Patent Description:
PTL <NUM> discloses a technique related to OFDI or OCT that acquires an image of a blood vessel lumen using a catheter in which an imaging core for transmitting and receiving light is provided at the tip so as to be rotatable and movable in an axial direction. The "OFDI" is an abbreviation of optical frequency domain imaging. The "OCT" is an abbreviation of optical coherence tomography.

In a stent treatment for bifurcation lesions, the OFDI is useful for evaluating the narrowing and dissociation of a side branch ostium of a bifurcation and the deformation of a stent after side branch dilation. In a case in which a stent strut crossed over at the side branch ostium is expanded with a balloon in single stenting to the main branch, it is possible to reduce the deformation of the stent and the retention of the strut by passing a guide wire inserted into the side branch from the distal side of the ostium. In the OFDI, a function of forming three-dimensional images of blood vessels, stents, and guide wires makes it possible to check whether the guide wire is properly passing through the distal part. This function makes the OFDI extremely useful in understanding the anatomical information on the vascular lumen at the ostium, the running of a side branch wire, and the deformation of the stent strut caused by posterior dilation in the stent treatment for bifurcation lesions.

In the stent treatment, IVUS that acquires an image of a blood vessel lumen using a catheter in which an imaging core for transmitting and receiving ultrasound is provided at the tip so as to be rotatable and movable in an axial direction is also widely used. The "IVUS" is an abbreviation of intravascular ultrasound.

In the OFDI, blood cells in blood vessels are impeditive. Therefore, it is necessary to flush the inside of the blood vessel with, for example, a contrast medium in order to remove the blood cells. On the other hand, in the IVUS, it is usually possible to acquire an image without flushing the inside of the blood vessel. When the inside of the blood vessel is not flushed, it is possible to suppress the use of the contrast medium which is a burden on the kidneys. However, since the blood cell with a brightness value equal to or greater than a predetermined value is included in the image, it is not possible to create an image, in which the state of the guide wire and the stent strut can be observed, with the same method as that in the OFDI.

An object of the present disclosure is to generate an image, in which the state of an object to be detected can be observed, from an observation result of a cross section of a biological tissue by ultrasound.

According to an aspect of the present disclosure, there is provided a diagnosis assistance device including: a control unit that generates line data indicating an intensity value of a reflected wave from a reflection object, which is present in a transmission direction of ultrasound, for each combination of a movement position of an ultrasound transducer that radially transmits the ultrasound while being moved inside a biological tissue and the transmission direction of the ultrasound with reference to an observation result of a cross section of the biological tissue by the ultrasound transducer and generates a detection image which includes pixels corresponding to the generated line data and in which the pixels corresponding to the line data at the same movement position are arranged in one direction, the pixels corresponding to the line data in the same transmission direction are arranged in a direction perpendicular to the one direction, and each pixel value is set according to a degree of abnormality of the generated line data.

The control unit calculates a degree of abnormality of a feature vector indicating features of the line data as the degree of abnormality of the line data.

The control unit generates, as the line data, data indicating an intensity value distribution of the reflected wave in the transmission direction of the ultrasound. The feature vector is a vector indicating a frequency distribution of intensity of the reflected wave in the transmission direction of the ultrasound which is calculated from the intensity value distribution.

As an embodiment of the present disclosure, the control unit refers to a cross-sectional image having a brightness value distribution corresponding to the intensity value distribution as the observation result of the cross section of the biological tissue.

As an embodiment of the present disclosure, the control unit converts the intensity value distribution into a distribution from a center of gravity of the cross section observed by the ultrasound transducer and generates data indicating the converted distribution as the line data.

As an embodiment of the present disclosure, the feature vector is a vector indicating the intensity value distribution.

As an embodiment of the present disclosure, the feature vector is a vector that is calculated using a change in the intensity value distribution caused by a difference in the movement position of the ultrasound transducer.

As an embodiment of the present disclosure, the feature vector is a vector that is calculated using a change in the intensity value distribution caused by a difference in the transmission direction of the ultrasound.

As an embodiment of the present disclosure, the control unit compares the feature vector with an identification vector for identifying an object to be detected and calculates the degree of abnormality of the feature vector as a result of the comparison.

As an embodiment of the present disclosure, the control unit calculates the degree of abnormality of the feature vector using at least two different vectors as the identification vector and sets at least two of an R value, a G value, and a B value according to the degree of abnormality calculated using different vectors when setting an RGB value of each pixel as each pixel value of the detection image.

As an embodiment of the present disclosure, when setting the RGB value of each pixel as each pixel value of the detection image, the control unit sets one or two of the R value, the G value, and the B value according to statistics of the line data and sets at least one of the remaining values of the R value, the G value, and the B value according to the degree of abnormality of the feature vector.

As an embodiment of the present disclosure, the control unit calculates the degree of abnormality of the feature vector using a vector which is different for each type of the object to be detected as the identification vector.

As an embodiment of the present disclosure, the object to be detected includes at least one of a stent, a guide wire, a vessel wall, a calcified lesion, and a plaque.

As an embodiment of the present disclosure, the control unit analyzes the line data to detect a position of a blood cell region that is present in the transmission direction of the ultrasound and calculates a degree of abnormality of a vector, from which an element corresponding to the detected position has been excluded, as the degree of abnormality of the feature vector.

As an embodiment of the present disclosure, the control unit generates, as the detection image, a developed image in which the biological tissue is cut open along a movement direction of the ultrasound transducer.

According to another aspect of the present disclosure, there is provided a diagnosis assistance system including the diagnosis assistance device and a probe including the ultrasound transducer.

According to still another aspect of the present disclosure, there is provided a diagnosis assistance method, which does not form part of the invention, including: allowing an ultrasound transducer to radially transmit ultrasound while being moved inside a biological tissue; allowing a diagnosis assistance device to generate line data indicating an intensity value of a reflected wave from a reflection object, which is present in a transmission direction of the ultrasound, for each combination of a movement position of the ultrasound transducer and the transmission direction of the ultrasound with reference to an observation result of a cross section of the biological tissue by the ultrasound transducer; and allowing the diagnosis assistance device to generate a detection image which includes pixels corresponding to the generated line data and in which the pixels corresponding to the line data at the same movement position are arranged in one direction, the pixels corresponding to the line data in the same transmission direction are arranged in a direction perpendicular to the one direction, and each pixel value is set according to a degree of abnormality of the generated line data.

According to an embodiment of the present disclosure, it is possible to generate an image, in which the state of an object to be detected can be observed, from an observation result of a cross section of a biological tissue by ultrasound.

Hereinafter, an embodiment of the present disclosure will be described with reference to the drawings.

