JP5558727B2 - Ultrasonic diagnostic apparatus and data processing program for ultrasonic diagnostic apparatus - Google Patents

Description

  The present invention relates to an ultrasonic diagnostic apparatus that automatically traces a spectrum waveform of an ultrasonic Doppler spectrum image and a data processing program of the ultrasonic diagnostic apparatus, and in particular, automatically measures blood flow based on the traced spectrum waveform. The present invention relates to an ultrasonic diagnostic apparatus and a data processing program for the ultrasonic diagnostic apparatus.

  Conventionally, blood flow measurement of a circulatory organ has been performed with an ultrasonic diagnostic apparatus. As a blood flow measurement method using an ultrasonic diagnostic apparatus, there are a tissue Doppler imaging (TDI) method and a pulsed Doppler method (PWD) for blood flow Doppler imaging. The TDI method is also called ACM (Automated Cardiac Flow Measurement) method.

  The TDI method directly measures heart tomographic information by the color Doppler tomography. This TDI method is effective in detecting backflow because it has the advantage that the tomographic image of the heart can be displayed by correcting the location and size of the valve and that it can be displayed in color in two dimensions (2D). is there. For example, the TDI method is used to measure the back flow of a blood flow (LV inflow) flowing into the left ventricle (LV) such as a mitral valve regurgitation. However, the TDI method has a problem in accuracy because the flow velocity is measured lower than the ultrasonic Doppler method using a catheter or PWD.

  On the other hand, PWD is used for measurement of blood flow information such as maximum flow velocity in measurement of blood flow in LV Inflow, right ventricle (RV) Inflow, LV Outflow, RV Outflow, and pulmonary vein (PV). The blood flow information normally measured is blood flow diagnostic parameters such as LV Inflow E wave, A wave, deceleration time (DCT: deceleration time), PV S wave, D wave, and AR wave. Blood flow diagnostic parameters are also measured during abnormal conditions such as mitral regurgitation (MR), tricuspid regurgitation (TR), and aortic stenosis (AS). The

  A Doppler spectrum method is a method for quantitatively and accurately obtaining a blood flow velocity at an arbitrary position of a subject by PWD. In this Doppler spectrum method, ultrasonic wave transmission / reception is performed a plurality of times at regular intervals on the same part of the subject, and a Doppler signal is detected from the ultrasonic wave reflected by a moving reflector such as a blood cell. Then, a Doppler signal at a desired portion is extracted from the Doppler signal by a range gate, and Doppler spectrum image data is generated by performing a fast Fourier transform (FFT) on the extracted Doppler signal.

  Through such a procedure, Doppler spectrum image data is continuously generated from a Doppler signal obtained from a desired part of the subject, and the generated Doppler spectrum image data is sequentially arranged, whereby Doppler spectrum image data ( D mode image) can be generated. The Doppler spectrum image data is generated with frequency on the vertical axis, time on the horizontal axis, and power (intensity) of each frequency component as luminance (gradation). Then, various diagnostic parameters are measured based on the Doppler spectrum image data.

  Specifically, the position of the maximum flow velocity Vp corresponding to the maximum frequency fp of the Doppler frequency component distributed in the frequency axis direction of the Doppler spectrum image data or the average flow velocity Vm corresponding to the average frequency fm is set. A trace waveform indicating the time change of the position of the flow velocity Vm is generated. Next, in the trace waveform, a systolic waveform peak (PS: Peak Systolic) and a diastolic waveform peak (ED: End Diastolic) are detected for each heartbeat. Based on the position information of PS or ED, the heart rate (HR: Heart Rate) is measured, and further, PI (Pulsatility Index) is a diagnostic parameter of peripheral blood vessels from the trace waveform in the heart rate section set by PS or ED. ), RI (Resistance Index) or HR is measured.

