Ultrasound diagnostic equipment, medical image processing equipment, and medical image processing program

Abstract

To enhance visibility of a biological structure in a medical image.SOLUTION: An ultrasonic diagnosis device includes: a multiple resolution decomposition unit which generates a plurality of pieces of low frequency data and high frequency data corresponding to a plurality of gradual resolutions from ultrasonic data; an extraction unit which extracts negative high frequency data across a prescribed resolution among the plurality of gradual resolutions from the plurality of pieces of high frequency data; a multiple resolution restoring unit which restores a resolution of first addition data obtained by adding resolutions of the negative high frequency data across the prescribed resolutions while matching the resolution to the same resolution as the resolution before reduction of the ultrasonic data; a synthesizing unit which generates a synthesized image on the basis of the restored first addition data and the ultrasonic data; and a display unit which displays the synthesized image.SELECTED DRAWING: Figure 2

Description

Technical Field

[0001] The embodiments disclosed in this specification and the drawings relate to an ultrasonic diagnostic apparatus, a medical image processing apparatus, and a medical image processing program.

Background Art

[0002] Conventionally, ultrasonic images may include artifacts such as multiple and side lobes together with biological information. This artifact hinders the diagnosis of biological structures and characteristics. Therefore, suppression of the generation of artifacts and suppression of the display of artifacts are desired. Conventionally, suppression of display has been performed by operations such as Dynamic Range operation and change of Color Map. This utilizes the fact that in many ultrasonic data, artifacts have a signal intensity (mostly low) different from that of biological information, and by emphasizing the difference between the signal intensity of biological information and the signal intensity of artifacts and displaying them, the visibility of biological information is improved.

[0003] Suppression of artifacts by conventional methods is a change in display settings. Therefore, it is difficult to appropriately obtain the artifact suppression effect unless the display settings are appropriately made each time according to the characteristics of the acquired ultrasonic data. Also, depending on the characteristics of the ultrasonic data, the overlap between the signal intensity of biological information and the signal intensity of artifacts becomes large, and it is difficult to improve visibility by emphasizing the difference in signal intensity. As another method for improving visibility, there is a method of improving contrast using a technique related to transmit aperture synthesis.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] One of the problems that the embodiments disclosed herein and in the drawings aim to solve is to improve the visibility of biological structures in medical images. However, the problems that the embodiments disclosed herein and in the drawings aim to solve are not limited to the above problem. Problems corresponding to the effects of each configuration shown in the embodiments described later can also be positioned as other problems. [Means for solving the problem]

[0006] The ultrasound diagnostic apparatus according to this embodiment comprises a multi-resolution decomposition unit, an extraction unit, a multi-resolution restoration unit, and a display unit. The multi-resolution decomposition unit generates a plurality of low-frequency data and high-frequency data corresponding to a plurality of stepped resolutions from ultrasound data. The extraction unit extracts negative high-frequency data from the plurality of high-frequency data over a predetermined resolution. The multi-resolution restoration unit restores the resolution of a first summation data obtained by adding the negative high-frequency data over the predetermined resolution to the same resolution as the ultrasound data before reduction. The synthesis unit generates a composite image based on the restored first summation data and the ultrasound data. The display unit displays the composite image. [Brief explanation of the drawing]

[0007] [Figure 1] Figure 1 shows an example of the configuration of an ultrasound diagnostic device according to the first embodiment. [Figure 2] Figure 2 is a diagram showing an example of multi-resolution decomposition according to the first embodiment. [Figure 3] Figure 3 is a diagram illustrating an example of various data corresponding to multiple hierarchical levels, relating to the first embodiment. [Figure 4] Figure 4 shows an example of a process performed by the extraction function according to the first embodiment, which uses a Laplacian pyramid, an example of a multi-resolution decomposition that generates high-frequency data. [Figure 5] Figure 5 shows an example of the first embodiment in which the fifth negative high-frequency data is extracted in the sixth and fifth layers based on the fifth reduced data and the sixth reduced data. [Figure 6] Figure 6 shows an example of the first embodiment in which the fifth negative high-frequency data is extracted from the fifth high-frequency data calculated using the fifth reduced data and the enlarged sixth reduced data. [Figure 7] Figure 7 is a diagram illustrating an example of processing by the restoration function according to the first embodiment. [Figure 8] Figure 8 is a diagram showing an example of an image related to the process shown in Figure 7, relating to the first embodiment. [Figure 9] Figure 9 is a diagram showing an example of processing related to the weighting function, synthesis function, and brightness correction function according to the first embodiment. [Figure 10] Figure 10 is a flowchart illustrating an example of the procedure for contrast improvement processing according to the first embodiment. [Figure 11] Figure 11 shows an example of a comparison with and without the use of contrast enhancement processing according to the first embodiment. [Figure 12] Figure 12 is a diagram illustrating an example of the effect of contrast improvement processing according to the first embodiment. [Figure 13] Figure 13 is a diagram illustrating a first application example of the first embodiment, in which a second weight is assigned to negative high-frequency data according to the hierarchy. [Figure 14] Figure 14 is a diagram illustrating an example of a process in which positive reconstructed data is generated based on positive high-frequency data extracted by the extraction function, relating to a second application example of the first embodiment. [Figure 15] Figure 15 is a diagram illustrating an example of processing related to weighting, synthesis, and brightness correction functions, relating to a second application example of the first embodiment. [Figure 16] Figure 16 relates to a third application example of the first embodiment and shows an example in which a fourth weight is assigned to negative high-frequency data according to the hierarchy. [Figure 17] Figure 17 shows an example of the configuration of a medical image processing device according to the second embodiment. [Modes for carrying out the invention]

[0008] The ultrasound diagnostic apparatus, medical image processing apparatus, and medical image processing program according to this embodiment will be described below with reference to the drawings. To make the explanation more concrete, the first embodiment will be described using the ultrasound diagnostic apparatus as an example. In the following embodiments, parts with the same reference numerals perform similar operations, and redundant explanations will be omitted as appropriate.

[0009] (First Embodiment) Figure 1 shows an example of the configuration of an ultrasound diagnostic device 1 according to this embodiment. As shown in Figure 1, the ultrasound diagnostic device 1 includes an ultrasound probe 11, an input interface 13, a display 15, and a device body 19.

[0010] The ultrasonic probe 11 includes a plurality of piezoelectric transducers, a matching layer provided on the ultrasonic radiation surface side of the piezoelectric transducers, and a backing material provided on the back side of the piezoelectric transducers to prevent the propagation of ultrasonic waves backward from the piezoelectric transducers. Each of the plurality of piezoelectric transducers generates ultrasonic waves in response to a drive signal supplied from a transmitting / receiving circuit 23, which will be described later. The ultrasonic probe 11 is a one-dimensional array probe that is detachably connected to the main body of the device 7. The plurality of piezoelectric transducers generate ultrasonic waves based on a drive signal supplied from an ultrasonic transmitting circuit 71 in the main body of the device 7. The ultrasonic probe 11 may also be equipped with a button that is pressed during operations such as freeze operation.

[0011] When ultrasound is transmitted from the ultrasound probe 11 to the subject P, the transmitted ultrasound is reflected one after another by discontinuities in acoustic impedance within the subject P's internal tissues. The reflected ultrasound is received as reflected wave signals (hereinafter referred to as echo signals) by multiple piezoelectric transducers on the ultrasound probe 11. The amplitude of the received echo signals depends on the difference in acoustic impedance at the discontinuities where the ultrasound is reflected. Note that when the transmitted ultrasound pulse is reflected by a moving surface such as blood flow or the heart wall, the echo signal undergoes a frequency shift due to the Doppler effect, depending on the velocity component of the moving object relative to the ultrasound transmission direction. The ultrasound probe 11 receives the echo signals from the subject P and converts them into electrical signals.

[0012] The input interface 13 captures various instructions, commands, information, selections, and settings from the operator into the ultrasonic diagnostic apparatus 1. The input interface 13 is realized by a trackball, a switch button, a mouse, a keyboard, a touch pad for performing an input operation by touching an operation surface, a touch panel display in which a display screen and the touch pad are integrated, and the like. The input interface 13 converts the input operation received from the operator into an electrical signal. Note that in this specification, the input interface 13 is not limited to those provided with physical operation components such as a mouse and a keyboard. For example, an electrical signal processing circuit that receives an electrical signal corresponding to an input operation from an external input device provided separately from the ultrasonic diagnostic apparatus 1 and outputs the received electrical signal to the apparatus main body 19 is also included in the example of the input interface 13. The input interface 13 corresponds to an input unit.