In each of the drawings, the same or equivalent portions are denoted by the same reference numerals. In the description of this embodiment, the description of the same or equivalent portions is appropriately omitted or simplified.

The outline of this embodiment will be described with reference to <FIG>, <FIG>, and <FIG>.

In this embodiment, an ultrasound transducer <NUM> radially transmits ultrasound while moving inside a biological tissue. A diagnosis assistance device <NUM> generates line data L[z, θ] indicating an intensity value of a reflected wave from a reflection object, which is present in a transmission direction θ of the ultrasound, for each combination of a movement position z of the ultrasound transducer <NUM> and the transmission direction θ of the ultrasound with reference to the observation result of the cross section of the biological tissue by the ultrasound transducer <NUM>. The diagnosis assistance device <NUM> generates a detection image which includes pixels P[z, θ] corresponding to the generated line data L[z, θ] and in which the pixels P[z, θ] corresponding to the line data L[z, θ] at the same movement position z are arranged in one direction, the pixels P[z, θ] corresponding to the line data L[z, θ] in the same transmission direction θ are arranged in a direction perpendicular to the one direction, and each pixel value is set according to the degree of abnormality of the generated line data L[z, θ].

According to this embodiment, it is possible to generate an image in which the state of an object to be detected can be observed from the observation result of the cross section of the biological tissue by the ultrasound. The biological tissue to be observed is a blood vessel in this embodiment, but may be an organ, such as the heart, or other biological tissues. The object to be detected is a stent in this embodiment, but may be a guide wire, a vessel wall, a calcified lesion, a plaque, or other reflection objects. For example, the object to be detected includes at least one of the stent, the guide wire, the vessel wall, the calcified lesion, and the plaque.

In this embodiment, data indicating an intensity value distribution A[z, θ] of the reflected wave in the transmission direction θ of the ultrasound is generated as the line data L[z, θ].

In this embodiment, a cross-sectional image <NUM> having a brightness value distribution corresponding to the intensity value distribution A[z, θ] is referred to as the observation result of the cross section of the biological tissue. The cross-sectional image <NUM> is, specifically, an IVUS two-dimensional image.

In this embodiment, a developed image <NUM> in which the biological tissue is cut open along the movement direction of the ultrasound transducer <NUM> is generated as the detection image. The developed image <NUM> is, specifically, a color map.

In this embodiment, the features of the line data L[z, θ] are extracted, and the degree of abnormality of a feature vector p indicating the extracted features is calculated. That is, the degree of abnormality of the feature vector p indicating the features of the line data L[z, θ] is calculated as the degree of abnormality of the line data L[z, Θ]. Specifically, the feature vector p is a vector indicating the intensity value distribution A[z, θ].

In this embodiment, comparison C between the feature vector p and an identification vector q for identifying the object to be detected is performed, and the degree of abnormality of the feature vector p is calculated as the result of the comparison C. Specifically, the identification vector q is a vector indicating an identification pattern of the object to be detected. Specifically, the degree of abnormality of the feature vector p is a similarity between the feature vector p and the identification vector q.

The configuration of a diagnosis assistance system <NUM> according to this embodiment will be described with reference to <FIG>.

The diagnosis assistance system <NUM> includes the diagnosis assistance device <NUM>, a cable <NUM>, a drive unit <NUM>, a keyboard <NUM>, a mouse <NUM>, and a display <NUM>.

The diagnosis assistance device <NUM> is a dedicated computer that is specialized for diagnostic imaging in this embodiment, but may be a general-purpose computer such as a PC. The "PC" is an abbreviation of personal computer.

The cable <NUM> is used to connect the diagnosis assistance device <NUM> and the drive unit <NUM>.

The drive unit <NUM> is a device that is connected to a probe <NUM> illustrated in <FIG> and is used to drive the probe <NUM>. The drive unit <NUM> is also called as an MDU. The "MDU" is an abbreviation of motor drive unit. The probe <NUM> is applied to the IVUS. The probe <NUM> is also called an IVUS catheter or a diagnostic imaging catheter.

The keyboard <NUM>, the mouse <NUM>, and the display <NUM> are connected to the diagnosis assistance device <NUM> through any cables or wirelessly. The display <NUM> is, for example, an LCD, an organic EL display, or an HMD. The "LCD" is an abbreviation of liquid crystal display. The "EL" is an abbreviation of electro luminescence. The "HMD" is an abbreviation of head-mounted display.

The diagnosis assistance system <NUM> further includes a connection terminal <NUM> and a cart unit <NUM> as options.

The connection terminal <NUM> is used to connect the diagnosis assistance device <NUM> and an external device. The connection terminal <NUM> is, for example, a USB terminal. The "USB" is an abbreviation of Universal Serial Bus. The external device is, for example, a recording medium such as a magnetic disk drive, a magneto-optical disk drive, or an optical disk drive.

The cart unit <NUM> is a cart with casters for movement. The diagnosis assistance device <NUM>, the cable <NUM>, and the drive unit <NUM> are installed in a cart main body of the cart unit <NUM>. The keyboard <NUM>, the mouse <NUM>, and the display <NUM> are installed on a table at the top of the cart unit <NUM>.

The configuration of the probe <NUM> and the drive unit <NUM> according to this embodiment will be described with reference to <FIG>.

The probe <NUM> includes a drive shaft <NUM>, a hub <NUM>, a sheath <NUM>, an outer tube <NUM>, the ultrasound transducer <NUM>, and a relay connector <NUM>.

The drive shaft <NUM> passes through the sheath <NUM> inserted into the body cavity of a living body and the outer tube <NUM> connected to a base end of the sheath <NUM> and extends to the inside of the hub <NUM> provided at a base end of the probe <NUM>. The drive shaft <NUM> has the ultrasound transducer <NUM>, which transmits and receives signals, at the distal end and is rotatably provided in the sheath <NUM> and the outer tube <NUM>. The relay connector <NUM> connects the sheath <NUM> and the outer tube <NUM>.

The hub <NUM>, the drive shaft <NUM>, and the ultrasound transducer <NUM> are connected to each other so as to be integrally moved forward and backward in an axial direction. Therefore, for example, when an operation of pushing the hub <NUM> to the distal end is performed, the drive shaft <NUM> and the ultrasound transducer <NUM> are moved to the distal end in the sheath <NUM>. For example, when an operation of pulling the hub <NUM> to the base end is performed, the drive shaft <NUM> and the ultrasound transducer <NUM> are moved to the base end in the sheath <NUM> as represented by an arrow.

The drive unit <NUM> includes a scanner unit <NUM>, a slide unit <NUM>, and a bottom cover <NUM>.

