Blood flow extraction image forming device, blood flow extraction image forming method, and blood flow extraction image forming program

Abstract

To further reduce a clutter component in a blood flow extraction image obtained by extracting a fine blood vessel.SOLUTION: A correlation matrix calculation unit 40 calculates a correlation matrix Rzx indicating a correlation of signal values in a plurality of pieces of frame data F for each data element E of the frame data F. A singular value decomposition calculation unit 42 performs singular value decomposition on the correlation matrix Rzx. A blood flow luminance image formation unit 44a constitutes a blood flow extraction filter Pk,N on the basis of a characteristic value λi of a rank equal to or less than a first threshold rank and characteristic vectors wi,wiH corresponding to the characteristic value λi obtained by the singular value decomposition, and forms a blood flow luminance image Uk,N by applying the blood flow extraction filter Pk,N to the frame data F. A tissue image formation unit 44b forms a tissue image including a tissue component on the basis of the signal value of each data element E constituting the plurality of pieces of frame data F. A blood flow extraction image formation unit 44c forms a blood flow extraction image Uout by subtracting the tissue image from the blood flow luminance image Uk,N.SELECTED DRAWING: Figure 2

Description

[Technical Field]

[0001] This specification discloses a blood flow extraction image forming device, a blood flow extraction image forming method, and a blood flow extraction image forming program. In particular, it discloses an improvement to DFI (Detective Flow Imaging), which forms a blood flow extraction image depicting blood flow in a subject based on an ultrasound signal indicating the signal intensity of ultrasound reflected from the subject. [Background technology]

[0002] Conventionally, ultrasound diagnostic devices have been widely used as medical examination devices that present information about the inside of a subject (herein referred to as a living body) that cannot be seen with the naked eye in the form of numerical values ​​or ultrasound images. A basic method for forming an ultrasound image involves transmitting ultrasound toward an imaging target within the subject, and converting the signal intensity of the ultrasound reflected from the subject into brightness. This allows the morphology of the tissue to be displayed in the ultrasound image.

[0003] In recent years, technology has been developed to generate ultrasound images showing blood flow in a subject by performing principal component analysis on ultrasound signals. Principal component analysis is a statistical analysis technique based on singular value decomposition (including eigenvalue decomposition). When applying principal component analysis to ultrasound signals, information that better reproduces the original ultrasound signal (ultrasound image) (e.g., relatively bright components such as tissue boundaries and parenchyma) is classified as the higher-order principal components, while information with a lower information dominance (e.g., dynamic blood flow components with lower reflectivity than tissue) is classified as the lower-order principal components. Utilizing this property, it has been proposed to specifically extract and visualize blood flow components from ultrasound signals.

[0004] For example, Patent Document 1 discloses a method for improving the quality of blood flow images by performing principal component analysis on Doppler signals (signals indicating phase shifts (Doppler shift frequencies) calculated by processing such as autocorrelation calculations for multiple ultrasound signals) obtained by transmitting and receiving ultrasound waves to and from a subject, thereby suppressing components (clutter components) derived from tissue movement due to changes in body position, breathing, heartbeat, etc. For example, a principal filter matrix is ​​calculated so that the first to third principal components corresponding to the clutter components of the Doppler signal are removed and the fourth to sixth principal components corresponding to the blood flow components are maintained. By applying the principal filter matrix to the Doppler signal, an image in which the clutter components are suppressed is formed. Specifically, a first specific principal component (e.g., the first principal component) and a second specific principal component (e.g., the sixth principal component) are extracted from the Doppler signal, and brightness adjustment (weighting) is performed using the ratio between them as an index. For example, if the ratio of the sixth principal component to the first principal component is used as an index, the index will be close to 1 for pixels located inside blood vessels, and close to 0 for pixels located in organ tissue. As a result, pixels located in organ tissue are hardly rendered, resulting in the removal of clutter components. Patent Document 2 also discloses a method of adaptively varying the eigenorder to be visualized for information obtained by principal component analysis, and a method of adjusting the signal intensity of the visualized signal according to the eigenorder. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Japanese Patent Application Publication No. 2019-54938 [Patent Document 2] Japanese Patent Publication No. 2020-185122 Summary of the Invention [Problem to be solved by the invention]

[0006] Unlike Patent Document 1, consider the case where a blood flow extraction image is formed by extracting blood flow in a subject based on an ultrasound signal indicating the signal intensity of ultrasound reflected from the subject, rather than a Doppler signal. As mentioned above, this technique is also called DFI. In DFI, singular value decomposition is performed on the ultrasound signal indicating the signal intensity of the reflected wave. A blood flow extraction filter constructed based on lower-rank signal components obtained by singular value decomposition of the ultrasound signal is a filter that mainly extracts blood flow components and removes tissue components. Therefore, by applying a blood flow extraction filter to the ultrasound signal, a blood flow extraction image can be formed in which microvessels (blood flow) are extracted.

[0007] Here, the signal components extracted by the blood flow extraction filter may contain not only signal components indicating blood flow but also clutter components, which are signal components derived from tissues due to the subject's body movement, etc. Therefore, these clutter components may also appear in the blood flow extraction image formed by applying the blood flow extraction filter to the ultrasound signal. For example, in the case of the liver, since the area near the heart is strongly affected by pulsation, signals related to microvessels are buried in the clutter components, significantly reducing the visibility of the microvessels.

[0008] To obtain a suitable blood flow extracted image, it is necessary to suppress clutter components without impairing the signal components that truly indicate blood flow among the signal components extracted by the blood flow extraction filter.

[0009] The blood flow extraction image forming apparatus, blood flow extraction image forming method, or blood flow extraction image forming program according to this specification aims to further reduce clutter components in a blood flow extraction image in which microvessels are extracted. [Means for solving the problem]

[0010] The blood flow extraction image forming device according to the present specification includes a frame data acquiring unit that acquires a plurality of frames of frame data, each frame having a signal value indicating a signal intensity of a reflected wave, generated based on a plurality of received signals obtained by an ultrasound probe that transmits and receives ultrasound to and from a subject, the ultrasound probe transmitting and receiving ultrasound to and from the subject, the plurality of received signals being generated based on a plurality of received signals obtained by receiving reflected waves of the ultrasound from the subject; a correlation matrix computing unit that computes a correlation matrix indicating a correlation of the signal values ​​in the plurality of frame data for each data element of the frame data corresponding to each pixel of an ultrasound image formed from the frame data; and a correlation matrix computing unit that computes the correlation matrix by dividing the signal values ​​into singular values. a blood flow intensity image forming unit that forms a blood flow intensity image by applying a blood flow extraction filter consisting of the singular values ​​of a rank equal to or lower than a first threshold rank and the singular vectors corresponding to the singular values ​​to the frame data; a tissue image forming unit that forms a tissue image based on the signal values ​​of each of the data elements that constitute the plurality of frame data; and a blood flow extraction image forming unit that forms a blood flow extraction image by subtracting the tissue image from the blood flow intensity image.