  In recent years, generation of trace waveforms of maximum flow velocity Vp and average flow velocity Vm, detection of PS / ED, and measurement of diagnostic parameters such as HR, PI, RI can be automatically performed from Doppler spectrum image data displayed in real time. (For example, refer to Patent Document 1 and Patent Document 2). For example, PS / ED can be detected by generating a trigger synchronized with a trace waveform or an electrocardiogram (ECG) waveform of the maximum flow velocity Vp or the average flow velocity Vm and using the generated synchronous trigger.

  Furthermore, in order to avoid malfunctions and display of erroneous measured values in the generation process of trace waveforms of maximum flow velocity Vp and average flow velocity Vm, detection processing of PS / ED, and measurement processing of diagnostic parameters such as HR, PI, RI, etc. A technique has also been devised in which parameters used for each process can be changed. With this technology,

US Pat. No. 5,628,321 JP 2003-284718 A

  However, measurement of blood flow diagnostic parameters such as LV Inflow E-wave, A-wave, DCT, PV S-wave, D-wave, and AR wave must still be performed manually by the user referring to the Doppler spectrum image. . Specifically, after freezing the Doppler spectrum cine image data, perform a retro search of the cine image data stored in the cine memory to select the heart rate region between the EDs to be measured, and two times in the selected heart rate region. It is necessary to set the cursor and detect peak of E wave etc. and trace Dct line manually. In particular, in the examination of the circulatory organ, the time required for manual measurement of blood flow diagnostic parameters occupies most of the examination time. For this reason, shortening of the measurement time of a blood flow diagnostic parameter is desired.

  In addition, since setting criteria such as a time cursor, a speed bar, and an approximate plot are different for each user, there is a possibility that the accuracy of blood flow diagnosis parameters may vary and examination reproducibility may be impaired.

  The present invention has been made in order to cope with such a conventional situation, and is capable of acquiring diagnostic information representing blood flow from ultrasonic Doppler spectrum images in a simpler and shorter time as uniform data. It is an object to provide a data processing program for an ultrasonic diagnostic apparatus and an ultrasonic diagnostic apparatus.

Ultrasonic diagnostic apparatus according to the present invention, in order to achieve the object described above, by transmitting and receiving ultrasonic waves to a subject, extracts the Doppler signal in the designated range gate, and based on the its Doppler signal Data collection means for collecting blood flow Doppler spectrum data and collecting tissue Doppler imaging waveform data based on tissue clutter component of the Doppler signal, and a plurality of heartbeats automatically traced from the Doppler spectrum data A plurality of waveform data is generated by performing a process of cutting out and arranging the Doppler spectrum envelope waveform for each heartbeat, and representative data generating means for generating one representative waveform data from the plurality of waveform data; and the representative waveform data Measuring a first diagnostic index related to blood flow velocity based on the Doppler image Calculating a measurement means for measuring a second diagnostic indicator for tissue motion speed based on the grayed waveform data, the third diagnostic indicator of on the basis of the first diagnostic index and the second diagnostic indicator as bloodstream diagnostic information and blood flow diagnostic information calculation means for, those equipped with.

In addition, the data processing program of the ultrasonic diagnostic apparatus according to the present invention extracts a Doppler signal in a designated range gate by transmitting and receiving an ultrasonic wave to a subject in order to achieve the above-described object. Te, the collecting Doppler spectrum data of blood flow based on the its Doppler signal, the data collecting means for collecting Doppler imaging waveform data organization based on clutter components of the tissue of the Doppler signal, from the Doppler spectrum data Generate representative waveform data by generating multiple waveform data by performing processing to cut out and arrange Doppler spectrum envelope waveforms for multiple heartbeats automatically traced for each heartbeat, and generate one representative waveform data from the plurality of waveform data Means for blood flow velocity based on the representative waveform data Measuring means for measuring a second diagnostic index related to tissue motion speed based on the Doppler imaging waveform data, and a third based on the first diagnostic index and the second diagnostic index the diagnostic of that they appear as a blood flow diagnostic information calculation means for calculating a blood flow diagnostic information.

  In the ultrasonic diagnostic apparatus and the data processing program of the ultrasonic diagnostic apparatus according to the present invention, the diagnostic information representing the blood flow can be acquired as simple data in a short time from the ultrasonic Doppler spectrum image. .