The scanner unit <NUM> is connected to the diagnosis assistance device <NUM> through the cable <NUM>. The scanner unit <NUM> includes a probe connection portion <NUM> connected to the probe <NUM> and a scanner motor <NUM> which is a driving source for rotating the drive shaft <NUM>.

The probe connection portion <NUM> is removably connected to the probe <NUM> through an insertion hole <NUM> of the hub <NUM> which is provided at the base end of the probe <NUM>. In the hub <NUM>, the base end of the drive shaft <NUM> is rotatably supported, and the rotational force of the scanner motor <NUM> is transmitted to the drive shaft <NUM>. In addition, signals are transmitted and received between the drive shaft <NUM> and the diagnosis assistance device <NUM> through the cable <NUM>. The diagnosis assistance device <NUM> performs the generation of a tomographic image of a biological lumen and image processing on the basis of the signals transmitted from the drive shaft <NUM>.

The scanner unit <NUM> is mounted on the slide unit <NUM> so as to be movable forward and backward, and the slide unit <NUM> is mechanically and electrically connected to the scanner unit <NUM>. The slide unit <NUM> includes a probe clamp portion <NUM>, a slide motor <NUM>, and a switch group <NUM>.

The probe clamp portion <NUM> is provided closer to the distal end than the probe connection portion <NUM> so as to be disposed coaxially with the probe connection portion <NUM> and supports the probe <NUM> connected to the probe connection portion <NUM>.

The slide motor <NUM> is a drive source that generates a driving force in the axial direction. The scanner unit <NUM> is moved forward and backward by the driving of the slide motor <NUM>, and the drive shaft <NUM> is moved forward and backward in the axial direction with the movement of the scanner unit <NUM>. The slide motor <NUM> is, for example, a servo-motor.

The switch group <NUM> includes, for example, a forward switch and a pullback switch which are pressed at the time of the operation of moving the scanner unit <NUM> forward and backward and a scan switch that is pressed when imaging is started and ended. The present disclosure is not limited to this example, and various switches are included in the switch group <NUM> as needed.

When the forward switch is pressed, the slide motor <NUM> is rotated forward, and the scanner unit <NUM> is moved forward. On the other hand, when the pull-back switch is pressed, the slide motor <NUM> is rotated backward, and the scanner unit <NUM> is moved backward.

When the scan switch is pressed, imaging is started, and the scanner motor <NUM> is driven. In addition, the slide motor <NUM> is driven to move the scanner unit <NUM> backward. A user, such as an operator, connects the probe <NUM> to the scanner unit <NUM> in advance such that the drive shaft <NUM> is moved to the base end in the axial direction while being rotated with the start of imaging. The scanner motor <NUM> and the slide motor <NUM> are stopped when the scan switch is pressed again, and the imaging ends.

The bottom cover <NUM> covers a bottom surface of the slide unit <NUM> and the entire periphery of a side surface on the bottom surface side and is provided so as to be close to and separated from the bottom surface of the slide unit <NUM>.

As illustrated in <FIG>, in the IVUS cross-sectional image <NUM>, since there is a blood cell portion, it is not possible to check the entire blood vessel using the same method as that in the OFDI. Therefore, in this embodiment, the diagnosis assistance device <NUM> reconstructs the detection image which is the blood vessel image, using the line data L[z, θ] that is obtained by moving an imaging core, which transmits an ultrasound signal toward the blood vessel and detects a reflected signal, in a catheter while rotating the imaging core and extends radially from the center of rotation of the imaging core. The imaging core is composed of at least the drive shaft <NUM> and the ultrasound transducer <NUM>.

Specifically, the diagnosis assistance device <NUM> generates a reception vector including a value indicating the intensity of the reflected signal and a depth component with reference to the line data L[z, θ] obtained by the rotation operation of the imaging core. The "depth" is a distance to the reflection object in the transmission direction θ of the ultrasound signal. The diagnosis assistance device <NUM> calculates the similarity between the generated reception vector and a detection vector having a characteristic that makes it easy to discriminate the pattern of the reflected waves obtained in a case in which the stent is present in the transmission direction θ of the ultrasound signal. The diagnosis assistance device <NUM> classifies data which has been three-dimensionally collected, using the position z of the ultrasound transducer <NUM>, the transmission direction θ of the ultrasound signal, and the similarity as parameters, and displays the classification result. The reception vector corresponds to the feature vector p. The detection vector corresponds to the identification vector q. The similarity corresponds to the degree of abnormality of the feature vector p. The reception vector may include the frequency of occurrence of signal intensity as a component or may include both the depth and the frequency of occurrence as components.

Specifically, the diagnosis assistance device <NUM> maps each reception vector to a polar coordinate space, performs projection in the radial direction of the blood vessel, and assigns a color on a projection plane using the similarity to generate the developed image <NUM> in which the blood vessel is cut open in a long axis direction. The color corresponds to the pixel value of a pixel P[z, θ].

According to this embodiment, the design of the stent can be identified by the color in the developed image <NUM>. In particular, the developed image <NUM> is useful for evaluating the narrowing and dissociation of the side branch ostium of the bifurcation and the deformation of the stent after side branch dilation.

The configuration of the diagnosis assistance device <NUM> according to this embodiment will be described with reference to <FIG>.

The diagnosis assistance device <NUM> includes components such as a control unit <NUM>, a storage unit <NUM>, a communication unit <NUM>, an input unit <NUM>, and an output unit <NUM>.

The control unit <NUM> is one or more processors. The processor is a general-purpose processor, such as a CPU or a GPU, or a dedicated processor specialized for a specific process. The "CPU" is an abbreviation of central processing unit. The "GPU" is an abbreviation of graphics processing unit. The control unit <NUM> may include one or more dedicated circuits. Alternatively, in the control unit <NUM>, one or more processors may be replaced with one or more dedicated circuits. The dedicated circuit is, for example, an FPGA or an ASIC. The "FPGA" is an abbreviation of field-programmable gate array. The "ASIC" is an abbreviation of application specific integrated circuit. The control unit <NUM> performs information processing related to the operation of the diagnosis assistance device <NUM> while controlling each unit of the diagnosis assistance system <NUM> including the diagnosis assistance device <NUM>.

The storage unit <NUM> is one or more semiconductor memories, one or more magnetic memories, one or more optical memories, or a combination of at least two of them. The semiconductor memory is, for example, a RAM or a ROM. The "RAM" is an abbreviation of random access memory. The "ROM" is an abbreviation of read only memory. The RAM is, for example, an SRAM or a DRAM. The "SRAM" is an abbreviation of static random access memory. The "DRAM" is an abbreviation of dynamic random access memory. The ROM is, for example, an EEPROM. The "EEPROM" is an abbreviation of electrically erasable programmable read only memory. The storage unit <NUM> functions as, for example, a main storage device, an auxiliary storage device, or a cache memory. The storage unit <NUM> stores information used for the operation of the diagnosis assistance device <NUM> and information obtained by the operation of the diagnosis assistance device <NUM>.