[0013] The display 15 can use, as appropriate, for example, a liquid crystal display (LCD), a cathode ray tube (CRT) display, an organic electro luminescence display (OELD), a plasma display, or any other display. Note that the display 15 may be incorporated in the apparatus main body 7. Also, the display 15 may be a desktop type, or may be configured by a tablet terminal or the like capable of wireless communication with the apparatus main body 7. The display 15 corresponds to a display unit.

[0014] The display 15 displays various images generated by an image generation circuit 29 and the like, which will be described later. The display 15 has a display circuit for realizing the display of various images. Also, the display 15 displays a graphical user interface (GUI) for the operator to input various setting requests. Note that a plurality of displays may be connected to the apparatus main body 19 of the ultrasonic diagnostic apparatus 1.

[0015] The main unit 19 of the device includes a transmitting / receiving circuit (transmitting / receiving unit) 23, a B-mode data generation circuit (B-mode data generation unit) 25, a Doppler data generation circuit (Doppler data generation unit) 27, an image generation circuit (image generation unit) 29, a communication interface 31, a memory (storage unit) 33, a control circuit (control unit) 35, and a processing circuit (processing unit) 37.

[0016] The transmitting / receiving circuit 23 includes a pulse generator, a transmit delay circuit, and a pulser circuit, and supplies a drive signal to each of the multiple piezoelectric transducers in the ultrasonic probe 11. The pulse generator repeatedly generates rate pulses to form the transmitted ultrasonic wave at a predetermined rate frequency fr (Hz) (period: 1 / fr seconds). The transmit delay circuit provides each rate pulse with the necessary delay time to focus the transmitted ultrasonic wave into a beam and determine the transmit directivity. The pulser circuit applies a voltage pulse as a drive signal to each piezoelectric transducer of the ultrasonic probe 11 at a timing based on the rate pulse. As a result, the ultrasonic beam is transmitted to the subject P.

[0017] The transmitting / receiving circuit 23 further includes a preamplifier, an analog-to-digital (A / D) converter, a receive delay circuit, and an adder. The transmitting / receiving circuit 23 generates a received signal based on the received echo signals generated by each piezoelectric vibrator. The preamplifier amplifies the echo signals from the subject P acquired via the ultrasonic probe 11 for each channel. The A / D converter converts the amplified received echo signals into digital signals. The receive delay circuit gives the received echo signals converted into digital signals a delay time necessary to determine the receiving directivity. The adder adds the multiple echo signals to which the delay time has been given. Through this addition, the transmitting / receiving circuit 23 generates a received signal that emphasizes the reflected component from the direction corresponding to the receiving directivity. Based on this transmission directivity and reception directivity... The overall directivity of the ultrasonic transmission and reception is determined. This overall directivity determines the ultrasonic beam (the so-called "ultrasonic scanning line").

[0018] The B-mode data generation circuit 25 includes an envelope detector and a logarithmic converter, and generates B-mode data based on the received signal. The envelope detector performs envelope detection on the received signal. The logarithmic converter performs a logarithmic transformation on the envelope-detected signal, relatively amplifying weak signals in the envelope-detected signal. Based on the signal amplified by the logarithmic converter, the B-mode data generation circuit 25 generates signal values ​​for each depth in each scan line (referred to as B-mode data).

[0019] The Doppler data generation circuit 27 includes a mixer, a low-pass filter (LPF), etc., and generates Doppler data based on the received signal. The mixer multiplies the received signal by a reference signal having the frequency f0 of the transmitted ultrasound, generating a signal with a component of the Doppler shift frequency fd and a signal with a frequency component of (2f0+fd). The LPF removes the high-frequency component (2f0+fd) from the signal output from the mixer. As a result, the Doppler data generation circuit 27 generates Doppler data from the received signal that has a component of the Doppler shift frequency fd. The Doppler data generation circuit 27 may also generate dynamic information such as blood flow velocity, dispersion, and power (for example, data related to color Doppler images, data related to power Doppler images) as Doppler data by performing Doppler processing including frequency analysis.

[0020] The image generation circuit 29 includes a digital scan converter (DSC), image memory, etc., which are not shown in the diagram. The DSC converts the scan line signal sequence of the ultrasound scan, consisting of B-mode data and Doppler data, into a scan line signal sequence in video format (scan conversion). The image generation circuit 29 synthesizes various parameter character information, scales, etc., onto the scan-converted B-mode data and Doppler data to generate ultrasound image data. The ultrasound image data is data for display. An ultrasound image is an example of a medical image. Also, ultrasound image data is an example of medical data. On the other hand, B-mode data, volume data, and Doppler data are also called raw data. Hereinafter, ultrasound images and raw data will be collectively referred to as ultrasound data. For example, ultrasound data consists of at least one of the following: data related to the B-mode image, raw data related to the B-mode image, and data related to the power Doppler image. The image memory stores multiple ultrasound images (hereinafter referred to as ultrasound motion images) corresponding to a series of frames immediately before the input of the freeze operation. Multiple ultrasound images stored in image memory are used for cine display.

[0021] The communication interface 31 connects to external devices such as medical image storage devices via a network. The communication interface 31 transfers various data output from the image generation circuit 29, processing circuit 37, etc., to the external devices.

[0022] Memory 33 is composed of HDD (hard disk drive), SSD (solid state drive), magnetic disk (floppy disk, hard disk, etc.), optical disk (CD-ROM, DVD, etc.), semiconductor memory, etc. Memory 33 stores programs related to ultrasonic transmission and reception, programs corresponding to various processes executed by the control circuit 35 and processing circuit 37, etc. Memory 33 stores raw data, ultrasonic image data, various data generated and processed by the processing circuit 37, etc.

[0023] The control circuit 35 includes hardware resources such as a processor and memory. The control circuit 35 functions as the central hub of the ultrasound diagnostic device 1. Specifically, the control circuit 35 reads the control program stored in the memory 33, loads it into the memory, and controls various units of the ultrasound diagnostic device 1 according to the loaded control program.

[0024] The processing circuit 37 includes, for example, a processor and memory as hardware resources. Specifically, the processing circuit 37 reads a program stored in memory 33, expands it in memory, and implements various functions according to the expanded program. The processing circuit 37 has a multi-resolution decomposition function 371, an extraction function 373, a multi-resolution restoration function 375, a weighting function 377, a synthesis function 379, and a brightness correction function 381. The processing circuit 37 that implements the multi-resolution decomposition function 371, the extraction function 373, the multi-resolution restoration function 375, the weighting function 377, the synthesis function 379, and the brightness correction function 381 corresponds to the multi-resolution decomposition unit, the extraction unit, the multi-resolution restoration unit, the weighting unit, the synthesis unit, and the brightness correction unit, respectively.

[0025] For example, when the B-mode data generation circuit 25 acquires raw data (B-mode data) of a B-mode image related to B-mode, the processing circuit 37 performs contrast improvement processing using a multi-resolution decomposition function 371, an extraction function 373, a multi-resolution restoration function 375, a weighting function 377, a synthesis function 379, and a brightness correction function 381. B-mode data is the B-mode image before the B-mode image as a display image is generated, i.e., before scan conversion. The contrast improvement processing is a process that, for example, performs multi-resolution analysis on ultrasound data to acquire structural information of living organisms in the ultrasound data, treats the region excluding the living structure spatially as a virtual image region, and suppresses the signal intensity in the said virtual image region. To make the explanation more specific below, the contrast improvement processing will be explained assuming that B-mode data, i.e., raw data, is used as the ultrasound data. Note that the contrast improvement processing may also use various ultrasound images after scan conversion as the ultrasound data.

[0026] The processing circuit 37 generates multiple low-frequency data and multiple high-frequency data corresponding to multiple stepped resolutions from the ultrasonic data using the multi-resolution decomposition function 371. The multiple stepped resolutions correspond to multiple layers. The multi-resolution decomposition performed by the multi-resolution decomposition function 371 will be described below with reference to Figures 2 and 4.