1 is a configuration diagram showing an embodiment of an ultrasonic diagnostic apparatus according to the present invention. The block diagram which shows the detailed function of the parameter measurement part shown in FIG. The flowchart which shows the flow at the time of collecting Doppler spectrum data by the ultrasonic diagnosing device shown in FIG. 1, and automatically measuring and displaying a diagnostic parameter based on the collected Doppler spectrum data. The figure which shows the example of the input data used as the object of the CAB process in the parameter measurement part shown in FIG. The figure which shows the example of the output data of the CAB process in the CAB process part shown in FIG. The figure which shows an example of several waveform data for every heartbeat after the CAB process used as the object of an averaging process and modeling. The figure which shows the example which displayed in parallel the Doppler spectrum data, the waveform data for every heartbeat, and the representative waveform data after the averaging process. The figure which shows the example which displayed in parallel the representative waveform data after the averaging process in time series. The figure which shows the example of the threshold value set as a rejection condition of waveform data. The figure which shows the example which identified and displayed the candidate of the waveform data which should be rejected. The figure which shows the effect by the rejection process of the waveform data which has a singular value. The figure which shows the example which measured the E wave, A wave, and DCT of LV Inflow based on the representative waveform data. The figure which shows the example which measured the S wave, D wave, and AR wave of PV Inflow based on the representative waveform data. The figure explaining the method to measure the diagnostic parameter E / e 'based on the representative waveform data and the velocity data corresponding to the clutter component of the mitral valve. The figure which shows the example which displayed the peak [Ve] max of the representative waveform data Ve automatically measured by the method shown in FIG. 14, and the peak [Vma] max of the TDI WAVE Vma of the mitral valve. The figure which shows the example which superimposed and displayed the representative waveform data showing the maximum flow velocity Vp of LV inflow, and TDI WAVE of a mitral valve using a common speed range. The maximum frequency fp of LV inflow and TDI of the mitral valve extracted with different filtering from representative waveform data representing the maximum frequency fp of LV inflow and TDI WAVE representing the speed of the mitral valve The figure which shows the example which measured diagnostic parameter E / e 'from WAVE. The figure which shows the definition of diagnostic parameter Tei-Index.

  Embodiments of the present invention will be described with reference to the accompanying drawings.

(Configuration and function)
FIG. 1 is a block diagram showing an embodiment of an ultrasonic diagnostic apparatus according to the present invention.

  The ultrasonic diagnostic apparatus 1 includes an ultrasonic probe 2, a reference signal generation unit 3, a transmission / reception unit 4, a data generation unit 5, a data storage unit 6, a blood flow evaluation unit 7, a display unit 8, an ECG unit 9, and a PCG (phono cardiogram ) Unit 10 is provided. The data generation unit 5 includes a B-mode data generation unit 11, a Doppler signal detection unit 12, a color Doppler data generation unit 13, and a spectrum data generation unit 14. The blood flow evaluation unit 7 includes a trace waveform generation unit 15, a PS An / ED detection unit 16 and a parameter measurement unit 17 are provided.

  In addition, the data generation part 5, the data storage part 6, and the blood flow evaluation part 7 can also be constructed by reading a data processing program into a computer in addition to being constructed by a circuit.

  The ultrasonic probe 2 includes a plurality of piezoelectric vibrators, and converts an electrical signal received from the transmission / reception unit 4 into an ultrasonic pulse and transmits the ultrasonic pulse to the subject, while receiving an ultrasonic reflection signal generated inside the subject. It has a function of converting it into an electrical signal and giving it to the transmission / reception unit 4 as received data.

  The reference signal generation unit 3 has a function of controlling the transmission / reception unit 4 by generating a reference signal for defining conditions such as timing of ultrasonic waves transmitted from the ultrasonic probe 2 and supplying the reference signal to the transmission / reception unit 4.