The communication unit <NUM> is one or more communication interfaces. The communication interface is a wired LAN interface, a wireless LAN interface, or a diagnostic imaging interface that receives IVUS signals and performs A/D conversion on the IVUS signals. The "LAN" is an abbreviation of local area network. The "A/D" is an abbreviation of analog to digital. The communication unit <NUM> receives the information used for the operation of the diagnosis assistance device <NUM> and transmits the information obtained by the operation of the diagnosis assistance device <NUM>. In this embodiment, the drive unit <NUM> is connected to the diagnostic imaging interface included in the communication unit <NUM>.

The input unit <NUM> is one or more input interfaces. The input interface is, for example, a USB interface or an HDMI (registered trademark) interface. The "HDMI (registered trademark)" is an abbreviation of High-Definition Multimedia Interface. The input unit <NUM> receives an operation of inputting the information used for the operation of the diagnosis assistance device <NUM>. In this embodiment, the keyboard <NUM> and the mouse <NUM> are connected to the USB interfaces included in the input unit <NUM>. However, the keyboard <NUM> and the mouse <NUM> may be connected to the wireless LAN interfaces included in the communication unit <NUM>.

The output unit <NUM> is one or more output interfaces. The output interface is, for example, a USB interface or an HDMI (registered trademark) interface. The output unit <NUM> outputs the information obtained by the operation of the diagnosis assistance device <NUM>. In this embodiment, the display <NUM> is connected to the HDMI (registered trademark) interface included in the output unit <NUM>.

The processor included in the control unit <NUM> executes a diagnosis assistance program according to this embodiment to implement the functions of the diagnosis assistance device <NUM>. That is, the functions of the diagnosis assistance device <NUM> are implemented by software. The diagnosis assistance program is a program for causing a computer to perform processes in steps included in the operation of the diagnosis assistance device <NUM> so as to implement functions corresponding to the processes in the steps. That is, the diagnosis assistance program is a program for causing the computer to function as the diagnosis assistance device <NUM>.

The program can be recorded on a computer-readable recording medium. The computer-readable recording medium is, for example, a magnetic recording device, an optical disk, a magneto-optical recording medium, or a semiconductor memory. The program is distributed, for example, by selling, transferring, or renting a portable recording medium such as a DVD or a CD-ROM having the program recorded thereon. The "DVD" is an abbreviation of digital versatile disc. The "CD-ROM" is an abbreviation of compact disc read only memory. The program may be distributed by storing the program in a storage of a server and transmitting the program from the server to another computer through the network. The program may be provided as a program product.

For example, the computer temporarily stores the program recorded on the portable recording medium or the program transmitted from the server in the main storage device. Then, in the computer, the processor reads the program stored in the main storage device and performs a process according to the read program. The computer may read the program directly from the portable recording medium and perform the process according to the program. The computer may sequentially perform the process according to the received program each time the program is transmitted from the server to the computer. The process may be performed by a so-called ASP-type service that implements functions only by an execution instruction and the acquisition of results without transmitting the program from the server to the computer. The "ASP" is an abbreviation of application service provider. The program includes information that is used for the processes of an electronic computer and is equivalent to the program. For example, data that is not a direct command to the computer but has the property of defining the processes of the computer corresponds to the "information equivalent to the program".

Some or all of the functions of the diagnosis assistance device <NUM> may be implemented by the dedicated circuit included in the control unit <NUM>. That is, some or all of the functions of the diagnosis assistance device <NUM> may be implemented by hardware.

The operation of the diagnosis assistance system <NUM> according to this embodiment will be described with reference to <FIG>. The operation of the diagnosis assistance system <NUM> corresponds to a diagnosis assistance method according to this embodiment.

Before the flow illustrated in <FIG> is started, the user primes the probe <NUM>. Then, the probe <NUM> is fitted to the probe connection portion <NUM> and the probe clamp portion <NUM> of the drive unit <NUM> and is connected and fixed to the drive unit <NUM>. Then, the probe <NUM> is inserted into a target part in the blood vessel.

In Step S1, a so-called pullback operation is performed by pressing the scan switch included in the switch group <NUM> and further pressing the pullback switch included in the switch group <NUM>. The probe <NUM> transmits ultrasound inside the blood vessel using the ultrasound transducer <NUM> that is moved backward in the axial direction by the pullback operation. The ultrasound transducer <NUM> radially transmits the ultrasound while being moved inside the blood vessel. The ultrasound transducer <NUM> receives a reflected wave of the transmitted ultrasound. The probe <NUM> inputs a signal of the reflected wave received by the ultrasound transducer <NUM> to the control unit <NUM> of the diagnosis assistance device <NUM>. The control unit <NUM> processes the input signal to sequentially generate the cross-sectional image <NUM> of the blood vessel and acquires tomographic images including a plurality of cross-sectional images <NUM>.

Specifically, as illustrated in <FIG>, the probe <NUM> transmits the ultrasound in <NUM> directions from the center of rotation to the outside using the ultrasound transducer <NUM> while rotating the ultrasound transducer <NUM> in a θ direction and moving the ultrasound transducer <NUM> in the z direction inside the blood vessel. The probe <NUM> receives the reflected waves from reflection objects that are present in each of the <NUM> directions inside the blood vessel using the ultrasound transducer <NUM>. The probe <NUM> transmits a signal of the received reflected waves to the diagnosis assistance device <NUM> through the drive unit <NUM> and the cable <NUM>. The communication unit <NUM> of the diagnosis assistance device <NUM> receives the signal transmitted from the probe <NUM>. The communication unit <NUM> performs A/D conversion on the received signal. The communication unit <NUM> inputs the signal subjected to the A/D conversion to the control unit <NUM>. The control unit <NUM> processes the input signal to sequentially generate frame data on the IVUS cross-sectional image <NUM>, thereby generating an IVUS tomographic image. The control unit <NUM> stores the generated tomographic image in the storage unit <NUM>.

As a modification example of this embodiment, an ultrasound transducer that transmits ultrasound in a plurality of directions without being rotated may be used instead of the ultrasound transducer <NUM> that transmits the ultrasound in a plurality of directions while being rotated in the θ direction.

As a modification example of this embodiment, instead of the configuration in which the diagnosis assistance device <NUM> generates the tomographic image, another device may generate the tomographic image, and the diagnosis assistance device <NUM> may acquire the tomographic image from another device. That is, instead of the configuration in which the control unit <NUM> of the diagnosis assistance device <NUM> processes the IVUS signal to generate the cross-sectional image <NUM> of the blood vessel, another device may process the IVUS signal to generate the cross-sectional image <NUM> of the blood vessel and input the generated cross-sectional image <NUM> to the control unit <NUM>.