[0027] Figure 2 shows an example of multi-resolution decomposition for generating low-frequency data, specifically the Gaussian Pyramid process. The original image OI shown in Figure 2 corresponds to the raw data of the B-mode image. For example, if the number of vertical pixels and horizontal pixels in the original image are greater than a predetermined number of pixels, the multi-resolution decomposition function 371 performs a resize process RS on the original image OI to generate a resized image RI. The number of vertical pixels × horizontal pixels in the resized image RI is, for example, 512 × 256. Note that if the number of vertical pixels and horizontal pixels in the original image are equal to a predetermined number of pixels, the resize process RS is unnecessary.

[0028] The multiple hierarchical PRs in Figure 2 represent six hierarchical levels with image sizes smaller than the resized image RI, i.e., six hierarchical levels with lower resolution than the original image OI but with different resolutions. The number of hierarchical levels is predetermined. However, the number of hierarchical levels may also be set by the user via the input interface 13. Alternatively, the number of hierarchical levels may be input by the user as appropriate through adjustment, modification, etc., via the input interface 13.

[0029] The multi-resolution decomposition function 371 performs a first Gaussian reduction GSD1 on the resized image RI to generate a first reduced data RD1 with an image size of 256 × 128, where the vertical and horizontal pixel counts are halved. Here, Gaussian reduction refers to image reduction processing using a Gaussian filter. The size of the first reduced data RD1 is 1 / 4 of the resized image RI and belongs to the first layer, which is a low-resolution reduction of the resized image RI.

[0030] The multi-resolution decomposition function 371 performs a second Gaussian reduction GSD2 on the first reduction data RD1, generating a second reduction data RD2 with an image size of 128×64, where the vertical and horizontal pixel counts are halved. The size of the second reduction data RD2 is 1 / 4 of the first reduction data RD1. The second reduction data RD2 belongs to a second layer, which is a low-resolution reduction of the first reduction data RD1.

[0031] The multi-resolution decomposition function 371 performs a third Gaussian reduction GSD3 on the second reduction data RD2, generating a third reduction data RD3 with an image size of 64×32, where the vertical and horizontal pixel counts are halved. The size of the third reduction data RD3 is 1 / 4 of the second reduction data RD2. The third reduction data RD3 belongs to a third layer, which is a low-resolution reduction of the second reduction data RD2.

[0032] The multi-resolution decomposition function 371 performs a fourth Gaussian reduction (GSD4) on the third reduction data RD3, generating a fourth reduction data RD4 with an image size of 32×16, where the vertical and horizontal pixel counts are halved. The size of the fourth reduction data RD4 is 1 / 4 of the third reduction data RD3. The fourth reduction data RD4 belongs to the fourth layer, which is a low-resolution reduction of the third reduction data RD3.

[0033] The multi-resolution decomposition function 371 performs a fifth Gaussian reduction (GSD5) on the fourth reduction data RD4, generating a fifth reduction data RD5 with a size of 16x8, where the vertical and horizontal pixel counts are halved. The size of the fifth reduction data RD5 is 1 / 4 of the fourth reduction data RD4. The fifth reduction data RD5 belongs to the fifth layer, which is a low-resolution reduction of the fourth reduction data RD4.

[0034] The multi-resolution decomposition function 371 performs a sixth Gaussian reduction (GSD6) on the fifth reduction data RD5, generating a sixth reduction data RD6 with an 8x4 image size where the vertical and horizontal pixel counts are halved. The size of the sixth reduction data RD6 is 1 / 4 of the fifth reduction data RD5. The sixth reduction data RD6 belongs to the sixth layer, which is a low-resolution reduction of the fifth reduction data RD5.

[0035] Figure 3 shows an example of the original image OI, the first reduced data RD1, the second reduced data RD2, the third reduced data RD3, the fourth reduced data RD4, the fifth reduced data RD5, and the sixth reduced data RD6. As shown in Figure 3, the reduced data becomes lower resolution depending on the layer.

[0036] Figure 4 illustrates a procedure using a Laplacian pyramid, which is an example of a multi-resolution decomposition that generates high-frequency data. Figure 4 shows an example of the process performed by the multi-resolution decomposition function 371. The process using the Laplacian pyramid in Figure 4 is an example of obtaining high-frequency data for each layer in multi-resolution decomposition, and is not limited to the above process. For example, high-frequency data obtained when multi-resolution decomposition is performed using other methods may be used.

[0037] In Figure 4, focusing on the sixth and fifth layers as two adjacent layers, the reduced data belonging to the higher layers in two adjacent layers of the multiple stepwise layer PR (hereinafter referred to as high-layer data) corresponds to the fifth reduced data RD5, and the reduced data belonging to the lower layers in two adjacent layers of the multiple stepwise layer PR (hereinafter referred to as low-layer data) corresponds to the sixth reduced data RD6. At this time, the multi-resolution decomposition function 371 performs a sixth Gaussian expansion GSE6 on the sixth reduced data RD6, which corresponds to the inverse processing of the sixth Gaussian reduction GSD6, and expands the sixth reduced data RD6 to the image size (16×8) corresponding to the fifth layer. Here, Gaussian expansion means image expansion processing using a Gaussian filter. The data obtained by expanding the sixth reduced data RD6 corresponds to the expanded data. The multi-resolution decomposition function 371 generates the fifth high-frequency data DD5 with an image size of 16×8 by subtracting (SU5) the expanded sixth reduced data (expanded data) from the fifth reduced data RD5.

[0038] In Figure 4, focusing on the fifth and fourth layers as two adjacent layers, the high-layer data corresponds to the fourth-layer reduction data RD4, and the low-layer data corresponds to the fifth-layer reduction data RD5. At this time, the multi-resolution decomposition function 371 performs a fifth-layer Gaussian expansion GSE5 on the fifth-layer reduction data RD5, which corresponds to the inverse processing of the fifth-layer Gaussian expansion GSD5, and expands the fifth-layer reduction data RD5 to the image size (32×16) corresponding to the fourth layer. The expanded data of the fifth-layer reduction data RD5 corresponds to the expanded data. The multi-resolution decomposition function 371 generates the fourth-layer high-frequency data DD4 with an image size of 32×16 by subtracting the expanded fifth-layer reduction data (expanded data) from the fourth-layer reduction data RD4 (SU4).

[0039] In Figure 4, focusing on the fourth and third layers as two adjacent layers, the high-layer data corresponds to the third-layer reduction data RD3, and the low-layer data corresponds to the fourth-layer reduction data RD4. At this time, the multi-resolution decomposition function 371 performs a fourth Gaussian expansion GSE4 on the fourth-layer reduction data RD4, which corresponds to the inverse processing of the fourth Gaussian expansion GSD4, and expands the fourth-layer reduction data RD4 to the image size (64×32) corresponding to the third layer. The expanded data of the fourth-layer reduction data RD4 corresponds to the expanded data. The multi-resolution decomposition function 371 generates the third high-frequency data DD3 with an image size of 64×32 by subtracting the expanded fourth-layer reduction data (expanded data) from the third-layer reduction data RD3 (SU3).

[0040] The multi-resolution decomposition shown in Figures 2 and 4 is an example, and the processing by the multi-resolution decomposition function 371 is not limited to the above processing. For example, multi-resolution decomposition may be performed based on other methods or pixel counts. The multi-resolution decomposition function 371 stores the generated multiple low-frequency data and multiple high-frequency data in the memory 33.

[0041] The processing circuit 37, using the extraction function 373, extracts negative high-frequency data, also known as negative edges, from the high-frequency data over a predetermined resolution, in other words, from the lowest resolution layer to a predetermined layer. Negative high-frequency data corresponds to data with negative luminance values ​​(negative values) in the high-frequency data. For example, the extraction function 373 extracts negative high-frequency data from the high-frequency data by replacing positive luminance values ​​in the high-frequency data with 0.

[0042] Specifically, as shown in Figure 4, the extraction function 373 extracts negative high-frequency data as structural information from high-frequency data across the fifth to the third layer. The high-frequency data corresponds to data that indicates the structure in the ultrasonic data. For this reason, the extraction function 373 may also be called a structural information extraction function. In this case, the processing circuit 37 that realizes the structural information extraction function corresponds to the structural information extraction unit. The negative high-frequency data corresponds to structural data that shows relatively low brightness values ​​within the structure. Furthermore, the negative high-frequency data at lower layers corresponds to data that shows relatively low brightness regions in the ultrasonic data due to its low resolution.