[0011] A blood flow intensity image obtained by applying a blood flow extraction filter based on the singular values ​​and singular vectors of lower ranks (ranks equal to or lower than the first threshold rank) obtained by singular value decomposition of the correlation matrix to frame data contains many blood flow components representing microvessels (blood flow). However, the blood flow intensity image may also contain clutter components. On the other hand, the tissue image does not contain many blood flow components but does contain tissue components and clutter components. Therefore, with the above configuration, by subtracting the tissue image from the blood flow intensity image, it is possible to obtain a blood flow extraction image in which the clutter components have been removed from the blood flow intensity image while leaving the blood flow components contained in the blood flow intensity image.

[0012] The tissue image forming unit may apply a tissue extraction filter configured from the singular values ​​of ranks greater than the first threshold rank and the singular vectors corresponding to the singular values ​​to the frame data to form the tissue image.

[0013] According to the above configuration, it is possible to further reduce the blood flow components contained in the tissue image, and obtain a blood flow extraction image in which clutter components have been removed from the blood flow brightness image while retaining more of the blood flow components contained in the blood flow brightness image.

[0014] The frame data may further include a calculation target region specifying unit that specifies a calculation target region including the data elements corresponding to tissue, and the correlation matrix calculation unit may calculate the correlation matrix within the calculation target region. In particular, the calculation target region specifying unit may select thinned frame data obtained by thinning out some of the data elements constituting the frame data as the calculation target region. Alternatively, the calculation target region specifying unit may select a set of data elements, among the plurality of data elements constituting the frame data, whose signal values ​​are equal to or greater than a threshold signal value, as the calculation target region. Alternatively, the calculation target region specifying unit may perform singular value decomposition on the plurality of frame data to obtain higher-rank extracted data composed of signal components with ranks greater than a second threshold rank, and select thinned frame data obtained by thinning out some of the data elements constituting the higher-rank extracted data as the calculation target region. Alternatively, the calculation target area identification unit may perform singular value decomposition on the plurality of frame data to obtain higher-rank extracted data composed of signal components of ranks greater than a second threshold rank, and identify, as the calculation target area, a set of data elements among the plurality of data elements constituting the higher-rank extracted data whose signal values ​​are equal to or greater than a threshold signal value.

[0015] The correlation matrix is ​​used to construct a blood flow extraction filter or a tissue extraction filter by singular value decomposition. Therefore, as long as the result of singular value decomposition of the correlation matrix allows a tissue extraction filter to be constructed based on higher-ranked signal components and a blood flow extraction filter to be constructed based on lower-ranked signal components, it is not necessary to obtain a correlation matrix for the entire frame data. Therefore, with this configuration, the objects to be calculated for the correlation matrix are limited, and the amount of calculation required to obtain the correlation matrix can be reduced.

[0016] The blood flow extraction image forming unit may form the blood flow extraction image by subtracting the tissue image multiplied by the coefficient from the blood flow brightness image.

[0017] According to this configuration, by adjusting the coefficient, it is possible to adjust the amount of signal to be subtracted from the blood flow luminance image.

[0018] The blood flow extraction image forming unit may determine the coefficients based on an input from a user.

[0019] According to this configuration, the user can obtain a blood flow extraction image in which a desired amount of signal is subtracted from the blood flow brightness image.

[0020] The blood flow extraction image forming device according to the present specification includes a frame data acquisition unit that acquires a plurality of frames of frame data, each frame having a signal value indicating the signal strength of a reflected wave, generated based on a plurality of received signals obtained by an ultrasound probe that transmits and receives ultrasound to and from a subject, the ultrasound probe transmitting and receiving ultrasound to and from the subject, and receiving reflected waves of the ultrasound from the subject; a calculation target region specifying unit that specifies a calculation target region in the frame data, the calculation target region including data elements corresponding to tissue, the data elements being data elements of the frame data corresponding to each pixel of an ultrasound image formed from the frame data; and a correlation matrix calculation unit that calculates a correlation matrix indicating the correlation of the signal values ​​in the plurality of frame data for each of the data elements that constitute the frame data. a singular value decomposition calculation unit that performs singular value decomposition of the correlation matrix to calculate a plurality of singular values ​​whose ranks are defined in order of magnitude and a plurality of singular vectors corresponding to each singular value; and an image processing unit that performs at least one of a blood flow intensity image formation process that forms a blood flow intensity image by applying a blood flow extraction filter made up of the singular values ​​of a rank equal to or less than a first threshold rank and the singular vectors corresponding to the singular values ​​to the frame data, or a tissue image formation process that forms a tissue image by applying a tissue extraction filter made up of the singular values ​​of a rank greater than the first threshold rank and the singular vectors corresponding to the singular values ​​to the frame data.

[0021] The blood flow extraction image forming method according to the present specification includes a frame data acquiring step of acquiring a plurality of frames of frame data each having a signal value indicating a signal intensity of a reflected wave, the signal value being generated based on a plurality of received signals obtained by an ultrasound probe that transmits and receives ultrasound to and from a subject, the ultrasound probe transmitting and receiving ultrasound to and from the subject, the plurality of received signals being generated based on a plurality of received signals obtained by receiving reflected waves of the ultrasound from the subject; a correlation matrix calculating step of calculating a correlation matrix indicating a correlation of the signal values ​​in the plurality of frame data for each data element of the frame data corresponding to each pixel of an ultrasound image formed from the frame data; and a correlation matrix calculating step of performing singular value decomposition on the correlation matrix. a blood flow intensity image forming step of applying a blood flow extraction filter consisting of the singular values ​​of a rank equal to or lower than a first threshold rank and the singular vectors corresponding to the singular values ​​to the frame data to form a blood flow intensity image; a tissue image forming step of forming a tissue image based on the signal values ​​for each of the data elements constituting the plurality of frame data; and a blood flow extraction image forming step of subtracting the tissue image from the blood flow intensity image to form a blood flow extraction image.

[0022] The blood flow extraction image formation program according to the present specification includes a computer, which includes a frame data acquisition unit that acquires a plurality of frames of frame data, each frame having a signal value indicating a signal intensity of a reflected wave, generated based on a plurality of received signals obtained by an ultrasound probe that transmits and receives ultrasound to and from a subject, transmitting ultrasound to the same scanning plane a plurality of times, and receiving reflected waves of the ultrasound from the subject; a correlation matrix calculation unit that calculates a correlation matrix indicating a correlation of the signal values ​​in the plurality of frame data for each data element of the frame data corresponding to each pixel of an ultrasound image formed from the frame data; and a correlation matrix calculation unit that calculates the correlation matrix. The apparatus is characterized by functioning as a singular value decomposition calculation unit that calculates a plurality of singular values, ranks of which are defined in order of magnitude, and a plurality of singular vectors corresponding to each singular value by performing singular value decomposition; a blood flow brightness image forming unit that forms a blood flow brightness image by applying a blood flow extraction filter consisting of the singular values ​​of ranks equal to or lower than a first threshold rank and the singular vectors corresponding to the singular values ​​to the frame data; a tissue image forming unit that forms a tissue image based on the signal values ​​for each of the data elements that make up the plurality of frame data; and a blood flow extraction image forming unit that forms a blood flow extraction image by subtracting the tissue image from the blood flow brightness image. [Effects of the Invention]