In Step S2, the control unit <NUM> of the diagnosis assistance device <NUM> performs control to display any cross-sectional image <NUM> included in the tomographic images.

Specifically, the control unit <NUM> displays the cross-sectional image <NUM> of any frame data included in the tomographic images stored in the storage unit <NUM> on the display <NUM> through the output unit <NUM>.

In Step S3, the control unit <NUM> of the diagnosis assistance device <NUM> acquires an observation position on the cross-sectional image <NUM> as illustrated in <FIG>.

In Step S4, the control unit <NUM> of the diagnosis assistance device <NUM> maps the observation position to a polar coordinate space <NUM> as illustrated in <FIG>.

In Step S5, the control unit <NUM> of the diagnosis assistance device <NUM> creates the developed images <NUM> corresponding to the required number of frames having the observation position as the center.

Specifically, the control unit <NUM> generates the line data L[z, θ] for each combination of the movement position z of the ultrasound transducer <NUM> and the transmission direction θ of the ultrasound, with reference to the observation result of the cross section of the blood vessel by the ultrasound transducer <NUM>. The line data L[z, θ] is data indicating the intensity value distribution A[z, θ] of the reflected waves from the reflection object that is present in the transmission direction θ of the ultrasound. The control unit <NUM> stores the generated line data L[z, θ] in the storage unit <NUM>. The control unit <NUM> generates the developed image <NUM> that includes the pixels P[z, θ] corresponding to the line data L[z, θ] stored in the storage unit <NUM>. The developed image <NUM> is an image in which the pixels P[z, θ] corresponding to the line data L[z, θ] at the same movement position z are arranged in the θ direction which is one direction, the pixels P[z, θ] corresponding to the line data L[z, θ] in the same transmission direction θ are arranged in the z direction which is a direction perpendicular to the θ direction. The developed image <NUM> is an image in which the blood vessel is cut open along the movement direction of the ultrasound transducer <NUM>. The control unit <NUM> stores the generated developed image <NUM> in the storage unit <NUM>.

For example, the control unit <NUM> refers to the cross-sectional image <NUM> of "#<NUM> frame data" illustrated in <FIG> as the observation result of the cross section of the blood vessel by the ultrasound transducer <NUM> at movement position <NUM>. The cross-sectional image <NUM> of the "#<NUM> frame data" is a two-dimensional image having brightness value distributions corresponding to intensity value distributions A[<NUM>, <NUM>] to A[<NUM>, <NUM>]. The control unit <NUM> analyzes the brightness value distribution of "line <NUM>" illustrated in <FIG> and <FIG> in the cross-sectional image <NUM> of the "#<NUM> frame data" to generate line data L[<NUM>, <NUM>]. The line data L[<NUM>, <NUM>] is data indicating the intensity value distribution A[<NUM>, <NUM>] of the reflected waves from a reflection object that is present in ultrasound transmission direction <NUM>. In addition, the control unit <NUM> analyzes the brightness value distribution of "line <NUM>" illustrated in <FIG> and <FIG> in the cross-sectional image <NUM> of the "#<NUM> frame data" to generate line data L[<NUM>, <NUM>]. The line data L[<NUM>, <NUM>] is data indicating the intensity value distribution A[<NUM>, <NUM>] of the reflected waves from a reflection object that is present in ultrasound transmission direction <NUM>. Similarly, the control unit <NUM> analyzes the brightness value distributions of "line <NUM>" to "line <NUM>" in the cross-sectional image <NUM> of the "#<NUM> frame data" to generate line data L[<NUM>, <NUM>] to line data L[<NUM>, <NUM>], respectively.

The control unit <NUM> refers to the cross-sectional image <NUM> of "#<NUM> frame data" illustrated in <FIG> as the observation result of the cross section of the blood vessel by the ultrasound transducer <NUM> at movement position <NUM>. The cross-sectional image <NUM> of the "#<NUM> frame data" is a two-dimensional image having brightness value distributions corresponding to intensity value distributions A[<NUM>, <NUM>] to A[<NUM>, <NUM>]. The control unit <NUM> analyzes the brightness value distribution of each line in the cross-sectional image <NUM> of the "#<NUM> frame data" to generate line data L[<NUM>, <NUM>] to line data L[<NUM>, <NUM>], as in the case of the cross-sectional image <NUM> of the "#<NUM> frame data".

The control unit <NUM> generates line data L[z, θ] other than the line data L[<NUM>, <NUM>] to the line data L[<NUM>, <NUM>] and the line data L[<NUM>, <NUM>] to the line data L[<NUM>, <NUM>] with reference to the cross-sectional images <NUM> of "#<NUM> frame data" and the subsequent frame data as in the case of the cross-sectional images <NUM> of the "#<NUM> frame data" and the "#<NUM> frame data".

In the developed image <NUM> generated by the control unit <NUM>, the pixels P[<NUM>, <NUM>] to P[<NUM>, <NUM>] respectively corresponding to the line data L[<NUM>, <NUM>] to the line data L[<NUM>, <NUM>] at the movement position <NUM> are arranged in a column. The pixels P[<NUM>,<NUM>] to P[<NUM>, <NUM>] respectively corresponding to the line data L[<NUM>, <NUM>] to the line data L[<NUM>, <NUM>] at the movement position <NUM> are arranged in the next column. Similarly, the pixels P[z, θ] corresponding to the line data L[z, θ] at movement position <NUM> and the subsequent movement positions are sequentially arranged in the next columns.

As a modification example of this embodiment, the control unit <NUM> may convert the intensity value distribution A[z, θ] into a distribution from the center of gravity of the cross section observed by the ultrasound transducer <NUM> and generate data indicating the converted distribution as the line data L[z, Θ]. The "cross section observed by the ultrasound transducer <NUM>" is the cross section of the biological tissue. However, it is assumed that the stent is also regarded as a portion of the biological tissue at the position where the stent is placed. Specifically, the control unit <NUM> may calculate the brightness value distribution from the center of gravity of the cross section of the blood vessel with reference to the cross-sectional image <NUM> and generate data indicating the calculated brightness value distribution as the line data L[z, θ]. That is, the control unit <NUM> may analyze the IVUS image to detect a cross-sectional component of the blood vessel and reconstruct, as the line data L[z, Θ], data indicating the intensity value distribution obtained by rθ reverse conversion from the position of the center of gravity of the blood vessel.