[0043] Figure 5 shows an example of extracting the fifth negative high-frequency data ND5 in the sixth and fifth layers based on the fifth reduced data RD5 and the expanded sixth reduced data ERD6. Figure 6 shows an example of extracting the fifth negative high-frequency data ND5 from the fifth high-frequency data DD5 calculated from the fifth reduced data RD5 and the expanded sixth reduced data ERD6. As shown in Figures 4 to 6, the extraction function 373 extracts the fifth negative high-frequency data ND5 from the fifth high-frequency data DD5. Similarly, the extraction function 373 extracts the fourth negative high-frequency data ND4 from the fourth high-frequency data DD4 and the third negative high-frequency data ND3 from the third high-frequency data DD3.

[0044] The processing circuit 37, using the multi-resolution restoration function 375, adjusts the resolution over a predetermined range and adds multiple negative high-frequency data to generate the first added data. That is, the multi-resolution restoration function 375 adjusts the resolution over a predetermined range and adds multiple negative high-frequency data to generate the first added data. Next, the multi-resolution restoration function 375 restores the resolution of the first added data to the same resolution as the resolution of the ultrasonic data before reduction. The processing by the multi-resolution restoration function 375 will be explained below with reference to Figures 7 and 8.

[0045] Figure 7 shows an example of processing by the multi-resolution restoration function 375. Figure 8 shows an example of an image related to the processing shown in Figure 7. In Figure 7, a predetermined layer corresponds to the third layer. As shown in Figure 7, the multi-resolution restoration function 375 adds the fifth negative high-frequency data ND5, which has been amplified by the fifth Gaussian augmentation GSE5, and the fourth negative high-frequency data ND4. Next, the multi-resolution restoration function 375 augments the added negative high-frequency data by the fourth Gaussian augmentation GSE4 and adds the augmented negative high-frequency data to the third negative high-frequency data ND3. As a result, as shown in Figure 8, the multi-resolution restoration function 375 generates the first summation data AD1 based on the third negative high-frequency data ND3, the fourth negative high-frequency data ND4, and the fifth negative high-frequency data ND5.

[0046] As shown in Figures 7 and 8, the multi-resolution restoration function 375 restores the resolution of the first summation data AD1 to the same resolution as the original ultrasound data (original image OI) before reduction using multiple Gaussian expansion AGSEs. The original ultrasound data (original image OI) before reduction corresponds to the size (512 × 256) of the resized image RI in Figure 2. The multiple Gaussian expansion AGSEs corresponding to restoration by Gaussian pyramids correspond to a Gaussian expansion that combines the third Gaussian expansion corresponding to the inverse processing of the third Gaussian reduction GSD3, the second Gaussian expansion corresponding to the inverse processing of the second Gaussian reduction GSD2, and the first Gaussian expansion corresponding to the inverse processing of the first Gaussian reduction GSD1.

[0047] Specifically, the multi-resolution reconstruction function 375 generates the negative reconstruction data NRD shown in Figure 8 by sequentially performing the third Gaussian expansion, the second Gaussian expansion, and the first Gaussian expansion on the first summation data AD1. The reconstruction data NRD is high-resolution data that reflects the information of the negative high-frequency data ND5, ND4, and ND3 extracted by the extraction function 373. As mentioned above, ND5, ND4, and ND3 are low-level negative high-frequency data that retain information indicating relatively low-luminance regions in the ultrasound data. In other words, the reconstruction data NRD is high-resolution data that indicates relatively low-luminance regions in the ultrasound data.

[0048] The processing circuit 37, using the weighting function 377, assigns a first weight to the reconstructed first summation data (negative reconstructed data NRD) according to the contrast of biological structures in the original image OI. The first weight is set, for example, as a positive value. The first weight can be input, set, and adjusted as appropriate by user instructions via the input interface 13. The first weight is set to a large value, for example, when highlighting intracardiac regions where low brightness is expected in the composite image.

[0049] The processing circuit 37 generates a composite image based on the first additive data (negative restored data NRD) restored by the synthesis function 379 and the ultrasound data. Specifically, the synthesis function 379 synthesizes the restored first additive data, to which a first weight has been assigned, and the ultrasound data to generate a composite image. As described above, the restored first additive data (negative restored data NRD) is data that indicates relatively low-luminance regions in the ultrasound data. By assigning a first weight and then synthesizing it with the ultrasound data, it becomes possible to further suppress the low-luminance regions in the ultrasound data, such as regions within the cardiac chambers where low luminance is expected, to low luminance in the composite image, thereby enhancing the contrast between low-luminance and high-luminance regions in the ultrasound data.

[0050] The processing circuit 37 corrects the brightness of the composite image using the brightness correction function 381. Specifically, the brightness correction function 381 corrects the brightness of the composite image based on a first average brightness value related to the ultrasound data (original image OI) and a second average brightness value in the composite image. More specifically, the brightness correction function 381 corrects the brightness value of the composite image so that the second average brightness value approaches the first average brightness value.

[0051] The processes related to the weighting function 377, the synthesis function 379, and the brightness correction function 381 will be explained below with reference to Figure 9. Figure 9 is a diagram showing an example of the processes related to the weighting function 377, the synthesis function 379, and the brightness correction function 381. As shown in Figure 9, the weighting function 377 assigns a first weight W1 to the negative reconstructed data NRD. The synthesis function 379 performs a resize process RS on the negative reconstructed data to which the first weight W1 has been assigned. As a result of this resize process, the image size of the negative reconstructed data to which the first weight W1 has been assigned becomes the same as the image size of the original image OI. The synthesis function 379 synthesizes the resized negative reconstructed data and the original image OI to generate a composite image CI. The brightness correction function 381 performs brightness correction on the composite image CI. The composite image CI to which brightness correction has been performed is displayed on the display 15.

[0052] In the description of the control circuit 35 and the processing circuit 37, the term "processor" refers to circuits such as a CPU (Central Processing Unit), a GPU (graphics processing unit), an Application Specific Integrated Circuit (ASIC), or a programmable logic device (e.g., a Simple Programmable Logic Device (SPLD), a Complex Programmable Logic Device (CPLD), and a Field Programmable Gate Array (FPGA)).

[0053] The processor implements various functions by reading and executing programs stored in memory 33. Alternatively, instead of storing programs in memory 33, the processor may be configured to directly incorporate programs into the processor's circuitry in the control circuit 35 or processing circuit 37. In this case, the processor implements various functions by reading and executing the programs incorporated into the circuitry. The functions executed by the processing circuit 37 may also be implemented in the control circuit 35. Furthermore, the functions executed by the control circuit 35 may also be implemented in the processing circuit 37.

[0054] The configuration of this embodiment has been described above. The procedure for contrast improvement processing will now be explained using Figure 10. Figure 10 is a flowchart showing an example of the procedure for contrast improvement processing. To make the explanation more concrete, the ultrasound data input to the contrast improvement processing will be assumed to be a B-mode image.

[0055] (Contrast enhancement processing) (Step S101) By transmitting and receiving ultrasound waves to and from the subject P, B-mode data is generated as ultrasound data. Raw data such as B-mode data is used as the original image OI, as shown in Figure 2.

[0056] (Step S102) The multi-resolution decomposition function 371 performs multi-resolution analysis on the original image OI. This allows the multi-resolution decomposition function 371 to generate multiple low-frequency data and multiple high-frequency data corresponding to multiple stepped resolutions (multiple layers) from the ultrasound data.

[0057] (Step S103) The extraction function 373 extracts negative high-frequency data from each of multiple high-frequency data up to a predetermined resolution.

[0058] (Step S104) The multi-resolution restoration function 375 adjusts the resolution over a predetermined range and adds multiple negative high-frequency data to generate the first summation data AD1. The multi-resolution restoration function 375 restores the resolution of the first summation data AD1 to the same resolution as the resolution of the ultrasound data (original image OI) before reduction. As a result, the multi-resolution restoration function 375 generates the negative restoration data NRD.

[0059] (Step S105) The weighting function 377 assigns a first weight W1 to the negative reconstructed data NRD according to the contrast of the biological structure in the original image OI. The first weight W1 can be changed as appropriate based on user instructions for the composite image CI displayed on the display 15.