[0023] According to the blood flow extraction image forming device, blood flow extraction image forming method, or blood flow extraction image forming program according to the present specification, clutter components in a blood flow extraction image in which microvessels are extracted can be further reduced. [Brief explanation of the drawings]

[0024] [Figure 1] 1 is a schematic diagram illustrating the configuration of an ultrasound diagnostic apparatus according to the present embodiment. [Figure 2] FIG. 2 is a schematic diagram illustrating the configuration of a blood flow visualization unit. [Figure 3] FIG. 10 is a conceptual diagram showing a plurality of frame data. [Figure 4] FIG. 10 is a conceptual diagram showing the flow of a process for forming a blood flow extraction image. [Figure 5] 10 is a graph showing the relationship between rank and variance of signal values ​​of frame data after application of a filter of that rank. [Figure 6A] FIG. 10 is a diagram showing a first example of a screen for determining coefficients of a tissue image. [Figure 6B] FIG. 10 is a diagram showing a second example of a screen for determining coefficients of a tissue image. [Figure 7A] FIG. 10 is a first conceptual diagram showing the content of the processing performed by the calculation target region specifying unit. [Figure 7B] FIG. 10 is a second conceptual diagram showing the content of the processing performed by the calculation target region specifying unit. [Figure 7C] FIG. 10 is a third conceptual diagram showing the content of the processing performed by the calculation target region specifying unit. [Figure 7D] FIG. 4 is a fourth conceptual diagram showing the content of the processing performed by the calculation target region specifying unit. [Figure 7E] FIG. 5 is a fifth conceptual diagram showing the content of the processing performed by the calculation target region specifying unit. [Figure 7F] FIG. 6 is a sixth conceptual diagram showing the content of the processing performed by the calculation target region specifying unit. [Figure 8] 4 is a flowchart showing the flow of processing performed by the ultrasound diagnostic apparatus according to the present embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0025] 1 is a schematic diagram of the configuration of an ultrasound diagnostic device 10 serving as a blood flow extraction image forming device according to this embodiment. The ultrasound diagnostic device 10 is installed in a medical institution such as a hospital, and forms and displays ultrasound images based on reception signals obtained by transmitting and receiving ultrasound waves to and from a living subject.

[0026] In particular, as will be described in detail later, the ultrasound diagnostic device 10 forms a blood flow extraction image depicting the microvessels (blood flow) of the subject based on an ultrasound signal (a plurality of frame data in this embodiment) indicating the signal intensity of the ultrasound reflected from the subject. That is, the ultrasound diagnostic device 10 has a DFI function.

[0027] The probe 12, which is an ultrasonic probe, is a device that transmits ultrasonic waves and receives reflected waves. Specifically, the probe 12 is placed in contact with the body surface of a subject, transmits ultrasonic waves toward the subject, and receives the reflected waves reflected by tissue within the subject. A transducer element array consisting of a plurality of transducer elements is provided within the probe 12. A transmission signal, which is an electrical signal, is supplied to each transducer element included in the transducer element array from a transmitter 14, which will be described later, thereby generating an ultrasonic beam. Furthermore, each transducer element included in the transducer element array receives reflected waves from the subject, converts the reflected waves into a reception signal, which is an electrical signal, and transmits the received signal to a receiver 16, which will be described later.

[0028] The transmitter 14 functions as a transmit beam former. When transmitting ultrasound, the transmitter 14 supplies multiple transmit signals in parallel to the probe 12 (specifically, the transducer element array). This causes an ultrasonic beam to be transmitted from the probe 12. Specifically, the ultrasonic beam is scanned within a scanning plane based on the transmit signals. In this embodiment, the ultrasonic beam is scanned multiple times (at different timings) on the same scanning plane.

[0029] The receiving unit 16, which serves as a frame data acquisition unit, functions as a receive beam former. When receiving reflected waves, the receiving unit 16 receives multiple received signals in parallel from the probe 12 (specifically, the transducer array). The receiving unit 16 performs processing such as smoothing and summing on the received signals, thereby generating receive beam data. The receive beam data has multiple signal values ​​that indicate the signal strength of reflected waves from each depth in the subject, aligned in the depth direction of the subject. Frame data is generated from multiple receive beam data corresponding to a scanning plane, which corresponds to one B-mode image (a tomographic image in which the amplitude strength of the reflected waves is converted into brightness). As described above, in this embodiment, ultrasound is transmitted multiple times to the same scanning plane, and therefore multiple frame data for multiple frames corresponding to the same scanning plane and obtained at different times is generated.

[0030] The signal processing unit 18 performs various signal processing on the frame data (received beam data) from the receiving unit 16, including detection processing, logarithmic amplification processing, gain correction processing, and filtering processing.

[0031] The cine memory 20 is a memory that stores a plurality of frame data processed by the signal processing unit 18. The cine memory 20 is a FIFO (First In First Out) buffer that outputs the frame data from the signal processing unit 18 in the order in which it was input.

[0032] The blood flow visualization unit 22 forms a blood flow extraction image in which microvessels (blood flow) are extracted based on multiple frame data (ultrasound signals indicating the signal strength of reflected waves) obtained by transmitting ultrasound multiple times to the same scanning plane. Details of the processing performed by the blood flow visualization unit 22 will be described later.

[0033] The display control unit 24 displays various images including the blood flow extraction image formed by the blood flow visualization unit 22 on a display 26 configured by, for example, a liquid crystal panel.

[0034] The input interface 28 is configured by, for example, a button, a trackball, a touch panel, etc. The input interface 28 is used to input user instructions to the ultrasound diagnostic apparatus 10.

[0035] The memory 30 includes a hard disk drive (HDD), a solid state drive (SSD), an embedded multi-media card (eMMC), a read-only memory (ROM), or a random access memory (RAM). The memory 30 stores a blood flow extraction image formation program for operating each unit of the ultrasound diagnostic device 10. The blood flow extraction image formation program can also be stored in a computer-readable non-transitory storage medium such as a USB (Universal Serial Bus) memory or a CD-ROM. The ultrasound diagnostic device 10 can read and execute the blood flow extraction image formation program from such a storage medium.

[0036] The control unit 32 includes at least one of a general-purpose processor (e.g., a CPU (Central Processing Unit)) and a dedicated processor (e.g., a GPU (Graphics Processing Unit), an ASIC (Application Specific Integrated Circuit), an FPGA (Field-Programmable Gate Array), or a programmable logic device). The control unit 32 may not be a single processing unit, but may be configured by the cooperation of multiple processing units located at physically separate locations. The control unit 32 controls each unit of the ultrasound diagnostic apparatus 10 in accordance with a blood flow extraction image formation program stored in the memory 30.

[0037] Each of the transmitting unit 14, receiving unit 16, signal processing unit 18, blood flow visualization unit 22, and display control unit 24 is configured by one or more processors, chips, electric circuits, etc. Each of these units may be realized by a combination of hardware and software.

[0038] 2 is a schematic diagram of the configuration of the blood flow visualization unit 22. Hereinafter, the processing in the blood flow visualization unit 22 will be described in detail with reference to the figures following FIG.