According to this modification example, the starting point of the intensity value distribution A[z, θ] indicated by the line data L[z, θ] at each movement position z is aligned with the position of the center of gravity of the cross section from the position z of the ultrasound transducer <NUM>. Therefore, it is possible to more accurately express the shape of the object to be detected in the finally obtained detection image.

In Step S6, the control unit <NUM> of the diagnosis assistance device <NUM> calculates the similarity between a brightness distribution <NUM> of each line and a classification pattern <NUM> which is a pattern of brightness with respect to the depth as illustrated in <FIG>.

Specifically, the control unit <NUM> extracts the features of the line data L[z, θ] stored in the storage unit <NUM> and stores the feature vector p indicating the extracted features in the storage unit <NUM>. That is, the control unit <NUM> stores the feature vector p indicating the intensity value distribution A[z, θ] indicated by the line data L[z, θ] in the storage unit <NUM>. The control unit <NUM> calculates the similarity between the feature vector p stored in the storage unit <NUM> and the identification vector q, which has been stored in the storage unit <NUM> in advance and is used to identify the stent present in the transmission direction θ of the ultrasound, to perform the comparison C between the feature vector p and the identification vector q. The control unit <NUM> stores the similarity as the result of the comparison C in the storage unit <NUM>. That is, the control unit <NUM> calculates the degree of abnormality of the feature vector p as the result of the comparison C and stores the calculated degree of abnormality in the storage unit <NUM>.

For example, the control unit <NUM> calculates the similarity between the brightness distribution <NUM> corresponding to the intensity value distribution A[<NUM>, <NUM>] indicated by the line data L[<NUM>, <NUM>] and the classification pattern <NUM> of the stent which has been defined in advance. In addition, the control unit <NUM> calculates the similarity between the brightness distribution <NUM> corresponding to the intensity value distribution A[<NUM>, <NUM>] indicated by the line data L[<NUM>, <NUM>] and the classification pattern <NUM> of the stent. Similarly, the control unit <NUM> calculates the similarities between the brightness distributions <NUM> corresponding to the intensity value distributions A[z, θ] indicated by other line data L[z, θ] and the classification pattern <NUM> of the stent.

Any method may be used to calculate the similarity. However, in this embodiment, an algorithm of cluster analysis or a nearest neighbor classification method is used. An example of this algorithm is a k-means method. The cluster analysis is a method that classifies an object to be classified into the closest class on the basis of a distance in a feature space. The "distance" is an index indicating the similarity and is, for example, a cosine similarity, a Euclidean distance, a standardized Euclidean distance, an average Euclidean distance, a Mahalanobis distance, a Pearson's correlation coefficient, a Jaccard coefficient, or a deviation pattern similarity. The cosine similarity is an index indicating the closeness of the angle between vectors. The Euclidean distance is an index indicating the closeness of the distance between vectors. The Mahalanobis distance is a distance normalized by variance. In the cluster analysis, classification is performed so as to minimize the distance between an input pattern and a representative pattern of each class. However, in this embodiment, the distance is used to create the color map.

An example of an expression for calculating the cosine similarity as the similarity between the feature vector p and the identification vector q is given below.

As a modification example of this embodiment, a different classification pattern <NUM> may be used for each type of object to be detected such as a stent, a guide wire, a vessel wall, a calcified lesion, and a plaque. That is, the control unit <NUM> may use a vector, which is different for each type of object to be detected, as the identification vector q to calculate the degree of abnormality of the feature vector p.

According to this modification example, since the similarity changes depending on the type of the object to be detected, each type of object to be detected can be displayed in different colors in the finally obtained detection image.

As a modification example of this embodiment, the control unit <NUM> may analyze the line data L[z, θ] to detect the position of a blood cell region that is present in the transmission direction θ of the ultrasound, and calculate, as the degree of abnormality of the feature vector p, the degree of abnormality of a vector from which an element corresponding to the detected position has been excluded. For example, the control unit <NUM> may analyze the line data L[z, θ] to detect a region in which a change in the intensity value from neighboring data is larger than a predetermined threshold value and may suppress data that is located inside the region or exclude the data from the object to be calculated. Alternatively, the control unit <NUM> may analyze the line data L[z, θ] to detect a region in which the intensity value is larger than a predetermined threshold value and may suppress data that is located inside the region or exclude the data from the object to be calculated.

According to this modification example, it is possible to more reliably suppress the influence of blood cell noise.

In Step S7, the control unit <NUM> of the diagnosis assistance device <NUM> performs control to assign a color to the developed image <NUM> on the basis of the similarity of each line and to display the developed image <NUM> as a color map as illustrated in <FIG>.

Specifically, the control unit <NUM> sets each pixel value of the developed image <NUM> stored in the storage unit <NUM> on the basis of the result of the comparison C stored in the storage unit <NUM>. That is, the control unit <NUM> sets each pixel value of the developed image <NUM> to a value corresponding to the similarity between the feature vector p and the identification vector q. As a result, a detection image which includes the pixels P[z, θ] corresponding to the line data L[z, θ] and in which the pixels P[z, θ] corresponding to the line data L[z, θ] at the same movement position z are arranged in the θ direction, the pixels P[z, θ] corresponding to the line data L[z, θ] in the same transmission direction θ are arranged in the z direction, and each pixel value is set on the basis of the result of the comparison C is obtained. Each pixel value is a color in this embodiment and is, specifically, an RGB value. However, each pixel value may be brightness, a combination of a color and brightness, or other pixel values. The control unit <NUM> displays the obtained detection image on the display <NUM> through the output unit <NUM>. In the example illustrated in <FIG>, the control unit <NUM> sets each pixel value of the developed image <NUM> such that, as the cosine similarity between the feature vector p and the identification vector q which corresponds to the degree of abnormality of the feature vector p becomes higher, pixel intensity corresponding to the pixel value of the corresponding pixel becomes higher.

As a modification example of this embodiment, the control unit <NUM> may calculate the degree of abnormality of the feature vector p using at least two different vectors as the identification vector q. When the RGB value of each pixel P[z, θ] is set as each pixel value of the detection image, the control unit may set at least two of the R value, the G value, and the B value according to the degree of abnormality calculated using different vectors. Alternatively, when the RGB value of each pixel P[z, θ] is set as each pixel value of the detection image, the control unit <NUM> may set one or two of the R value, the G value, and the B value according to the statistics of the line data L[z, θ] and set at least one of the remaining values of the R value, G value, and B value according to the degree of abnormality of the feature vector p. For example, the control unit <NUM> may set the G value according to the cosine similarity corresponding to the degree of abnormality of the feature vector p, set the R value according to the standard deviation of a brightness value equal to or greater than a predetermined threshold value which corresponds to a statistic of the line data L[z, θ], and set the B value according to the proportion of the bright value equal to or greater than the predetermined threshold value to the line which corresponds to another statistic of the line data L[z, θ].