[0060] (Step S106) The synthesis function 379 synthesizes negative reconstructed data, to which a first weight W1 is assigned, with ultrasound data to generate a composite image CI. This reduces artifacts in non-structural parts such as cardiac chambers in the composite image CI.

[0061] (Step S107) The brightness correction function 381 corrects the brightness of the composite image CI. The image generation circuit 29 performs scan conversion on the composite image CI whose brightness has been corrected. The display 15 displays the scan-converted composite image CI. Note that if a B-mode image is used as the source image OI, scan conversion in this step is unnecessary.

[0062] According to the ultrasound diagnostic apparatus 1 of the first embodiment described above, multiple low-frequency data and multiple high-frequency data corresponding to multiple step-level resolutions are generated from ultrasound data, negative high-frequency data is extracted from the multiple high-frequency data over a predetermined resolution, the resolution of the first summation data obtained by adding the negative high-frequency data over a predetermined resolution is restored to the same resolution as the ultrasound data before reduction, a composite image CI is generated based on the restored first summation data and ultrasound data, and the composite image CI is displayed.

[0063] In other words, this ultrasound diagnostic device 1 selectively extracts negative high-frequency data that constitutes negative edges from the low-resolution data obtained by multi-resolution decomposition, and performs multi-resolution reconstruction, weighting, and synthesis into ultrasound data. This allows for selective brightness correction of relatively low-luminance regions, such as cardiac chambers, among the general structures of living organisms in ultrasound images. Figure 11 shows an example of a comparison with and without the use of contrast enhancement processing in this ultrasound diagnostic device 1. In Figure 11, the dotted line frame represents the cardiac chamber region, and the solid line frame represents the myocardial region (living structure) that is the target of observation. Low luminance is expected in the cardiac chamber region. As shown in the NCP of Figure 11, when contrast enhancement processing is not performed, artifacts appear in the cardiac chamber region. On the other hand, as shown in the ECP of Figure 11, when contrast enhancement processing is performed, the cardiac chamber region becomes low-luminance and artifacts are suppressed. As shown in Figure 11, this ultrasound diagnostic device 1 can suppress the signal intensity of virtual images in spatially non-biological regions (e.g., cardiac chambers), widening the signal intensity difference between biological information and virtual images in ultrasound data, and improving the visibility of biological information in ultrasound images.

[0064] Figure 12 shows an example of the effect of contrast enhancement processing. In the B-mode raw data RD shown in Figure 12, the cardiac septal region SR is shown with a dotted line frame, the cardiac wall region WR is shown with a long dashed line frame, and the cardiac chamber region CR is shown with a solid line frame. In the raw data RD, the histogram IH of luminance values ​​before input to contrast enhancement processing shows the histogram SRIH of luminance values ​​in the septal region SR, the histogram WRIH of luminance values ​​in the cardiac wall region WR, and the histogram CRIH of luminance values ​​in the cardiac chamber region CR. In the raw data RD, the histogram OH of luminance values ​​after output from contrast enhancement processing shows the histogram SROH of luminance values ​​in the septal region SR, the histogram WROH of luminance values ​​in the cardiac wall region WR, and the histogram CROH of luminance values ​​in the cardiac chamber region CR. Histograms SRIH, SROH, WRIH, and WROH represent biological information. Furthermore, the histograms CRIH and CROH show artifacts in the cardiac chamber region.

[0065] As shown in the histograms IH and OH in Figure 12, the difference DOH between the peak frequency of luminance values ​​in histogram WROH and the peak frequency of luminance values ​​in histogram SROH is greater than the difference DIH between the peak frequency of luminance values ​​in histogram WRIH and the peak frequency of luminance values ​​in histogram SRIH. In other words, the peak frequency of luminance values ​​in histogram SROH is lower than the peak frequency of luminance values ​​in histogram SRIH. As shown in the histograms IH and OH in Figure 12, the contrast enhancement process reduces the luminance of artifacts in the cardiac chamber region while maintaining the luminance of biological structures. That is, this reduction in peak values ​​SA improves the ratio of artifacts (histogram CROH) to biological information (histograms SROH and WRIH), as shown in Figure 12.

[0066] Based on these findings, this ultrasound diagnostic device 1 can improve the visibility of the septal region SR and cardiac wall region WR relative to the cardiac chamber region CR. In other words, with this ultrasound diagnostic device 1, contrast improvement processing is performed using the spatial information of the ultrasound data, making it possible to improve the visibility of biological information in ultrasound data where it is difficult to separate biological information from virtual images using only signal intensity information.

[0067] Furthermore, this ultrasound diagnostic device 1 can perform contrast enhancement processing on ultrasound data acquired in real time, so that contrast enhancement processing is performed according to the characteristics of the ultrasound data. For this reason, with this ultrasound diagnostic device 1, there is no need to set the display settings for the ultrasound image each time ultrasound data is acquired, and it is possible to display ultrasound images with improved visibility of biological information in the ultrasound image, thereby improving diagnostic throughput, operability, and diagnostic efficiency.

[0068] Furthermore, according to this ultrasound diagnostic device 1, a first weight W1 is assigned to the restored first summation data NRD according to the contrast of the structure, and the first summation data to which the first weight W1 has been assigned is combined with the ultrasound data to generate a composite image CI. At this time, the first weight W1 can be adjusted as appropriate by user instruction via the input interface 13. As a result, according to this ultrasound diagnostic device 1, the degree of artifact reduction by contrast improvement processing can be appropriately set according to the user's wishes or default settings, and the composite image CI can be displayed on the display 15.

[0069] Furthermore, this ultrasound diagnostic device 1 corrects the brightness of the composite image CI based on a first average brightness value related to the ultrasound data and a second average brightness value in the composite image CI, and displays the composite image CI with corrected brightness. As a result, this ultrasound diagnostic device 1 can reduce the darkening of the composite image CI due to artifact reduction, and can display ultrasound images with improved visibility of biological information in ultrasound images.

[0070] (First application example) The first application example involves assigning a weight (hereinafter referred to as the second weight) to negative high-frequency data according to multiple layers. For example, if a predetermined resolution is the maximum resolution, the extraction function 373 generates multiple negative high-frequency data corresponding to multiple resolutions based on high-frequency data of all stepwise resolutions related to the multi-resolution analysis. The multi-resolution restoration function 375 adds the negative high-frequency data to which the second weight has been assigned, matching the resolutions over the predetermined resolution, to generate the first summed data. The technical concept regarding the generation of multiple high-frequency data and multiple negative high-frequency data is the same as in the first embodiment, so a detailed explanation is omitted. To make the explanation more specific below, the predetermined resolution is assumed to be the maximum resolution corresponding to the 0th layer, and the layer corresponding to the lowest resolution is assumed to be the 5th layer.

[0071] Figure 13 shows an example in which a second weight is assigned to negative high-frequency data according to the resolution. As shown in Figure 13, multiple negative high-frequency data (ND0 to ND5) are extracted across the 0th to 5th layers. The multiple second weights (0W2 to 5W2) in Figure 13 differ for each layer corresponding to the resolution.

[0072] The weighting function 377 assigns a second weight (0W2~5W2) to the negative high-frequency data (ND0~ND5) according to the hierarchy to which the negative high-frequency data belongs. The second weight (0W2~5W2) corresponding to the hierarchy, that is, multiple second weights (0W2~5W2) corresponding to multiple hierarchy levels, are, for example, positive values ​​greater than or equal to 0, and are stored in memory 33 in advance. The multiple second weights (0W2~5W2) can be set, changed, and adjusted as appropriate by user instructions via the input interface 13.

[0073] The multi-resolution reconstruction function 375 generates the first summation data AD1 by sequentially adding together multiple negative high-frequency data, each assigned a second weight (0W2 to 5W2), from the lowest resolution up to a predetermined resolution, while adjusting the resolution. Specifically, the multi-resolution reconstruction function 375 expands the fifth negative high-frequency data ND5, which is assigned a second weight 5W2 in the fifth layer, using the fifth Gaussian expansion GSE5. The multi-resolution reconstruction function 375 then adds the negative high-frequency data expanded by the fifth Gaussian expansion GSE5 and the fourth negative high-frequency data ND4, which is assigned a second weight 4W2 in the fourth layer, in the fourth layer.