  The transmission / reception unit 4 has a function of transmitting an ultrasonic wave from the ultrasonic probe 2 by giving an electric signal to the ultrasonic probe 2 in accordance with the reference signal received from the reference signal generation unit 3, and received data received by the ultrasonic probe 2. And has a function of giving it to the data generation unit 5.

  The B mode data generation unit 11 performs various processes such as logarithmic conversion processing, envelope detection processing, threshold processing, and A / D (analog to digital) conversion processing on the received data received from the transmission / reception unit 4 to perform the B mode. It has a function of generating image data and a function of supplying the generated B-mode image data to the data storage unit 6.

  The Doppler signal detection unit 12 has a function of detecting a Doppler signal by performing quadrature phase detection on the received data received from the transmission / reception unit 4, and a color Doppler data generation unit 13 and a spectrum data generation unit 14 that detect the detected Doppler signal. It has a function to give to.

  The color Doppler data generation unit 13 extracts a blood flow component by removing the clutter component from the Doppler signal received from the Doppler signal detection unit 12, and based on the autocorrelation value of the blood flow component, A function of generating color Doppler data such as a variance value and a function of supplying the generated color Doppler data to the data storage unit 6 are provided. When performing TDI, the color Doppler data generation unit 13 extracts a clutter component of a tissue such as a mitral valve from the Doppler signal, and generates tissue Doppler data such as tissue movement speed data based on the clutter component. Configured to do.

  The spectrum data generation unit 14 performs a FFT analysis on the Doppler signal received from the Doppler signal detection unit 12 to generate spectrum data indicating temporal changes in blood flow velocity or tissue velocity, and the generated spectrum data. It has a function to be given to the data storage unit 6.

  The trace waveform generation unit 15 extracts spectrum cine image data in a desired period from the data storage unit 6, and sets the maximum frequency fp (or maximum flow velocity Vp) and average frequency fm (or average flow velocity Vm) according to control parameters such as threshold values. By calculating, a function of automatically generating a Doppler spectrum envelope waveform showing a time change of the maximum frequency fp and average frequency fm for a predetermined period as auto trace waveform data, and giving the generated trace waveform data to the data storage unit 6 It has a function. Further, the trace waveform generation unit 15 has a function of detecting a heartbeat cycle based on trace waveform data, an ECG signal acquired from the ECG unit 9 or a PCG signal acquired from the PCG unit 10. Therefore, the trace waveform generation unit 15 can automatically generate trace waveform data for a plurality of heartbeats.

  The PS / ED detection unit 16 acquires the trace waveform data generated by the trace waveform generation unit 15 and performs a known process such as a statistical process, thereby performing a PS indicating the systole of the heart and an ED indicating the diastole. It has a function to detect the position. When setting of PS and ED is difficult only from the trace waveform data, the ECG signal or PCG signal acquired from the ECG unit 9 or the PCG unit 10 is used.

  The maximum frequency (maximum flow velocity), the average frequency (average flow velocity) tracing method, and the PS / ED peak position detection method are arbitrary. For detailed examples, see, for example, Japanese Patent Application Laid-Open No. 2003-284718 and US Pat. No. 628,321.

  The parameter measurement unit 17 has a function of acquiring the PS and ED position information in the desired period detected by the PS / ED detection unit 16 and the trace waveform data in the desired period created in the trace waveform generation unit 15. A function that acquires multiple waveform data for each heartbeat as blood flow diagnosis data by performing CAB (Cut and Align Beats) processing, which will be described later, on the trace waveform data during the period, averaging of multiple waveform data after CAB processing A function to obtain representative waveform data by performing modeling processing using multiple waveform data after processing and / or CAB processing, LV Inflow E wave, A wave, DCT, PV S wave based on representative waveform data A function of measuring diagnostic parameters such as D wave and AR wave, and a function of displaying blood flow diagnostic information such as blood flow diagnostic data and diagnostic parameters obtained on the display unit 8

  The ECG unit 9 has a function of measuring the ECG waveform of the subject and supplying the obtained ECG waveform to the trace waveform generation unit 15 and the PS / ED detection unit 16. The PCG unit 10 has a function of measuring a heart sound waveform (PCG waveform) of a subject and giving the obtained PCG waveform to the trace waveform generation unit 15 and the PS / ED detection unit 16.