[0039] First, the multiple frame data to be processed by the blood flow visualization unit 22 will be described. FIG. 3 is a conceptual diagram showing multiple frame data F. As described above, a single B-mode image is formed from a single frame data F when the signal intensity of the reflected wave contained therein is converted into brightness. In this specification, the data portions of the frame data F corresponding to each pixel of the B-mode image are referred to as data elements E. In a single frame data F, the data elements E are arranged two-dimensionally in the lateral direction (X-axis) and the depth direction (Z-axis). Each data element E has a signal value (hereinafter, sometimes simply referred to as "signal value") that indicates the signal intensity of the reflected wave.

[0040] As described above, each frame data F is acquired at a different timing, so multiple (N) frame data F are arranged in the time direction. A blood flow extraction image is formed using the N frame data F. The larger N is, the more accurate the blood flow extraction image (the more reduced the clutter components) can be formed, but the amount of calculation increases accordingly. In this embodiment, N is set to a value of approximately 10 to 20.

[0041] Since multiple frame data F are arranged in the time direction, when focusing on one data element E, it is possible to define a set of corresponding data elements E between multiple frame data F, that is, a set of data elements E arranged in the time direction (N data elements E). In this specification, a vector having such N data elements E as elements is defined, and this is called vector u zx For example, u 11 is a vector whose elements are N data elements E located at (z,x)=(1,1) of each frame data F. And, the vector u zx A matrix having elements (z=1 to Z, x=1 to X) is referred to as a matrix U. The matrix U indicates a plurality of frame data F for forming a blood flow extraction image, and is expressed as follows:

number

[0042] The correlation matrix calculation unit 40 calculates a correlation matrix that indicates the correlation of signal values ​​in a plurality of frames of data F for each data element E of the frame data F. That is, the correlation matrix calculation unit 40 calculates a correlation matrix that indicates the correlation of signal values ​​in a plurality of frames of data F for each vector u zx For vector u zx In this embodiment, the correlation matrix calculation unit 40 calculates the correlation between each element included in the entire frame data F (i.e., all vectors u zx Calculate the correlation matrix for (z=1 to Z, x=1 to X).

[0043] The correlation matrix (covariance matrix with itself) is the vector u zx and its transpose, the correlation matrix R zx is calculated using the following formula 1.

number

[0044] Furthermore, the correlation matrix calculation unit 40 calculates each vector u zx Each correlation matrix R zx is averaged over all data elements E. The average correlation matrix is ​​expressed by the following Equation 2.

number

[0045] The singular value decomposition calculation unit 42 performs singular value decomposition on the correlation matrix calculated by the correlation matrix calculation unit 40. In this embodiment, the singular value decomposition calculation unit 42 performs singular value decomposition on the average correlation matrix. In this embodiment, since the average correlation matrix is ​​a square matrix, the singular value decomposition calculation unit 42 performs eigenvalue decomposition on the average correlation matrix. The eigenvalue decomposition of the average correlation matrix is ​​expressed by the following equation 3. Note that a case where the matrix to be subjected to singular value decomposition is a non-square matrix will be described later.

number

number

[0046] In the matrix Λ, there are multiple eigenvalues ​​λ i are sorted in order of magnitude, i.e., λ1>λ2>...>λ N In addition, multiple eigenvalues ​​λ i The ranks of eigenvalues ​​are defined in the order of their magnitude. For example, λ1 is the rank 1 eigenvalue, λ2 is the rank 2 eigenvalue, and λ N is the eigenvalue of rank N, and so on.

[0047] Here, the eigenvalues ​​λ of higher rank (closer to rank 1) i and the corresponding eigenvector w i ,w i H contains information that better reproduces the average correlation matrix, and the lower-ranked (closer to rank N) eigenvalues ​​λ i and the corresponding eigenvector w i ,w i H will contain information that is not very relevant to reproducing the average correlation matrix.

[0048] The image forming unit 44 forms a blood flow extraction image in which the subject's microvessels (blood flow) are depicted based on the matrix U. As shown in FIG. 2, the image forming unit 44 functions as a blood flow brightness image forming unit 44a, a tissue image forming unit 44b, and a blood flow extraction image forming unit 44c. FIG. 4 is a conceptual diagram showing the flow of the blood flow extraction image formation process by the image forming unit 44. Details of each function of the image forming unit 44 will be explained below, so please refer to FIG. 4 as appropriate.

[0049] The blood flow intensity image forming unit 44a first calculates the plurality of eigenvalues ​​λ calculated by the singular value decomposition calculation unit 42. i and eigenvector w i ,w i H Among them, the eigenvalues ​​λ of ranks equal to or lower than the first threshold rank set in advance i and the eigenvalue λ i The eigenvector w corresponding to i ,w i H A blood flow extraction filter is configured based on the above. In this specification, the expression "less than or equal to the threshold rank" means a rank greater than the threshold rank. For example, less than or equal to the threshold rank k means ranks k to N. Similarly, the expressions "more than or equal to the threshold rank" or "greater than the threshold rank" mean a rank less than the threshold rank. For example, more than or equal to the threshold rank k means ranks 1 to k.

[0050] Specifically, the blood flow extraction filter P k,Nis expressed by the following equation 4.

number

[0051] The eigenvalue λ of the lower rank (rank below the first threshold rank) obtained by singular value decomposition of the average correlation matrix i and the eigenvalue λ i The eigenvector w corresponding to i ,w i H The blood flow extraction filter P is constructed based on k,N When applied to a plurality of frame data F (i.e., matrix U), this becomes a filter that extracts blood flow components that represent microvessels (blood flow) and cuts out tissue components that represent the tissue of the subject.

[0052] Next, the blood flow luminance image forming unit 44a applies the blood flow extraction filter P k,N By applying k,N In this embodiment, the blood flow intensity image forming unit 44a applies a blood flow extraction filter P k,N Then, by averaging the signal values ​​of multiple frames of data F after application, a blood flow brightness image U is obtained. k,N The blood flow intensity image U k,N is expressed by the following equation 5.

number

[0053] As mentioned above, the blood flow extraction filter P k,Nis a filter that, when applied to matrix U, extracts blood flow components that represent microvessels (blood flow) and cuts tissue components that represent the tissue of the subject. Therefore, when applying the blood flow extraction filter P k,N By applying this, the tissue component is cut off and the blood flow component remains. Therefore, the blood flow intensity image U k,N However, as mentioned above, the blood flow intensity image U k,N may contain clutter components, which are signal components derived from tissues due to body movements of the subject, etc.

[0054] The tissue image forming unit 44b forms a tissue image including tissue components based on the signal values ​​of each data element E that constitutes the plurality of frame data F (that is, the matrix U).

[0055] In this embodiment, the tissue image forming unit 44b first calculates the plurality of eigenvalues ​​λ calculated by the singular value decomposition calculation unit 42. i and eigenvector w i ,w i H Among them, the eigenvalue λ of the rank greater than the first threshold rank i and the eigenvalue λ i The eigenvector w corresponding to i ,w i H A tissue extraction filter is constructed based on the above.