[0074] The multi-resolution restoration function 375 expands the added negative high-frequency data in the fourth layer using the fourth Gaussian expansion GSE4, and adds the expanded negative high-frequency data and the third negative high-frequency data ND3, to which the second weight 3W2 in the third layer has been assigned, in the third layer. The multi-resolution restoration function 375 expands the added negative high-frequency data in the third layer using the third Gaussian expansion GSE3, and adds the expanded negative high-frequency data and the second negative high-frequency data ND2, to which the second weight 2W2 in the second layer has been assigned, in the second layer. The multi-resolution restoration function 375 expands the added negative high-frequency data in the second layer using the second Gaussian expansion GSE2, and adds the expanded negative high-frequency data and the first negative high-frequency data ND1, to which the second weight 1W2 in the first layer has been assigned, in the first layer. The multi-resolution restoration function 375 expands the negative high-frequency data after addition in the first layer by the first Gaussian expansion GSE1, and adds the expanded negative high-frequency data and the 0th negative high-frequency data ND0, to which the second weight 0W2 in the 0th layer is assigned, in the 0th layer.

[0075] Based on these, the multi-resolution reconstruction function 375 generates the first summation data AD1 based on multiple negative high-frequency data (ND0~ND5) and multiple second weights (0W2~5W2), as shown in Figure 13. As shown in Figure 12, the first summation data AD1 in this application corresponds to the negative reconstruction data NRD shown in Figure 6.

[0076] For example, if the second weight 0W2 in the 0th layer, the second weight 1W2 in the 1st layer, and the second weight 2W2 in the 2nd layer are all 0, and the second weight 3W2 in the 3rd layer, the second weight 4W2 in the 4th layer, and the second weight 5W2 in the 5th layer are all 1, then this application example is the same as the first embodiment. In other words, this application example makes it possible to appropriately adjust the negative high-frequency data contributing to the first summation data AD1 by assigning a second weight to the negative high-frequency data according to the layer.

[0077] According to the ultrasound diagnostic apparatus 1 of the first application example of the first embodiment described above, a second weight W2 corresponding to the hierarchy to which the negative high-frequency data belongs is assigned to the negative high-frequency data, and the negative high-frequency data to which the second weight W2 is assigned is added together over a predetermined resolution to generate first added data AD1. At this time, the second weight W2 can be adjusted as appropriate by user instruction via the input interface 13. As a result, with this ultrasound diagnostic apparatus 1, the second weight W2 can be set according to multiple hierarchies, thereby expanding the range of contrast adjustment in the composite image CI. Therefore, with this ultrasound diagnostic apparatus 1, the degree of artifact reduction by contrast improvement processing can be set more appropriately according to the user's wishes or default settings, and the composite image CI can be displayed on the display 15. As a result, with this ultrasound diagnostic apparatus 1, the visibility of biological information can be further improved. Other effects are the same as in the first embodiment, so their explanation is omitted.

[0078] (Second application example) The second application involves extracting positive high-frequency data over a predetermined resolution and generating a composite image CI based on the extracted positive high-frequency data, negative high-frequency data, and ultrasound data. The fifth positive high-frequency data PD5 corresponds to, for example, the data shown in Figure 6. To make the explanation more specific, the predetermined hierarchy will be the third hierarchy, as in the first embodiment.

[0079] Figure 14 shows an example of the process by which positive reconstructed data is generated based on positive high-frequency data extracted by the extraction function 373. As shown in Figure 14, the extraction function 373 extracts positive high-frequency data, also called positive edges, from the high-frequency data, from the lowest resolution to a predetermined resolution. Positive high-frequency data corresponds to data that has positive luminance values ​​(positive values) in the high-frequency data, as shown in Figure 6. For example, the extraction function 373 extracts positive high-frequency data from the high-frequency data by replacing negative luminance values ​​in the high-frequency data with 0. As shown in Figure 14, the extraction function 373 extracts the fifth positive high-frequency data PD5 from the fifth high-frequency data DD5, the fourth positive high-frequency data PD4 from the fourth high-frequency data DD4, and the third positive high-frequency data PD3 from the third high-frequency data DD3.

[0080] The multi-resolution restoration function 375 adjusts the resolution over a predetermined resolution, adds multiple positive high-frequency data, and generates a second summation data AD2. Next, the multi-resolution restoration function 375 restores the resolution of the second summation data AD2 to the same resolution as the resolution of the ultrasonic data before reduction. As shown in Figure 14, the multi-resolution restoration function 375 adds the fifth positive high-frequency data PD5 and the fourth positive high-frequency data PD4, which have been amplified by the fifth Gaussian augmentation GSE5. Next, the multi-resolution restoration function 375 augments the added positive high-frequency data by the fourth Gaussian augmentation GSE4 and adds the augmented positive high-frequency data to the third positive high-frequency data PD3. As a result, as shown in Figure 14, the multi-resolution restoration function 375 generates a second summation data AD2 based on the third positive high-frequency data PD3, the fourth positive high-frequency data PD4, and the fifth positive high-frequency data PD5.

[0081] As shown in Figure 14, the multi-resolution restoration function 375 restores the resolution of the second summation data AD1 to the same resolution as the original ultrasound data (original image OI) before reduction using multiple Gaussian expansion AGSEs. The original ultrasound data (original image OI) before reduction corresponds to the resolution of the resized image RI in Figure 2 (512 × 256). The multiple Gaussian expansion AGSEs corresponding to the restoration using a Gaussian pyramid correspond to the combined Gaussian expansion of the third Gaussian expansion GSE3, the second Gaussian expansion GSE2, and the first Gaussian expansion GSE1. Specifically, the multi-resolution restoration function 375 generates the positive restoration data PRD shown in Figure 14 by sequentially performing the third Gaussian expansion, the second Gaussian expansion, and the first Gaussian expansion on the second summation data AD2.

[0082] The weighting function 377 assigns a third weight to the reconstructed second additive data (positive reconstructed data PRD) according to the contrast of biological structures in the original image OI. The third weight is set, for example, as a negative value. The third weight can be input, set, and adjusted as appropriate by user instructions via the input interface 13.

[0083] The synthesis function 379 generates a composite image CI based on negative reconstructed data NRD, positive reconstructed data PRD, and ultrasound data. Specifically, the synthesis function 379 synthesizes the first additive data NRD, which has been reconstructed with a first weight, the second additive data PRD, which has been reconstructed with a second weight, and ultrasound data to generate a composite image CI.

[0084] The brightness correction function 381 corrects the brightness value of the composite image CI so that the second average brightness value approaches the first average brightness value, based on the first average brightness value of the ultrasound data (original image OI) and the second average brightness value of the composite image CI. The image generation circuit 29 performs scan conversion on the composite image CI whose brightness has been corrected. The display 15 displays the scan-converted composite image CI.

[0085] The following describes the processing related to the weighting function 377, the synthesis function 379, and the brightness correction function 381 using Figure 15. Figure 15 is a diagram showing an example of the processing related to the weighting function 377, the synthesis function 379, and the brightness correction function 381. As shown in Figure 15, the weighting function 377 assigns a first weight W1 to the negative reconstructed data NRD and a third weight W3 to the positive reconstructed data PRD. The synthesis function 379 performs a resize process RS on the negative reconstructed data to which the first weight W1 has been assigned and on the positive reconstructed data to which the third weight W3 has been assigned. As a result of this resize process, the image size of the negative reconstructed data to which the first weight W1 has been assigned and the image size of the negative reconstructed data to which the third weight W3 has been assigned become the same as the image size of the original image OI. The synthesis function 379 synthesizes the resized negative reconstructed data, the resized positive reconstructed data, and the original image OI to generate a composite image CI. The brightness correction function 381 performs brightness correction on the composite image CI. The composite image CI with brightness correction applied is displayed on the display 15.

[0086] According to the ultrasound diagnostic apparatus 1 of the second application example of the first embodiment described above, positive high-frequency data is extracted from high-frequency data of multiple step-level resolutions over a predetermined resolution, the extracted positive high-frequency data is added together over the predetermined resolution to restore the resolution of the second added data AD2 to the same resolution as the resolution of the ultrasound data before reduction, a third weight W3 is assigned to the restored second added data PRD according to the contrast of the structure, and the first added data NRD to which the first weight W1 is assigned, the second added data RPD to which the third weight W3 is assigned, and the ultrasound data are combined to generate a composite image CI. At this time, the third weight W3 can be adjusted as appropriate by user instruction via the input interface 13.