As a result, the data storage unit 6, B-mode image data, color Doppler data, spectrum data, trace waveform, the position information and E-wave of the PS and ED, A wave, DCT, S-wave, D wave, the AR wave like diagnostic parameters are stored.

  The display unit 8 has a function of reading and displaying desired information among the information stored in the data storage unit 6.

  FIG. 2 is a block diagram showing detailed functions of the parameter measurement unit 17 shown in FIG.

  The parameter measurement unit 17 includes a CAB processing unit 20, a CAB condition setting unit 21, a range setting unit 22, an averaging processing unit 23, an averaging condition setting unit 24, a mathematical modeling unit 25, a modeling condition setting unit 26, and parameter calculation. Unit 27, data reject unit 28, reject data selection unit 29, reject condition setting unit 30, display processing unit 31, and input device 32.

  The CAB processing unit 20 has a function of creating a plurality of waveform data for each heartbeat by performing CAB processing on the trace waveform data in a desired period. The CAB process is a process of cutting out and arranging trace waveform data for each heartbeat. Therefore, three-dimensional data having a time axis, an axis representing the size (speed or frequency) of processing target data, and a heartbeat axis is obtained by CAB processing. However, the time in a plurality of waveform data for each heartbeat can be normalized using a period of one heartbeat as necessary. Further, the CAB processing unit 20 is configured to acquire an ECG signal or a PCG signal from the ECG unit 9 or the PCG unit 10 as necessary in order to determine the extraction timing of the waveform data in the CAB processing.

  The CAB condition setting unit 21 has a function of setting a CAB processing condition according to information from the input device 32 and giving the set CAB condition to the CAB processing unit 20. For example, the extraction condition (CabTrig) that determines whether the waveform data extraction timing is determined based on the synchronization signal of the ECG signal, PCG signal, or trace waveform, and between the reference position on the synchronization signal and the waveform data extraction timing Whether to set an offset time (CabOffset) and whether to normalize time in a plurality of waveform data for each heartbeat (CabType) can be set as CAB processing conditions.

  The range setting unit 22 automatically or manually sets the coordinate system such as the frequency range (or speed range) and origin position of the input / output data of the CAB process, and performs the CAB process on the set range information and coordinate system information. A function to be given to the unit 20. When automatically setting the frequency range and coordinate system, the frequency range and coordinate system can be optimized using the histogram of the trace waveform data of the maximum frequency fp and the average frequency fm acquired from the data storage unit 6. . Further, when manually setting the frequency range and the coordinate system, setting information of the frequency range and the coordinate system can be input to the range setting unit 22 from the input device 32.

  The averaging processing unit 23 has a function of obtaining one representative waveform data by performing averaging processing of a plurality of waveform data after CAB processing. The averaging processing unit 23 is configured to exclude the waveform data notified from the data rejecting unit 28 from the target of the averaging process. The averaging condition setting unit 24 has a function of setting conditions for averaging processing according to the setting information from the input device 32 and supplying the set averaging processing condition information to the averaging processing unit 23.

  The mathematical modeling unit 25 has a function of obtaining one representative waveform data by a modeling process using a plurality of waveform data after the CAB process and a mathematical model. The mathematical modeling unit 25 is configured to exclude the waveform data notified from the data reject unit 28 from the modeling target. The modeling condition setting unit 26 has a function of setting modeling conditions according to the setting information from the input device 32 and providing the set modeling condition information to the mathematical modeling unit 25.

