side lobe amplitude to below -50 dB. This quantitatively verifies the effectiveness and advantages of this invention in improving lateral resolution and suppressing side lobe artifacts, thereby enabling better differentiation of closely scattering targets.

[0038] Example 3 In this example, a cluster of scattering points was used as the ultrasound imaging target to verify the imaging performance of the proposed intracardiac ultrasound imaging method based on the joint weighting of minimum variance and phase coherence factor. Specifically, a scattering point was placed every 10 mm in the depth range of [10, 80] mm, a scattering point was placed every 10 mm in the transverse range of [-50, 50] mm at a depth of 60 mm, and two strong scattering regions and two cyst phantoms were placed in the transverse range of [-40, 40] mm at a depth of 40 mm.

[0039] Figure 6 and Figure 7 The results of ultrasound imaging of point scatterers are compared between the classical time-delay summation beamforming method and the method proposed in this invention. Both methods can form speckle patterns at the corresponding locations of the point scatterer and the strong scattering region, and form the boundary of the cyst region. The bright spot formed by the classical time-delay summation beamforming method has a significantly larger lateral width, indicating lower lateral resolution, thus making the boundaries of the strong scattering region and the cyst boundary more blurred; in addition, its lateral resolution gradually decreases with increasing distance from the emission focal point. In contrast, the method proposed in this invention exhibits lower sidelobe artifacts and higher lateral resolution throughout the entire depth range, and can clearly distinguish the scattering point, the boundary of the strong scattering region, and the cyst boundary at different depths.

[0040] Figure 8 and Figure 9 They are respectively the corresponding Figure 6 and Figure 7 The amplitude curves of the ultrasound image distributed laterally at a depth of 30 mm were analyzed. The results show that the classical time-delay summation beamforming method has a wide main lobe width (approximately 0.92 mm) and high side lobe amplitude (approximately 65 dB), limiting its lateral resolution and contrast. In contrast, the joint weighting method proposed in this invention significantly narrows the main lobe width to approximately 0.42 mm, while reducing the side lobe amplitude to below 30 dB. This quantitatively verifies the effectiveness and advantages of this invention in improving lateral resolution and suppressing side lobe artifacts.

[0041] The foregoing illustrative description of the present application and its embodiments is not restrictive and can be implemented in other specific forms without departing from the spirit or essential characteristics of the present application. The accompanying drawings are only one embodiment of the present application, and the actual structure is not limited thereto. Therefore, if those skilled in the art are inspired by this description and design similar structures and embodiments without departing from the spirit of the present application, such designs should fall within the scope of protection of this application. Furthermore, the word "comprising" does not exclude other elements or steps, and the word "a" preceding an element does not exclude the inclusion of "a plurality" of that element. Terms such as "first," "second," etc., are used to indicate names and do not indicate any specific order.

Claims

1. A method for intracardiac ultrasound imaging, characterized in that, include: The ultrasound echo signal of the target tissue was acquired using an intracardiac ultrasound transducer, and the ultrasound echo signal was subjected to time delay correction and signal analytical transformation to obtain a complex analytical signal. Complex analytical signals within the imaging aperture range are selected at each imaging depth. Spatial smoothing and diagonal loading processing are performed on the complex analytical signals within the aperture range, and the sample covariance matrix is ​​calculated. Based on the minimum variance constraint criterion and the sample covariance matrix, adaptive weighting coefficients are calculated, and the complex analytic signals within the aperture range are weighted and superimposed using the adaptive weighting coefficients to calculate the first image data. Based on the phase consistency of the complex analytic signal within the aperture range, the phase coherence factor is calculated, and the first image data is weighted using the phase coherence factor to calculate the second image data. The second image data is post-processed to output an ultrasound image.

2. The intracardiac ultrasound imaging method according to claim 1, characterized in that: Time delay correction of ultrasonic echo signals includes: Acquire the spatial coordinates of the array elements, the spatial coordinates of the emission focus, and the spatial coordinates of the imaging points of the intracardiac ultrasound transducer; Calculate the transmission focusing delay and the reception focusing delay based on the spatial coordinates of the transducer array elements, the spatial coordinates of the transmission focus, and the spatial coordinates of the imaging image points. The ultrasonic echo signal is time-delay corrected based on the transmission focusing delay and the reception focusing delay.