According to this modification example, for example, even when the cosine similarity between the brightness distribution <NUM> of the line in which the stent is actually present and the classification pattern <NUM> for detecting the stent is small, it is possible to set at least one of the R value, the G value, and the B value to a value corresponding to the stent, using the statistics of the line. Therefore, it is possible to accurately display the object to be detected in a different color in the finally obtained detection image.

As a modification example of this embodiment, the control unit <NUM> may receive an operation of selecting the position that the user wants to display in the detection image through the input unit <NUM>. In this case, the control unit <NUM> displays the detection image having the selected position as the center on the display <NUM> through the output unit <NUM>. Alternatively, the control unit <NUM> may receive an operation of selecting a display range in the z direction through the input unit <NUM>. In this case, the control unit <NUM> generates a detection image in the selected display range and displays the generated detection image on the display <NUM> through the output unit <NUM>. The display range is selected by a combination of the position in the z direction and the size in the z direction based on the position.

As described above, in this embodiment, the control unit <NUM> of the diagnosis assistance device <NUM> generates the line data L[z, θ] indicating the intensity value of the reflected wave from the reflection object, which is present in the transmission direction θ of the ultrasound, for each combination of the movement position z of the ultrasound transducer <NUM> that radially transmits the ultrasound while being moved in the biological tissue and the transmission direction θ of the ultrasound with reference to the observation result of the cross section of the biological tissue by the ultrasound transducer <NUM>. The control unit <NUM> generates the detection image which includes the pixels P[z, θ] corresponding to the generated line data L[z, θ] and in which the pixels P[z, θ] corresponding to the line data L[z, θ] at the same movement position z are arranged in one direction, the pixels P[z, θ] corresponding to the line data L[z, θ] in the same transmission direction θ are arranged in a direction perpendicular to the one direction, and each pixel value is set according to the degree of abnormality of the generated line data L[z, Θ].

According to this embodiment, it is possible to generate an image in which the state of the object to be detected can be observed from the observation result of the cross section of the biological tissue by the ultrasound.

In this embodiment, the control unit <NUM> derives line data that is composed of a plurality of brightness values arranged in the radial direction from the center of rotation for each rotation angle on the basis of the data obtained for the period for which the imaging core is rotated and moved. When the line data at the rotation angle θ of the probe <NUM> and the movement position z of the imaging core is defined as L[z, Θ], the control unit <NUM> classifies the pattern of the line data L[z, Θ], calculates the pixels P[z, θ] from the classification result, and calculates two-dimensional image data having z and θ as two axes. The control unit <NUM> displays the calculated two-dimensional image data.

According to this embodiment, the similarity between the vector for identifying the composition of the blood vessel or the stent and the vector composed of the line data is calculated for all of the lines, and a numerical value indicating the similarity is displayed as a pixel value, such as brightness, to visualize a composition pattern in the blood vessel.

As a modification example of this embodiment, the feature vector p may be a vector indicating a frequency distribution F[z, θ] of the intensity of the reflected wave in the transmission direction θ of the ultrasound which is calculated from the intensity value distribution A[z, Θ].

In Step S6 of this modification example, the control unit <NUM> of the diagnosis assistance device <NUM> calculates the similarity between a frequency distribution <NUM> of the brightness of each line and a classification pattern <NUM> which is the pattern of the frequency with respect to brightness as illustrated in <FIG>.

For example, the control unit <NUM> converts the brightness distribution <NUM> corresponding to the intensity value distribution A[<NUM>, <NUM>] indicated by the line data L[<NUM>, <NUM>] into the frequency distribution <NUM> of the brightness and calculates the similarity between the frequency distribution <NUM> and the classification pattern <NUM> of the stent which has been defined in advance. In addition, the control unit <NUM> converts the brightness distribution <NUM> corresponding to the intensity value distribution A[<NUM>, <NUM>] indicated by the line data L[<NUM>, <NUM>] into the frequency distribution <NUM> of the brightness and calculates the similarity between the frequency distribution <NUM> and the classification pattern <NUM> of the stent. Similarly, the control unit <NUM> converts the brightness distributions <NUM> corresponding to the intensity value distributions A[z, θ] indicated by other line data L[z, θ] into the frequency distributions <NUM> of the brightness and calculates the similarities between the frequency distributions <NUM> and the classification pattern <NUM> of the stent.

As a modification example of this embodiment, the feature vector p may be a vector indicating a change in the intensity value distribution A[z, θ] caused by a difference in the movement position z of the ultrasound transducer <NUM>.

In Step S6 of the modification example, the control unit <NUM> of the diagnosis assistance device <NUM> calculates the similarity between the distribution of the amount of change in the brightness of each line between the cross-sectional images <NUM> and a classification pattern which is the pattern of the amount of change in the brightness with respect to the depth.

For example, the control unit <NUM> calculates the difference between the brightness distribution <NUM> corresponding to the intensity value distribution A[<NUM>, <NUM>] indicated by the line data L[<NUM>, <NUM>] and the brightness distribution <NUM> corresponding to the intensity value distribution A[<NUM>, <NUM>] indicated by the line data L[<NUM>, <NUM>] to calculate the change amount distribution of the brightness and calculates the similarity between the change amount distribution and the classification pattern of the stent which has been defined in advance. In addition, the control unit <NUM> calculates the difference between the brightness distribution <NUM> corresponding to the intensity value distribution A[<NUM>, <NUM>] indicated by the line data L[<NUM>, <NUM>] and the brightness distribution <NUM> corresponding to the intensity value distribution A[<NUM>, <NUM>] indicated by the line data L[<NUM>, <NUM>] to calculate the change amount distribution of the brightness and calculates the similarity between the change amount distribution and the classification pattern of the stent which has been defined in advance. Similarly, the control unit <NUM> calculates the difference between the intensity value distributions A[z, θ] indicated by other line data L[z, θ] in a time direction to calculate the change amount distribution of the brightness and calculates the similarities between the change amount distributions and the classification pattern of the stent.

In this modification example, the vector indicating the change in the intensity value distribution A[z, θ] caused by the difference in the movement position z of the ultrasound transducer <NUM> is an example of a vector that is calculated using the change in the intensity value distribution A[z, θ] caused by the difference in the movement position z of the ultrasound transducer <NUM>. As another example of the vector, the feature vector p may be data from which noise has been removed by performing a filtering process as preprocessing such that the change in the intensity value distribution A[z, θ] caused by the difference in the movement position z of the ultrasound transducer <NUM> is reduced, instead of using the change in the intensity value distribution A[z, θ] caused by the difference in the movement position z of the ultrasound transducer <NUM> as it is.