  The data reject unit 28 automatically or manually determines the waveform data to be excluded from the averaging processing in the averaging processing unit 23 and / or the modeling in the mathematical modeling unit 25, and averages the information of the determined waveform data A function of notifying the conversion processing unit 23 and / or the mathematical modeling unit 25. The waveform data to be rejected should be determined based on the amount of characteristic such as the PS position of the waveform data after CAB processing, the heart rate period, the amount of deviation from the representative waveform data created by averaging processing or modeling Can do. For this reason, the data reject unit 28 sets the waveform data feature amount acquired from the CAB processing unit 20 or the divergence amount of the waveform data from the representative waveform data acquired from the averaging processing unit 23 or the mathematical modeling unit 25 to a constant value. An error detection process is performed to determine whether or not the error is exceeded, and the result of error detection can be displayed on the display unit 8 through the display processing unit 31.

  The reject data selection unit 29 has a function of selecting waveform data to be excluded from the averaging process and / or modeling in accordance with information from the input device 32, and a function of notifying the data reject unit 28 of waveform data selection information. . The reject condition setting unit 30 sets a reject condition such as a threshold for automatically determining waveform data to be excluded from the averaging process and / or modeling in accordance with information from the input device 32, and the set waveform It has a function of notifying the data rejection unit 28 of data rejection conditions.

  The parameter calculation unit 27 is based on the representative waveform data acquired from the averaging processing unit 23 and / or the mathematical modeling unit 25, and the LV Inflow E wave, A wave, DCT, PV S wave, D wave, AR wave, etc. It has a function to measure the diagnostic parameters.

  The display processing unit 31 has a function of causing the display unit 8 to output desired data generated in each component of the parameter measurement unit 17 and an input through an image displayed on the display unit 8 by GUI (Graphical User Interface) technology. Display processing for displaying electronic keys on the display unit 8 so that various instruction information can be input to the parameter measurement unit 17 by operating the device 32, and thumbnail display on the display unit 8 so that specific data can be selected. A display process, and a display process such as a display process for identifying or extracting and displaying specific data.

  The input device 32 is a device such as a mouse, a hard key, or a soft key for inputting necessary information to the parameter measurement unit 17 by GUI technology.

(Operation and action)
Next, the operation and action of the ultrasonic diagnostic apparatus 1 will be described.

  FIG. 3 is a flowchart showing a flow when Doppler spectrum data is collected by the ultrasonic diagnostic apparatus 1 shown in FIG. 1, and diagnostic parameters are automatically measured and displayed based on the collected Doppler spectrum data.

  First, in step S1, time-series Doppler spectrum data is sequentially collected together with B-mode image data and color Doppler data by cine imaging.

  That is, the reference signal generated in the reference signal generator 3 is given to the transmitter / receiver 4, and the electrical signal is given to the ultrasonic probe 2 from the transmitter / receiver 4 according to the reference signal. For this reason, an ultrasonic wave is transmitted from the ultrasonic probe 2 to the subject, and an ultrasonic reflection signal generated inside the subject is received by the ultrasonic probe 2 and converted into an electric signal as the transmission / reception unit 4. Given to. The received data is given from the transmission / reception unit 4 to the B-mode data generation unit 11 and the Doppler signal detection unit 12. The B-mode data generation unit 11 generates B-mode image data, while the Doppler signal detection unit 12 generates a Doppler signal. Detected and provided to the color Doppler data generation unit 13 and the spectrum data generation unit 14.

  The color Doppler data generation unit 13 extracts a blood flow component from the Doppler signal to generate color Doppler data, and the spectrum data generation unit 14 generates spectrum data. The B-mode image data, color Doppler data, and spectrum data are given to the data storage unit 6 and stored.

  Next, in step S2, the trace waveform generation unit 15 automatically creates trace waveform data indicating time changes for a plurality of heartbeats of the maximum frequency fp and the average frequency fm from the spectrum data. At this time, the reference wave of the ECG signal from the ECG unit 9 or the reference wave of the PCG signal from the PCG unit 10 is used as a heartbeat synchronization trigger as necessary. However, a heartbeat synchronization trigger can be created based on a change in the value of the trace waveform data itself.

  Next, in step S3, the PS / ED detector 16 detects the position of the PS indicating the myocardial systole and the ED indicating the diastole based on the trace waveform data. At this time, the reference wave of the ECG signal from the ECG unit 9 or the reference wave of the PCG signal from the PCG unit 10 is used as necessary.