[0056] Specifically, the tissue extraction filter P 2,N is expressed as the following equation 6.

number

[0057] The top-ranked eigenvalue λ obtained by singular value decomposition of the average correlation matrix i and the eigenvalue λ i The eigenvector w corresponding to i ,w i H The tissue extraction filter P constructed based on 2,N When applied to a plurality of frame data F (i.e., matrix U), it becomes a filter that extracts tissue components that represent the tissue of the subject and cuts blood flow components that represent the microvessels (blood flow).

[0058] Next, the tissue image forming unit 44b applies a tissue extraction filter P 2,N By applying 2,N In this embodiment, the tissue image forming unit 44b applies a tissue extraction filter P 2,N and obtain the variance of each signal value of the multiple frame data F after application, thereby obtaining the tissue image U 2,N Forming a tissue image U 2,N is expressed by the following equation 7.

number

[0059] As mentioned above, the tissue extraction filter P 2,Nis a filter that, when applied to matrix U, extracts tissue components that represent the tissue of the subject and cuts off blood flow components that represent the microvessels (blood flow). Therefore, when applied to matrix U, the tissue extraction filter P 2,N By applying this, the blood flow component is cut off and the tissue component remains. Therefore, the tissue image U 2,N is an image that represents the tissue components. 2,N contains clutter components, which are signal components derived from tissues caused by the body movement of the subject.

[0060] In particular, the tissue extraction filter P 2,N The variance of each signal value of the plurality of frame data F after application of the filter of the rank contains many clutter components. FIG. 5 is a graph showing the relationship between the rank of a filter composed of eigenvalues ​​and eigenvectors and the variance of each signal value of the plurality of frame data F after application of the filter of that rank. Of the graphs shown in FIG. 5, graph A represents the clutter components, graph B represents the tissue components, graph C represents the blood flow components, and graph D represents the branch components corresponding to the branching parts of the blood flow. As shown in FIG. 5, the variance of graph A is large at higher ranks. In other words, the eigenvalue λ of the higher ranks i and eigenvector w i ,w i H The tissue extraction filter P 2,N The tissue image U obtained by applying 2,N contains not only tissue components but also many clutter components.

[0061] Graph A in FIG. 5 shows that the variance of clutter components is larger at a rank slightly below rank 1 (for example, rank 2) than at rank 1. This is because signal components extracted by a rank 1 filter tend to contain many signal components that are stable over time, while ranks 2 and higher contain many signal components that fluctuate over time and space. Therefore, in this embodiment, the eigenvalue λ2 and eigenvectors w2, w2 of rank 2 are H Tissue extraction filter P based on 2,N It is composed of:

[0062] Tissue extraction filter P 2,N The relationship between the variance of each signal value of the plurality of frame data F after application of the filter rank is as shown in FIG. 5. 2,N The relationship between the average value of each signal value of the plurality of frame data F after application of the filter rank also shows a relationship similar to that shown in FIG. 5. Therefore, the tissue extraction filter P 2,N The tissue image U is obtained by averaging the signal values ​​of the multiple frame data F after applying 2,N may be formed.

[0063] As described above, in this embodiment, the tissue image forming unit 44b uses the tissue extraction filter P 2,N Based on tissue image U 2,N However, a tissue image may be formed without forming a tissue extraction filter. Specifically, a tissue image may be formed by taking the variance of each signal value (i.e., matrix U) of a plurality of frame data F. In this case, the tissue image U org,N is formed by the following equation 8:

number

[0064] As will be described later, in order to form a blood flow extraction image with reduced clutter components, it is preferable that the tissue image contains a large amount of clutter components. As shown in Figure 5, the variance value of the clutter components (graph A) is relatively large from the top rank to the bottom rank. Therefore, even if the variance value of the matrix U is taken without applying a tissue extraction filter, it is possible to obtain a tissue image U containing a large amount of clutter components. org,N can be obtained.

[0065] The blood flow extraction image forming unit 44c extracts the blood flow intensity image U formed by the blood flow intensity image forming unit 44a. k,N From this, the tissue image U formed by the tissue image forming unit 44b 2,N (or tissue image U org,N ) is subtracted to form a blood flow extracted image.out is expressed by the following equation 9.

number

[0066] As mentioned above, the blood flow intensity image U k,N may contain clutter components, while the tissue image U 2,N In the tissue image U, the blood flow component is cut off and many clutter components are included. org,N In the matrix U, the blood flow component is not cut off, but the signal intensity of the blood flow component is lower than that of the clutter component. k,N Tissue images from U 2,N (or tissue image U org,N ) to obtain the blood flow intensity image U k,N The blood flow extracted image U is obtained by removing clutter components while leaving the blood flow components. out can be obtained.

[0067] α in Equation 9 is the tissue image U 2,N (or tissue image U org,N ) in the present embodiment. k,N From the tissue image U multiplied by the coefficient 2,N (or tissue image U org,N ) is subtracted to obtain the blood flow extracted image U out By adjusting the coefficient α, the blood flow intensity image U k,N In other words, by increasing the coefficient α, the amount of signal to be subtracted from the blood flow intensity image U k,N The clutter component is further reduced from the coefficient α, but the blood flow component and tissue component may also be further reduced. k,N Therefore, the clutter components are not reduced much, but the reduction in the blood flow components and tissue components is also suppressed.

[0068] The blood flow extraction image forming unit 44c may determine the coefficient α based on an input from the user of the ultrasound diagnostic device 10. This allows the user to generate the blood flow intensity image Uk,N A blood flow extracted image U with a desired amount of signal subtracted from out For example, the display control unit 24 may display a coefficient adjustment operator (slide bar) 50 on the display 26 as shown in FIGS. 6A and 6B, and the user may adjust the coefficient α by operating the coefficient adjustment operator 50 using the input interface 28. In addition, by changing the coefficient α, the blood flow extraction image U out In order to allow the user to easily understand what the blood flow extraction image 52 calculated with the adjusted coefficient α will be, the display control unit 24 may also display, on the screen, a blood flow extraction image 52 calculated with the adjusted coefficient α.

[0069] Returning to FIG. 2, the blood flow visualization unit 22 may have a calculation target region specification unit 46. In the above-described embodiment, the correlation matrix calculation unit 40 calculates the correlation matrix of the entire frame data F (all vectors u zx Correlation matrix R for (z=1~Z, x=1~X) zx However, the correlation matrix R zx is obtained by singular value decomposition as the blood flow extraction filter P k,N or tissue extraction filter P 2,N Therefore, the correlation matrix R zx As a result of singular value decomposition, a tissue extraction filter P 2,N can be constructed, and a blood flow extraction filter P k,N is possible to construct, the correlation matrix R for the entire frame data F is not necessarily zx Therefore, according to this configuration, it is not necessary to obtain the correlation matrix R zx Since the calculation target of is limited, the correlation matrix R zx The amount of calculation required to obtain the above equation can be reduced.