[0087] As a result, this ultrasound diagnostic device 1 can appropriately set the brightness of biological information through contrast enhancement processing according to the user's wishes or default settings. Therefore, this ultrasound diagnostic device 1 can appropriately set the contrast between biological information and non-biological information such as cardiac chambers in the composite image CI, further improving the visibility of biological information in the composite image CI. Furthermore, by appropriately adjusting the third weight W3, biological information in the composite image CI can be emphasized.

[0088] Furthermore, this ultrasound diagnostic device 1 can reduce the brightness value in areas showing biological information in conjunction with the reduction in brightness value in areas showing non-biological information. This reduces the saturation (overexposure) of brightness values ​​in areas showing biological information due to brightness correction, i.e., it can suppress brightness saturation. As a result, this ultrasound diagnostic device 1 can further improve the visibility of biological information in the composite image CI. Other effects are the same as in the first embodiment, so their explanation will be omitted.

[0089] (Third application example) The third application involves assigning weights (hereinafter referred to as the fourth weight) to positive high-frequency data according to multiple step-by-step resolutions. For example, if the hierarchy corresponding to a predetermined resolution is the 0th hierarchy, the extraction function 373 generates multiple high-frequency data that are differences between multiple resolutions, excluding the lowest resolution among all step-by-step resolutions related to the multi-resolution analysis, and the resolution before the first Gaussian reduction GSD1 (resolution of the resized image RI). Based on the multiple high-frequency data, the extraction function 373 generates multiple positive high-frequency data corresponding to multiple resolutions. The technical concept regarding the generation of multiple high-frequency data and multiple positive high-frequency data is the same as in the first embodiment and the second application, so a detailed explanation is omitted. To make the explanation more specific, the predetermined resolution is assumed to be the 0th hierarchy corresponding to the maximum resolution, and the hierarchy corresponding to the lowest resolution is assumed to be the 5th hierarchy.

[0090] Figure 16 shows an example in which a fourth weight is assigned to negative high-frequency data depending on the resolution. As shown in Figure 16, multiple positive high-frequency data (PD0~PD5) are extracted from the 0th to the 5th layer. The multiple fourth weights (0W4~5W4) in Figure 16 differ for each layer indicating the image size.

[0091] The weighting function 377 assigns a fourth weight (0W4~5W4) to the positive high-frequency data (PD0~PD5) according to the hierarchy to which the positive high-frequency data belongs. The fourth weight corresponding to the hierarchy, i.e., the multiple fourth weights (0W4~5W4) corresponding to multiple hierarchy levels, are, for example, negative values ​​less than or equal to 0, and are stored in memory 33 in advance. The multiple fourth weights (0W4~5W4) can be set, changed, and adjusted as appropriate by user instructions via the input interface 13.

[0092] The multi-resolution restoration function 375 sequentially adds up multiple negative high-frequency data, each assigned a fourth weight (0W4 to 5W4), while adjusting the resolution over a predetermined resolution, to generate a second summation data AD2. Specifically, the multi-resolution restoration function 375 expands the fifth positive high-frequency data PD5, which is assigned a fourth weight 5W4 in the fifth layer, using the fifth Gaussian expansion GSE5. The multi-resolution restoration function 375 then adds the positive high-frequency data expanded by the fifth Gaussian expansion GSE5 and the fourth positive high-frequency data PD4, which is assigned a fourth weight 4W4 in the fourth layer, in the fourth layer.

[0093] The multi-resolution restoration function 375 expands the added positive high-frequency data in the fourth layer using the fourth Gaussian expansion GSE4, and adds the expanded positive high-frequency data and the third positive high-frequency data PD3, to which the fourth weight 3W4 in the third layer has been assigned, in the third layer. The multi-resolution restoration function 375 expands the added positive high-frequency data in the third layer using the third Gaussian expansion GSE3, and adds the expanded positive high-frequency data and the second positive high-frequency data PD2, to which the fourth weight 2W4 in the second layer has been assigned, in the second layer. The multi-resolution restoration function 375 expands the added positive high-frequency data in the second layer using the second Gaussian expansion GSE2, and adds the expanded positive high-frequency data and the first positive high-frequency data PD1, to which the fourth weight 1W4 in the first layer has been assigned, in the first layer. The multi-resolution restoration function 375 expands the added positive high-frequency data in the first layer using the first Gaussian expansion GSE1, and adds the expanded positive high-frequency data and the 0th positive high-frequency data PD0, to which the 4th weight 0W4 in the 0th layer has been assigned, in the 0th layer.

[0094] Based on these, the multi-resolution restoration function 375 generates a second summation data AD2 based on multiple positive high-frequency data (PD0~PD5) and multiple fourth weights (0W4~5W4), as shown in Figure 16. As shown in Figure 13, the second summation data AD2 in this application corresponds to the positive restoration data PRD in Figure 14. Note that the luminance correction and the display of the luminance-corrected composite image CI in this application are the same as in the second application, so the explanation is omitted.

[0095] For example, if the fourth weight 0W4 in the 0th layer, the fourth weight 1W4 in the 1st layer, and the fourth weight 2W4 in the 2nd layer are all weights of 0, and the fourth weight 3W4 in the 3rd layer, the fourth weight 4W4 in the 4th layer, and the fourth weight 5W4 in the 5th layer are all 1, then this application example will be the same as the second application example. In other words, this application example makes it possible to appropriately adjust the positive high-frequency data that contributes to the second summation data AD2 by assigning a fourth weight to the positive high-frequency data according to the layer.

[0096] According to the ultrasound diagnostic device 1 of the third application example of the first embodiment described above, a fourth weight W4 is assigned to the positive high-frequency data according to the hierarchy to which the positive high-frequency data belongs. The positive high-frequency data to which the fourth weight W4 is assigned is then added together, adjusting the resolution from the lowest resolution to a predetermined resolution, to generate a second summation data PRD. At this time, the fourth weight W4 can be adjusted as appropriate by user instructions via the input interface 13. Thus, with this ultrasound diagnostic device 1, the fourth weight W4 can be set according to multiple hierarchies, expanding the range of contrast adjustment in the composite image CI. As a result, with this ultrasound diagnostic device 1, the brightness of biological information through contrast improvement processing can be set more appropriately according to the user's wishes or default settings. As a result, with this ultrasound diagnostic device 1, the contrast between biological information and non-biological information such as cardiac chambers in the composite image CI can be set more appropriately, further improving the visibility of biological information in the composite image CI. In addition, by appropriately adjusting the fourth weight W4, biological information in the composite image CI can be emphasized.

[0097] Furthermore, this ultrasound diagnostic device 1 can reduce the brightness value in areas showing biological information in conjunction with the reduction in brightness value in areas showing non-biological information. This further reduces the saturation (overexposure) of brightness values ​​in areas showing biological information due to brightness correction, i.e., it enables more appropriate suppression of brightness saturation. As a result, this ultrasound diagnostic device 1 can further improve the visibility of biological information in the composite image CI. Other effects are the same as in the first embodiment, so their explanation will be omitted.

[0098] (Fourth application example) This application example involves generating a composite image CI using power Doppler image data as ultrasound data, determining a threshold for blanking based on the brightness value in the composite image CI corresponding to the power Doppler image, performing blanking using the determined threshold, and generating a power Doppler image. Blanking is a preprocessing step performed, for example, when generating power Doppler images and dispersion images. Blanking is a process that removes noise components by making areas where the velocity value is above a threshold into color display areas. The details of the blanking process can be appropriately utilized using known techniques, so a detailed explanation is omitted.

[0099] The image generation circuit 29 determines a threshold for blanking based on the luminance values ​​in the composite image CI generated by the contrast improvement process. The image generation circuit 29 then performs blanking using the determined threshold to generate a power Doppler image.