3. The intracardiac ultrasound imaging method according to claim 2, characterized in that: Calculate launch focusing delay : Where n = 1, 2, ..., N represents the transducer element number and N represents the total number of transducer elements; m = 1, 2, ..., M represents the emission focus number and M represents the total number of emission focuses; c represents the speed of sound in biological tissue. This represents the x-coordinate in the transducer array element space; Represents the x-coordinate of the launch focus space; This represents the y-coordinate in the transducer array element space; Represents the y-coordinate of the launch focus space; This represents the z-coordinate of the transducer array element space; This represents the z-coordinate of the emission focus space.

4. The intracardiac ultrasound imaging method according to claim 2, characterized in that: Calculate the receiver focusing delay : Where k = 1, 2, ..., K represents the image point number, and K represents the total number of image points; This represents the x-coordinate in space of the nth transducer element. This represents the y-coordinate in space of the nth transducer element. This represents the z-coordinate of the nth transducer element in space; This represents the spatial x-coordinate of the nth image point. This represents the spatial y-coordinate of the nth image point. This represents the spatial z-coordinate of the nth image point.

5. The intracardiac ultrasound imaging method according to claim 3 or 4, characterized in that: Signal analytical transformation employs Hilbert transform or orthogonal detection processing.

6. The intracardiac ultrasound imaging method according to claim 5, characterized in that: Spatial smoothing and diagonal loading processing are performed on complex analytic signals within the aperture range, including: Select complex analytic signals within an aperture range of size L, and construct a complex analytic signal vector. ; Set the spatial neighborhood range and extract multiple complex analytic signal vectors within the spatial neighborhood range; Perform covariance operations on multiple complex analytic signal vectors to calculate the initial sample covariance matrix. ; Based on the initial sample covariance matrix Calculate diagonal loading ; Using diagonal loading For the initial sample covariance matrix Diagonal loading is performed to obtain the sample covariance matrix R.

7. The intracardiac ultrasound imaging method according to claim 6, characterized in that: Calculate the initial sample covariance matrix The following formula is used: Wherein, the superscript H indicates the conjugate transpose of the vector; It represents half the length of the spatial neighborhood; N represents the number of transducer array elements; L represents the subarray length; n represents the transducer array element number. represents the complex analytic signal within the aperture; k represents the image point number; i represents the spatial neighborhood point number.

8. The intracardiac ultrasound imaging method according to claim 6, characterized in that: The diagonal loading amount ε is calculated using the following formula: Where α represents the diagonal loading parameter, tr{ } represents calculating the trace of a matrix.

9. The intracardiac ultrasound imaging method according to claim 8, characterized in that: The adaptive weighting coefficients are calculated using the following formula: ； Where 'a' represents a vector whose values ​​are all 1.

10. An intracardiac ultrasound imaging system for implementing the method according to any one of claims 1 to 9, characterized in that, include: Intracardiac ultrasound transducer to acquire ultrasound echo signals from target tissue; The signal preprocessing module performs time delay correction and signal analytical transformation on the ultrasonic echo signal to obtain a complex analytical signal; the time delay correction includes calculating the transmission focusing delay and the reception focusing delay based on the spatial coordinates of the transducer array elements, the spatial coordinates of the transmission focus, and the spatial coordinates of the imaging image points; The covariance matrix calculation module selects complex analytical signals within the imaging aperture range at each imaging depth, performs spatial smoothing and diagonal loading on the complex analytical signals within the aperture range, and calculates the sample covariance matrix. The adaptive beamforming module calculates adaptive weighting coefficients based on the minimum variance constraint criterion and the sample covariance matrix, and uses the adaptive weighting coefficients to weight and superimpose complex analytical signals within the aperture range to calculate the first image data. The phase coherence weighting module calculates the phase coherence factor based on the phase consistency of the complex analytic signal within the aperture range, and uses the phase coherence factor to weight the first image data to calculate the second image data; The image post-processing module performs image post-processing on the second image data and outputs an ultrasound image.

Images