  Next, in step S4, the CAB processing unit 20 of the parameter measurement unit 17 performs CAB processing on the trace waveform data. Thereby, a plurality of waveform data for each heartbeat is created as blood flow diagnosis data.

  FIG. 4 is a diagram showing an example of input data to be subjected to CAB processing in the parameter measurement unit 17 shown in FIG.

  4A and 4B, the horizontal axis indicates time, and the vertical axis indicates frequency. 4A shows the trace waveform of the maximum frequency fp and average frequency fm superimposed on the LV Inflow Doppler spectrum data, and FIG. 4B shows the waveform superimposed on the carotid Doppler spectrum data. The trace waveform of the displayed maximum frequency fp and average frequency fm is shown.

  The trace waveform is automatically detected from the Doppler spectrum data as shown by the curves shown in FIGS. Further, as indicated by the marker, PS and ED are automatically detected from the trace waveform. The trace waveform, PS and ED detected in this way can be used as input data for CAB processing.

  In FIG. 4, the vertical axis of the Doppler spectrum data is the frequency, but a corresponding speed may be used. Also in the following figures, the vertical axis of spectrum data or waveform data may use either a frequency scale or a speed scale.

  FIG. 5 is a diagram illustrating an example of output data of the CAB process in the CAB processing unit 20 illustrated in FIG.

  In FIG. 5A, the horizontal axis indicates time, and the vertical axis indicates frequency. In FIG. 5 (B), the right axis indicates time, the left axis indicates heartbeat, and the upward axis indicates maximum frequency fp. Further, in FIG. 5C, the right axis indicates time, the left axis indicates heartbeat, and the upward axis indicates average frequency fm.

  FIG. 5A shows trace waveforms of the maximum frequency fp and the average frequency fm for 18 heartbeats automatically detected on the Doppler spectrum data of the carotid artery. A trace waveform can be cut out for each heart beat (HB) using each ED position as a cut-out timing on the trace waveform of such a predetermined heartbeat period. Then, when the extracted waveform data of the maximum frequency fp are arranged in the direction of the heartbeat axis, CAB processing data is obtained as array information of a three-dimensional waveform as shown in FIG. Similarly, when the extracted waveform data of the average frequency fm are arranged in the direction of the heartbeat axis, CAB processing data as shown in FIG. 5C is obtained.

  The CAB process data as shown in FIGS. 5B and 5C can be automatically created and stored in the data storage unit 6 as blood flow diagnosis data or displayed on the display unit 8.

  Note that the waveform data cut-out timing can be determined not only based on the ED position, but also based on the sync signal such as PS, ECG signal, PCG signal, etc., or automatically from the manual or reference position based on the trace waveform itself It can also be determined.

  In addition, a reference wave such as an R wave of the PS, ED, or ECG signal, a reference wave such as an S1 wave or S2 wave of the PCG signal, or a reference position that is automatically or manually set on the trace waveform is used as a trigger. It is also possible to cut out waveform data at a timing before or after the offset time has elapsed. For example, if an offset is set as a margin immediately before the PS using the PS as a trigger, the start position of the extracted waveform data does not become the PS, so that it is possible to observe the PS on each of a plurality of extracted waveform data. . On the other hand, if the minimum point on the trace waveform immediately before the PS position is used as a trigger, it is possible to observe PS on each of a plurality of waveform data even if the offset value is zero.

  Further, the time unit can be normalized by the period of one heartbeat and expressed in% without using the SI unit system (International System of Units). Therefore, when the unit in the time direction is normalized, the offset time from the trigger to the extraction timing is also a normalized value.