As a modification example of this embodiment, the feature vector p may be a vector indicating a change in the intensity value distribution A[z, θ] caused by a difference in the transmission direction θ of the ultrasound.

In Step S6 of this modification example, the control unit <NUM> of the diagnosis assistance device <NUM> calculates the similarity between the change amount distribution of the brightness of each line between each line and an adjacent line and the classification pattern which is the pattern of the amount of change in the brightness with respect to the depth.

For example, the control unit <NUM> calculates the difference between the brightness distribution <NUM> corresponding to the intensity value distribution A[<NUM>, <NUM>] indicated by the line data L[<NUM>, <NUM>] and the brightness distribution <NUM> corresponding to the intensity value distribution A[<NUM>, <NUM>] indicated by the line data L[<NUM>, <NUM>] to calculate the change amount distribution of the brightness and calculates the similarity between the change amount distribution and the classification pattern of the stent which has been defined in advance. In addition, the control unit <NUM> calculates the difference between the brightness distribution <NUM> corresponding to the intensity value distribution A[<NUM>, <NUM>] indicated by the line data L[<NUM>, <NUM>] and the brightness distribution <NUM> corresponding to the intensity value distribution A[<NUM>, <NUM>] indicated by the line data L[<NUM>, <NUM>] to calculate the change amount distribution of the brightness and calculates the similarity between the change amount distribution and the classification pattern of the stent which has been defined in advance. Similarly, the control unit <NUM> calculates the difference between the intensity value distributions A[z, θ] indicated by other line data L[z, θ] in a spatial direction to calculate the change amount distribution of the brightness and calculates the similarities between the change amount distributions and the classification pattern of the stent.

In this modification example, the vector indicating the change in the intensity value distribution A[z, θ] caused by the difference in the transmission direction θ of the ultrasound transducer <NUM> is an example of a vector that is calculated using the change in the intensity value distribution A[z, θ] caused by the difference in the transmission direction θ of the ultrasound transducer <NUM>. As another example of the vector, the feature vector p may be data from which noise has been removed by performing a filtering process as preprocessing such that the change in the intensity value distribution A[z, θ] caused by the difference in the transmission direction θ of the ultrasound transducer <NUM> is reduced, instead of using the change in the intensity value distribution A[z, θ] caused by the difference in the transmission direction θ of the ultrasound transducer <NUM> as it is.

As described above, as a principle common to this embodiment and each modification example, the control unit <NUM> performs abnormality detection based on the distance between data items to detect, as an abnormal value, data having a behavior different from that of other data, that is, data that is not similar to other data, and expresses a difference in the distance between data with colors.

As a first step, the control unit <NUM> extracts features related to the object to be identified from the line data L[z, θ] and generates the feature vector p.

For example, there are the following methods for extracting the features from the line data L[z, θ].

As a second step, the control unit <NUM> calculates the degree of abnormality of the feature vector p with respect to the object to be identified, using the identification vector q.

For example, there are the following methods for generating the identification vector q.

In a case in which the feature vector p is similar to the identification vector q of the stent, "normal", that is, the degree of abnormality indicating the object to be identified is calculated. In a case in which the feature vector p is not similar to the identification vector q of the stent, "abnormal", that is, the degree of abnormality indicating an object other than the object to be identified is calculated.

As a third step, the control unit <NUM> assigns the degree of abnormality of the line data L[z, θ] to each pixel of the color map.

As illustrated in <FIG>, in a case in which "normal", that is, the degree of abnormality indicating the object to be identified is calculated, the pixel intensity is set to a large value, and display is highlighted. In a case in which "abnormal", that is, the degree of abnormality indicating an object other than the object to be identified is calculated, the pixel intensity is set to a small value, and display is suppressed.

The object to be identified is not limited to the stent and may be another cluster such as a guide wire, a vessel wall, a calcified lesion, or a plaque. The degrees of abnormality for a plurality of clusters may be used together.

The brightness features of an image may be used together. For example, since a line including the stent has a large number of high-brightness components, the proportion of a high-brightness region to the entire line may be used. The proportion of brightness values equal to or greater than the threshold value to the frequency distribution of the brightness of each line may also be used. The threshold value may be a fixed value, a value input by the user, or a statistical value such as a mode value, an average value, or a variance of the line data L [z, θ].

The statistics of an image may also be used. For example, since the stent and the guide wire have different amounts of attenuation from the brightness peak, the variance of the high-brightness region of the line or the slope of an approximate curve of the brightness distribution <NUM> of the line may be used. As illustrated in <FIG>, for the frequency distribution of the brightness, brightness variance in a region of interest equal to or greater than a threshold value may be used. The threshold value may be a fixed value, a value input by the user, or a value calculated from statistical characteristics such as a mode value, an average value, or a variance of the line data L[z, θ]. As illustrated in <FIG>, an approximate curve may be calculated for the brightness distribution, and the slope of a curve passing through the maximum peak may be used.

As a modification example of this embodiment, the R value of each pixel of the detection image may be set according to the variance of the high-brightness region, the G value may be set according to the degree of abnormality with respect to the stent, and the B value may be set according to the proportion of the high-brightness region.

As a modification of this embodiment, at least two of the R value, G value, and B value of each pixel of the detection image may be set according to the degree of abnormality with respect to the stent, and different feature extraction methods, such as the above-described methods (<NUM>) and (<NUM>), may be applied to each of the set values.

Claim 1:
A diagnosis assistance device (<NUM>) comprising:
a control unit (<NUM>) that generates line data indicating an intensity value of a reflected wave from a reflection object, which is present in a transmission direction of ultrasound, for each combination of a movement position (z) of an ultrasound transducer (<NUM>) that radially transmits the ultrasound while being moved inside a biological tissue and the transmission direction (θ) of the ultrasound with reference to an observation result of a cross section of the biological tissue by the ultrasound transducer (<NUM>) and generates a detection image (<NUM>) which includes pixels corresponding to the generated line data and in which the pixels corresponding to the line data at the same movement position (z) are arranged in one direction, the pixels corresponding to the line data in the same transmission direction (θ) are arranged in a direction perpendicular to the one direction, and each pixel value is set according to a degree of abnormality of the generated line data,
wherein the control unit (<NUM>) calculates a degree of abnormality of a feature vector indicating features of the line data as the degree of abnormality of the line data,
wherein the control unit generates, as the line data, data indicating an intensity value distribution of the reflected wave in the transmission direction of the ultrasound, characterized in that
the feature vector is a vector indicating a frequency distribution of intensity of the reflected wave in the transmission direction of the ultrasound which is calculated from the intensity value distribution.