[0070] In view of the above, the calculation target region specifying unit 46 determines whether the correlation matrix calculation unit 40 calculates the correlation matrix R zx Before calculating, some data elements E (vector u zx ) into the correlation matrix R zxThe correlation matrix calculation unit 40 calculates the correlation matrix R zx As a result, the correlation matrix R zx The amount of calculation required to obtain the average correlation matrix R zx The amount of calculation required to obtain the above equation is reduced.

[0071] The calculation target region specifying unit 46 can specify the calculation target region by various methods. However, if the calculation target region includes only data elements E corresponding to microvessels (blood flow), the correlation matrix calculation unit 40 uses the tissue extraction filter P 2,N and blood flow extraction filter P k,N An appropriate correlation matrix R to construct zx Therefore, the calculation target region specifying unit 46 specifies a calculation target region that includes at least the data element E corresponding to the tissue.

[0072] As a first method for specifying the calculation target region, the calculation target region specifying unit 46 can set thinned frame data obtained by thinning out some of the data elements E that make up the frame data F as the calculation target region, as shown in Fig. 7A. One example of a thinning method is to arrange data elements E to be included in the calculation target region and data elements E not to be included in the calculation target region alternately in the X-axis direction and the Z-axis direction (see Fig. 3). However, any thinning method may be used as long as the calculation target region includes data elements E that correspond to tissue.

[0073] As a second method for identifying the calculation target region, the calculation target region identifying unit 46 can identify, as the calculation target region, a set of data elements E whose signal values ​​are equal to or greater than a threshold signal value, among the multiple data elements E that make up the frame data F, as shown in FIG. 7B. In general, the signal values ​​of data elements E corresponding to tissue are relatively large. Therefore, by setting the set of data elements E whose signal values ​​are equal to or greater than a threshold signal value as the calculation target region, it is possible to identify a calculation target region that includes data elements E corresponding to tissue.

[0074] As a third method for identifying the calculation target region, the calculation target region identifying unit 46 can use a combination of the first and second identification methods. That is, as shown in Fig. 7C, the calculation target region identifying unit 46 can extract a set of data elements E whose signal values ​​are equal to or greater than a threshold signal value from among the multiple data elements E constituting frame data F, and then thin out some of the extracted multiple data elements E to set the thinned frame data as the calculation target region. Alternatively, the calculation target region identifying unit 46 can thin out some of the data elements E constituting frame data F to obtain thinned frame data, and then set out the set of data elements E whose signal values ​​are equal to or greater than a threshold signal value from among the multiple data elements E constituting the thinned frame data.

[0075] As a fourth method for identifying the calculation target region, the calculation target region identifying unit 46 first performs singular value decomposition on multiple frame data F. As a result of this singular value decomposition, higher ranks contain many signal components with large signal values ​​(e.g., tissue components), while lower ranks contain many signal components with small signal values ​​(e.g., blood flow components). Therefore, as shown in FIG. 7D , the calculation target region identifying unit 46 first acquires higher-rank extracted data composed of signal components with higher ranks (more specifically, ranks greater than a predetermined second threshold rank) obtained by singular value decomposition. The higher-rank extracted data is data that primarily represents tissue components. Then, the calculation target region identifying unit 46 can designate, as the calculation target region, thinned frame data obtained by thinning out some of the data elements E constituting the acquired higher-rank extracted data.

[0076] As a fifth method for identifying the area to be calculated, the calculation target area identification unit 46 acquires higher-rank extracted data in the same manner as in the fourth identification method, and as shown in Figure 7E, the calculation target area identification unit 46 can identify, as the area to be calculated, a set of data elements E whose signal values ​​are equal to or greater than a threshold signal value among the multiple data elements E that make up the acquired higher-rank extracted data.

[0077] As a sixth method for identifying the calculation target region, the calculation target region identifying unit 46 can use a combination of the fourth and fifth identification methods. That is, as shown in FIG. 7F , the calculation target region identifying unit 46 can acquire high-rank extracted data, extract a set of data elements E whose signal values ​​are equal to or greater than a threshold signal value from among the multiple data elements E constituting the acquired high-rank extracted data, and then set the thinned frame data obtained by thinning out some of the extracted multiple data elements E as the calculation target region. Alternatively, the calculation target region identifying unit 46 can acquire thinned frame data obtained by thinning out some of the data elements E constituting the acquired high-rank extracted data, and then set the set of data elements E whose signal values ​​are equal to or greater than a threshold signal value from among the multiple data elements E constituting the thinned frame data as the calculation target region.

[0078] As mentioned above, the correlation matrix R zx is the blood flow extraction filter P k,N or tissue extraction filter P 2,N Therefore, as in this embodiment, the blood flow intensity image U k,N and tissue image U 2,N Based on the blood flow extraction image U out When performing the process of obtaining the correlation matrix R, the calculation target region specifying unit 46 specifies the calculation target region. zx In addition to the effect of reducing the amount of calculation required to obtain the blood flow extraction filter P k,N or tissue extraction filter P 2,N Even when various processes are performed using the correlation matrix R zx For example, when the image forming unit 44 is configured to use the blood flow extraction filter P k,N is applied to the frame data F (e.g., matrix U) to obtain the blood flow intensity image U k,N The display control unit 24 performs a blood flow luminance image formation process to form the blood flow luminance image U k,N is displayed on the display 26, or the image forming unit 44 uses the tissue extraction filter P 2,N is applied to the frame data F (e.g., matrix U) to obtain the tissue image U 2,NThe display control unit 24 performs a tissue image formation process to form the tissue image U 2,N Even when displaying the correlation matrix R zx This has the effect of reducing the amount of calculation required to obtain the above equation.

[0079] In the above embodiment, the correlation matrix R zx (more specifically, the average correlation matrix) is a square matrix, and the singular value decomposition calculation unit 42 has performed eigenvalue decomposition on the square matrix. However, the matrix to be subjected to singular value decomposition is a non-square matrix, and the singular value decomposition calculation unit 42 may be configured to perform singular value decomposition on the non-square matrix.

[0080] In this case, the correlation matrix calculation unit 40 generates a spatiotemporal matrix S as a correlation matrix, in which space (ZX (Z-axis direction and X-axis direction in FIG. 3)) is arranged in the row direction and time (N) is arranged in the column direction, based on a plurality of frame data F (i.e., matrix U) stored in the cine-memory 20. The spatiotemporal matrix S is a ZX×N matrix, and is a non-square matrix. The singular value decomposition calculation unit 42 performs singular value decomposition on the spatiotemporal matrix S, which is a non-square matrix. The singular value decomposition here is expressed by the following equation 10:

number

[0081] The blood flow intensity image forming unit 44a calculates the singular values ​​λ of the ranks equal to or lower than the first threshold rank. i and the singular value λ i The singular vector v corresponding to i ,w i H is extracted as a blood flow component, and the matrix is ​​retransformed so that each element becomes a vector arranged in the N direction, with the row direction being X and the column direction being Z, to obtain the blood flow component matrix S k,N The blood flow component matrix S k,N is expressed by the following equation 11.

number

[0082] Next, the blood flow intensity image forming unit 44a calculates the blood flow component matrix S k,N By averaging these, the blood flow intensity image S k,avg A blood flow intensity image S k,avg is expressed by the following equation 12.