[0100] According to the ultrasound diagnostic apparatus 1 of the fourth application example of the first embodiment described above, when the ultrasound data is data relating to a power Doppler image, a threshold for blanking is determined based on the brightness value in the composite image CI, and blanking is performed using the determined threshold to generate a power Doppler image. As a result, with this ultrasound diagnostic apparatus 1, the threshold for blanking can be set using a composite image CI with reduced artifacts, thereby improving the accuracy of noise component removal in power Doppler images and the like generated by blanking. Therefore, with this ultrasound diagnostic apparatus 1, the visibility of biological information and the like in power Doppler images after blanking can be improved. Other effects are the same as in the first embodiment, so their explanation is omitted.

[0101] (Second Embodiment) The difference between the second embodiment and the first embodiment is that the contrast improvement process implemented in the first embodiment is carried out by a medical image processing device. The medical image processing device is implemented, for example, by a server device connected to the modality via a network. The various functions performed by the medical image processing device may be implemented by various in-hospital servers such as a PACS (Picture Archiving and Communication Systems) server.

[0102] Figure 17 shows an example of the configuration of the medical image processing device 20. The processing circuit 37 in the medical image processing device 20 includes, for example, a multi-resolution decomposition function (multi-resolution decomposition unit) 371, an extraction function (extraction unit) 373, a multi-resolution restoration function (multi-resolution restoration unit) 375, a weighting function (weighting unit) 377, a synthesis function (synthesis unit) 379, and a brightness correction function (brightness correction unit) 381. The multi-resolution decomposition function (multi-resolution decomposition unit) 371 generates multiple low-frequency data and multiple high-frequency data corresponding to multiple step-level resolutions from a medical image. The extraction function (extraction unit) 373 extracts negative high-frequency data from the high-frequency data over a predetermined resolution among the multiple step-level resolutions. The multi-resolution restoration function (multi-resolution restoration unit) 375 restores the resolution of the first summed data, obtained by summing the negative high-frequency data over a predetermined resolution, to the same resolution as the medical image before reduction. The synthesis function (synthesis unit) 379 generates a composite image CI based on the restored first additive data and the medical image.

[0103] In the contrast enhancement processing performed by the medical image processing device 20, the medical image to be processed is not limited to ultrasound data. In this embodiment, the contrast enhancement processing can appropriately utilize medical images acquired by various modalities, for example. In this case, the medical image diagnostic device 5 acquires medical images to be used for contrast enhancement processing, corresponding to various modalities. In this case, the communication interface 31 outputs the medical image acquired by the medical image diagnostic device 5 to the processing circuit 37.

[0104] Furthermore, if the medical image input from the medical imaging diagnostic device 5 is volume data, the image processing circuit 21 may generate a two-dimensional image from the volume data using known three-dimensional image processing such as rendering or MPR processing. Next, the image processing circuit 21 outputs the two-dimensional image to the processing circuit 37. The processing circuit 37 performs contrast improvement processing on the two-dimensional image. Furthermore, the processing circuit 37 may perform contrast improvement processing using volume data as the medical image.

[0105] The procedure and effects of the contrast improvement process in the second embodiment are the same as in the first embodiment, so a description will be omitted. The various functions performed in the medical image processing device 20 may be implemented in various modalities or other medical image diagnostic devices 5. Furthermore, as a modification of the second embodiment, the medical image processing device 20 may be implemented by a workstation or cloud computing. In this case, the input interface 13 and the display 15 may be connected to a network as, for example, client devices. Also, the communication interface 31, memory 33, and processing circuit 87 may be implemented on a server on the network. The effects of the second embodiment are the same as in the first embodiment, so a description will be omitted.

[0106] When implementing the technical concepts in various embodiments and modifications using a medical image processing program, the medical image processing program enables the computer to generate multiple low-frequency data and multiple high-frequency data corresponding to multiple stepped resolutions from a medical image; extract negative high-frequency data from the high-frequency data over a predetermined resolution among the multiple stepped resolutions; restore the resolution of the first summed data, obtained by adding the negative high-frequency data over the predetermined resolution, to the same resolution as the medical image before reduction; and generate a composite image CI based on the restored first summed data and the medical image.

[0107] For example, the index acquisition process can also be realized by installing the medical image processing program on a computer in a hospital information system such as a PACS server or integrated server, or in an ultrasound diagnostic device 1, and then loading these into memory. In this case, the program that allows the computer to execute this method can also be stored and distributed on a storage medium such as a magnetic disk (hard disk, etc.), optical disk (CD-ROM, DVD, etc.), or semiconductor memory. The processing procedure and effects of the medical image processing program are the same as in the first embodiment, so a description will be omitted.

[0108] According to at least one embodiment and application example described above, the visibility of biological structures in medical images can be improved.

[0109] Although several embodiments have been described, these embodiments are presented as examples only and are not intended to limit the scope of the invention. These embodiments can be implemented in various other forms, and various omissions, substitutions, modifications, and combinations of embodiments can be made without departing from the spirit of the invention. These embodiments and their variations are included in the scope and spirit of the invention, as well as in the claims and their equivalents. [Explanation of Symbols]

[0110] 1. Ultrasound diagnostic equipment 11. Ultrasound probe 13 Input Interfaces 15 displays 19 Main unit of the device 20 Medical Image Processing Equipment 21 Image Processing Circuit 23 Transmit / Receive Circuit 25 B-mode data generation circuit 27. Doppler Data Generation Circuit 29 Image generation circuit 31 Communication Interface 33 memory 35 Control circuits 37 Processing Circuit 371 Multi-resolution resolution function 373 Extraction function 375. Multi-resolution restoration function 377 Weighting function 379 Synthesis Function 381 Brightness Correction Function

Claims

1. An acquisition unit that acquires multiple negative high-frequency data corresponding to multiple resolutions for which multi-resolution analysis has been performed on ultrasound data, A restoration unit that restores the resolution of the data obtained based on the negative high-frequency data to the resolution corresponding to the ultrasonic data, A generation unit generates an output image based on the restored data with the resolution restored and the ultrasound data, An ultrasound diagnostic device equipped with the following features.

2. The system includes a display unit that displays the output image generated by the generation unit. The ultrasound diagnostic apparatus according to claim 1.

3. The acquisition unit acquires the negative high-frequency data from a plurality of high-frequency data corresponding to a plurality of stepped resolutions obtained by performing the multi-resolution analysis. The ultrasound diagnostic apparatus according to claim 1.

4. The restoration unit restores the resolution of the data obtained by synthesizing the plurality of negative high-frequency data to the resolution corresponding to the ultrasonic data. The ultrasound diagnostic apparatus according to claim 1.

5. The aforementioned resolution is provided in a weighting unit that assigns weights to the restored data, The generation unit generates the output image based on the weighted restored data. The ultrasound diagnostic apparatus according to claim 1.

6. The weighting unit assigns a second weight to the negative high-frequency data, corresponding to the resolution to which the negative high-frequency data belongs. The restoration unit restores the resolution of the data obtained based on the negative high-frequency data to which the second weight has been applied to the resolution corresponding to the ultrasonic data. The ultrasound diagnostic apparatus according to claim 5.

7. The system includes a brightness correction unit that corrects the brightness of the output image based on a first average brightness value related to the ultrasound data and a second average brightness value in the output image. The ultrasound diagnostic apparatus according to any one of claims 1 to 6.

8. The ultrasound data is at least one of the following: data relating to a B-mode image, raw data relating to the B-mode image, and data relating to a power Doppler image. If the ultrasound data is data relating to a power Doppler image, the image generation unit determines a threshold for blanking based on the brightness value in the output image, performs the blanking process using the determined threshold, and generates the power Doppler image. The ultrasound diagnostic apparatus according to any one of claims 1 to 6.

9. An acquisition unit that acquires multiple negative high-frequency data corresponding to multiple resolutions for which multi-resolution analysis has been performed on medical image data, A restoration unit that restores the resolution of the data obtained based on the negative high-frequency data to the resolution corresponding to the medical image data, A generation unit generates an output image based on the restored data with the restored resolution and the medical image data, A medical image processing device equipped with [a specific feature].

10. Computers, An acquisition unit that acquires multiple negative high-frequency data corresponding to multiple resolutions for which multi-resolution analysis has been performed on medical image data. A restoration unit that restores the resolution of the data obtained based on the negative high-frequency data to the resolution corresponding to the medical image data. A generation unit generates an output image based on the restored data with the restored resolution and the medical image data. A medical image processing program that functions as such.

Images