number

[0083] The tissue image forming unit 44b calculates the singular value λ2 of a rank (here, rank 2) greater than the first threshold rank and the singular vectors v2 and w2 corresponding to the singular value λ2. H is extracted as a tissue component, and the matrix is ​​retransformed so that each element becomes a vector arranged in the N direction, with the row direction being X and the column direction being Z, to obtain the tissue component matrix S 2,N The tissue component matrix S 2,N is expressed by the following equation 13.

number

[0084] Next, the tissue image forming unit 44b calculates the tissue component matrix S 2,N By obtaining the variance of 2,var Forming a tissue image S 2,var is expressed by the following equation 14.

number

[0085] The blood flow extraction image forming unit 44c extracts the blood flow intensity image S formed by the blood flow intensity image forming unit 44a. k,avg From this, the tissue image S formed by the tissue image forming unit 44b 2,var By subtracting out The blood flow extraction image U out is expressed by the following equation 15.

number

[0086] The outline of the configuration of the ultrasound diagnostic apparatus 10 according to this embodiment has been described above. The flow of processing by the ultrasound diagnostic apparatus 10 will now be described with reference to the flowchart shown in FIG.

[0087] In step S10, the probe 12 transmits ultrasonic waves multiple times to the same scanning plane in response to a transmission signal from the transmitter 14, and the receiver 16 generates multiple frame data F based on multiple received signals obtained by receiving the ultrasonic waves reflected from the subject. This allows multiple frame data F to be acquired, each having a signal value indicating the signal strength of the reflected wave. Step S10 corresponds to a frame data acquisition step.

[0088] In step S12, the calculation target region specifying unit 46 calculates the correlation matrix R in the frame data F by one of the above-mentioned methods. zx Identify the region to be calculated.

[0089] In step S14, the correlation matrix calculation unit 40 calculates a correlation matrix R that indicates the correlation of signal values ​​in a plurality of frames of data F for each data element E included in the calculation target region identified in step S12 among the data elements E included in the frame data F acquired in step S10. zx (see equation 1). Step S14 corresponds to a correlation matrix calculation step.

[0090] In step S16, the correlation matrix calculation unit 40 calculates the correlation matrix for each vector u zx Each correlation matrix R zx is averaged over all data elements E to obtain the average correlation matrix (see Equation 2).

[0091] In step S18, the singular value decomposition calculation unit 42 performs singular value decomposition on the average correlation matrix obtained in step S16 to obtain a plurality of singular values ​​(here, eigenvalues) λ i and multiple singular vectors w corresponding to each eigenvalue i ,w i H (Here, eigenvectors) are calculated (see Equation 3). Step S18 corresponds to the singular value decomposition calculation step.

[0092] In step S20, the blood flow intensity image forming unit 44a calculates the plurality of eigenvalues ​​λ calculated in step S18. i and eigenvector w i ,w i H Among them, the eigenvalues ​​λ of ranks equal to or lower than the first threshold rank set in advance i and the eigenvalue λ i The eigenvector w corresponding to i ,w i H Based on the blood flow extraction filter P k,N (See Equation 4).

[0093] In step S22, the blood flow intensity image forming unit 44a applies a blood flow extraction filter P k,N Then, by averaging the signal values ​​of multiple frames of data F after application, a blood flow brightness image U is obtained. k,N (See Equation 5). Step S20 corresponds to a blood flow intensity image formation step.

[0094] In step S24, the tissue image forming unit 44b calculates the plurality of eigenvalues ​​λ calculated in step S18. i and eigenvector w i ,wi H Among them, the eigenvalue λ of the rank greater than the first threshold rank (here, rank 2) i and the eigenvalue λ i The eigenvector w corresponding to i ,w i H Based on the tissue extraction filter P 2,N (See Equation 6).

[0095] In step S26, the tissue image forming unit 44b applies a tissue extraction filter P to each of the plurality of frame data F represented by the matrix U. 2,N and obtain the variance of each signal value of the multiple frame data F after application, thereby obtaining the tissue image U 2,N (see Equation 7). Step S26 corresponds to a tissue image formation step.

[0096] In step S28, the blood flow extraction image forming unit 44c extracts the blood flow intensity image U formed in step S22. k,N From the tissue image U formed in step S26 2,N By subtracting out (See Equation 9). Step S28 corresponds to a blood flow extraction image formation step.

[0097] In step S30, the display control unit 24 displays the extracted blood flow image U formed in step S28. out is displayed on the display 26.

[0098] The blood flow extraction image forming device according to the present disclosure has been described above, but the blood flow extraction image forming device according to the present disclosure is not limited to the above embodiment, and various modifications are possible as long as they do not deviate from the spirit of the present invention.

[0099] For example, in this embodiment, the blood flow extraction image forming device is the ultrasound diagnostic device 10, but the blood flow extraction image forming device is not limited to the ultrasound diagnostic device 10 and may be another computer. In this case, the computer as the blood flow extraction image forming device performs the function of the blood flow visualization unit 22. Specifically, the computer as the blood flow extraction image forming device receives a plurality of frame data from the ultrasound diagnostic device, and calculates a correlation matrix R zx Calculation of blood flow extraction filter P k,N and tissue extraction filter P 2,N and the blood flow intensity image U k,N , tissue image U 2,N (or tissue image U org,N ), and blood flow extraction image U out The formation process is performed.

[0100] Furthermore, in the above embodiment, the processing target of the blood flow rendering unit 22 is a plurality of frame data F, and the matrix U indicates the signal value of each data element of each frame data F. However, the processing target of the blood flow rendering unit 22 may also be an ultrasound image (B-mode image) generated by converting the signal values ​​of the plurality of frame data F into brightness values. In this case, the matrix U indicates the brightness value of each pixel of each ultrasound image. [Explanation of symbols]

[0101] 10 Ultrasound diagnostic device, 12 Probe, 14 Transmitter, 16 Receiver, 18 Signal processing unit, 20 Cine memory, 22 Blood flow visualization unit, 24 Display control unit, 26 Display, 28 Input interface, 30 Memory, 32 Control unit, 40 Correlation matrix calculation unit, 42 Singular value decomposition calculation unit, 44 Image formation unit, 44a Blood flow brightness image formation unit, 44b Tissue image formation unit, 44c Blood flow extraction image formation unit, 46 Calculation target region identification unit.

Claims

1. a frame data acquisition unit that acquires a plurality of frames of frame data, each frame having a signal value indicating the signal intensity of a reflected wave, generated based on a plurality of received signals obtained by an ultrasonic probe that transmits and receives ultrasonic waves to and from a subject, the ultrasonic probe transmitting and receiving ultrasonic waves to and from the subject, the frame data acquisition unit acquiring a plurality of frames of frame data, each frame having a signal value indicating the signal intensity of the reflected wave; a correlation matrix calculation unit that calculates a correlation matrix indicating a correlation of the signal values ​​in the plurality of frame data for each data element of the frame data corresponding to each pixel of an ultrasound image formed from the frame data; a singular value decomposition calculation unit that calculates a plurality of singular values, ranks of which are defined in order of magnitude, and a plurality of singular vectors corresponding to each singular value, by performing singular value decomposition on the correlation matrix; a blood flow intensity image forming unit that applies a blood flow extraction filter configured from the singular values ​​of a rank equal to or lower than a first threshold rank set in advance and the singular vectors corresponding to the singular values ​​to the frame data to form a blood flow intensity image; a tissue image forming unit that forms a tissue image based on the signal values ​​of each of the data elements that constitute the plurality of frame data; a blood flow extraction image forming unit that subtracts the tissue image from the blood flow brightness image to form a blood flow extraction image; A blood flow extraction image forming apparatus comprising:

2. the tissue image forming unit applies a tissue extraction filter configured from the singular values ​​of ranks greater than the first threshold rank and the singular vectors corresponding to the singular values ​​to the frame data to form the tissue image.

2. The blood flow extraction image forming apparatus according to claim 1.

3. a calculation target region specifying unit for specifying a calculation target region including the data element corresponding to tissue in the frame data; Furthermore, the correlation matrix calculation unit calculates the correlation matrix within the calculation target region.

2. The blood flow extraction image forming apparatus according to claim 1.

4. the calculation target region specifying unit specifies thinned frame data obtained by thinning out some of the data elements constituting the frame data as the calculation target region; 4. The blood flow extraction image forming apparatus according to claim 3.

5. the calculation target region specifying unit specifies, as the calculation target region, a set of data elements whose signal values ​​are equal to or greater than a threshold signal value, among the plurality of data elements constituting the frame data; 5. The blood flow extraction image forming apparatus according to claim 3 or 4.

6. the calculation target region specifying unit performs singular value decomposition on the plurality of frame data to obtain higher rank extracted data composed of signal components with ranks greater than a second threshold rank, and sets thinned frame data obtained by thinning out some of the data elements constituting the higher rank extracted data as the calculation target region.

4. The blood flow extraction image forming apparatus according to claim 3.

7. the calculation target region identification unit acquires higher rank extracted data composed of signal components of ranks greater than a second threshold rank by performing singular value decomposition on the plurality of frame data, and identifies, as the calculation target region, a set of data elements of which signal values ​​are equal to or greater than a threshold signal value, among the plurality of data elements constituting the higher rank extracted data; 7. The blood flow extraction image forming apparatus according to claim 3 or 6.

8. the blood flow extraction image forming unit forms the blood flow extraction image by subtracting the tissue image multiplied by the coefficient from the blood flow brightness image; 3. The blood flow extraction image forming apparatus according to claim 1, wherein the blood flow extraction image forming apparatus is a blood flow extraction image forming apparatus.

9. the blood flow extraction image forming unit determines the coefficients based on an input from a user; 9. The blood flow extraction image forming apparatus according to claim 8.

10. a frame data acquisition unit that acquires a plurality of frames of frame data, each frame having a signal value indicating the signal intensity of a reflected wave, generated based on a plurality of received signals obtained by an ultrasonic probe that transmits and receives ultrasonic waves to and from a subject, the ultrasonic probe transmitting and receiving ultrasonic waves to and from the subject, the frame data acquisition unit acquiring a plurality of frames of frame data, each frame having a signal value indicating the signal intensity of the reflected wave, the frame data being generated based on a plurality of received signals obtained by receiving reflected waves of the ultrasonic waves from the subject; a calculation target region specifying unit that specifies a calculation target region in the frame data, the calculation target region including data elements corresponding to tissues, the data elements being data elements of the frame data corresponding to each pixel of an ultrasound image formed from the frame data; a correlation matrix calculation unit that calculates a correlation matrix indicating a correlation of the signal values ​​in the plurality of frame data for each of the data elements that constitute the frame data, the correlation matrix calculation unit performing calculation of the correlation matrix within the calculation target region; a singular value decomposition calculation unit that calculates a plurality of singular values, ranks of which are defined in order of magnitude, and a plurality of singular vectors corresponding to each singular value, by performing singular value decomposition on the correlation matrix; an image processing unit that executes at least one of a blood flow intensity image forming process that applies a blood flow extraction filter made up of the singular values ​​of a rank equal to or less than a first threshold rank and the singular vectors corresponding to the singular values ​​to the frame data to form a blood flow intensity image, and a tissue image forming process that applies a tissue extraction filter made up of the singular values ​​of a rank greater than the first threshold rank and the singular vectors corresponding to the singular values ​​to the frame data to form a tissue image; A blood flow extraction image forming apparatus comprising:

11. a frame data acquisition step of acquiring a plurality of frames of frame data, each frame having a signal value indicating the signal intensity of a reflected wave, generated based on a plurality of received signals obtained by transmitting ultrasonic waves to and receiving ultrasonic waves from the subject, using an ultrasonic probe that transmits and receives ultrasonic waves to and from the subject, to the same scanning plane a plurality of times; a correlation matrix calculation step of calculating a correlation matrix indicating a correlation of the signal values ​​in the plurality of frame data for each data element of the frame data corresponding to each pixel of an ultrasound image formed from the frame data; a singular value decomposition calculation step of calculating a plurality of singular values, ranks of which are defined in order of magnitude, and a plurality of singular vectors corresponding to each singular value, by performing singular value decomposition on the correlation matrix; a blood flow intensity image forming step of applying a blood flow extraction filter configured from the singular values ​​of a rank equal to or lower than a first threshold rank set in advance and the singular vectors corresponding to the singular values ​​to the frame data to form a blood flow intensity image; a tissue image forming step of forming a tissue image based on the signal values ​​for each of the data elements constituting the plurality of frame data; a blood flow extraction image forming step of forming a blood flow extraction image by subtracting the tissue image from the blood flow brightness image; A blood flow extraction image forming method comprising:

12. Computer, a frame data acquisition unit that acquires a plurality of frames of frame data, each frame having a signal value indicating the signal intensity of a reflected wave, generated based on a plurality of received signals obtained by an ultrasonic probe that transmits and receives ultrasonic waves to and from a subject, the ultrasonic probe transmitting and receiving ultrasonic waves to and from the subject, the frame data acquisition unit acquiring a plurality of frames of frame data, each frame having a signal value indicating the signal intensity of the reflected wave; a correlation matrix calculation unit that calculates a correlation matrix indicating a correlation of the signal values ​​in the plurality of frame data for each data element of the frame data corresponding to each pixel of an ultrasound image formed from the frame data; a singular value decomposition calculation unit that calculates a plurality of singular values, ranks of which are defined in order of magnitude, and a plurality of singular vectors corresponding to each singular value, by performing singular value decomposition on the correlation matrix; a blood flow intensity image forming unit that applies a blood flow extraction filter configured from the singular values ​​of a rank equal to or lower than a first threshold rank set in advance and the singular vectors corresponding to the singular values ​​to the frame data to form a blood flow intensity image; a tissue image forming unit that forms a tissue image based on the signal values ​​for each of the data elements that constitute the plurality of frame data; a blood flow extraction image forming unit that subtracts the tissue image from the blood flow brightness image to form a blood flow extraction image; A blood flow extraction image formation program characterized by functioning as follows.

Images