  The CAB processing conditions as described above can be set in advance in the CAB condition setting unit 21 by operating the input device 32 separately in step S5 using parameters. For example, ECG, PCG, PS, ED, ECG, PCG, PS, ED, and the extraction condition parameter CabTrig that determines whether the waveform data extraction timing is determined based on the synchronization signal at the reference position on the ECG signal, PCG signal, PS, ED, or trace waveform. You can select from WAVEFORM. Similarly, second or% can be selected as a parameter CabType indicating whether or not to normalize time in a plurality of waveform data for each heartbeat. Also, a desired numerical value can be set as the parameter CabOffset of the offset time between the reference position on the synchronization signal and the waveform data cut-out timing. When the time of the waveform data for each heartbeat is not normalized, the unit of the parameter CabOffset is ms, and when the time of the waveform data for each heartbeat is normalized, the unit of the parameter CabOffset is%.

  The CAB processing conditions set in this way are given from the CAB condition setting unit 21 to the CAB processing unit 20, and the CAB processing unit 20 executes the CAB processing according to the CAB processing conditions.

  It should be noted that the coordinate system such as the frequency range (or speed range) and origin position of the trace waveform to be subjected to the CAB process is optimized by the range setting unit 22 prior to the CAB process. The optimization of the coordinate system can be automatically performed in the range setting unit 22 based on the histogram of the trace waveform data of the maximum frequency fp and the average frequency fm to be subjected to CAB processing. In addition, the coordinate system can be manually set in the range setting unit 22 by operating the input device 32.

  Next, in step S6, one or both of averaging processing of a plurality of waveform data for each heartbeat and modeling using a mathematical model of the plurality of waveform data for each heartbeat are executed. Thereby, one stable representative waveform data can be obtained.

  FIG. 6 is a diagram illustrating an example of a plurality of waveform data for each heartbeat after the CAB process that is the target of the averaging process and modeling.

  In FIG. 6, the horizontal axis indicates time and the vertical axis indicates frequency. As shown in FIG. 6, a plurality of waveform data of maximum frequency fp or average frequency fm corresponding to different heartbeats can be obtained by CAB processing. An averaging process and / or modeling can be performed on the plurality of waveform data.

  First, the case where the averaging process is performed will be described.

The averaging process is performed in the averaging processing unit 23. Expression (1) is an expression indicating an averaging process for a plurality of waveform data.

  In Equation (1), n is a discrete time, k is a heartbeat, N is the number of heartbeats (that is, waveform data) used for averaging, and CAB (n, k) is a heartbeat k and time n extracted by CAB processing. The array data x (n) is averaged representative waveform data. Conditions for averaging processing such as the number N of heartbeats used for averaging can be set as parameters in the averaging condition setting unit 24 based on information from the input device 32. In order to improve accuracy, averaging processing may be performed using a plurality of waveform data after the elapse of a predetermined heartbeat in which detection of a trigger for heartbeat synchronization such as ED is stabilized. The number of heartbeats can also be set as a condition for the averaging process. The set averaging process condition is given from the averaging condition setting unit 24 to the averaging processing unit 23.

  The averaged representative waveform data can be displayed on the display unit 8 together with the Doppler spectrum data, the trace waveform, and the plurality of waveform data after CAB processing. In this case, the Doppler spectrum data, the trace waveform, and the plurality of waveform data after CAB processing are output to the display unit 8 through the display processing unit 31. Further, the averaging processing unit 23 provides the display processing unit 31 with additional information such as representative waveform data and identification information of the waveform data used to create the representative waveform data. The representative waveform data and the plurality of waveform data can be displayed by various display methods by the display processing in the display processing unit 31.

  FIG. 7 is a diagram illustrating an example in which Doppler spectrum data, waveform data for each heartbeat, and representative waveform data after averaging processing are displayed in parallel.

  In FIG. 7A, the horizontal axis indicates time, and the vertical axis indicates frequency. In FIG. 7B, the horizontal axis represents the heartbeat direction, the vertical axis represents the update direction, the horizontal axis of each graph represents time, and the vertical axis of each graph represents the frequency value of the waveform data.

  FIG. 7A shows a trace waveform of the maximum frequency fp and the average frequency fm automatically traced on the Doppler spectrum data. By such CAB processing for the trace waveform, waveform data for each heartbeat as shown in FIG. 7B is sequentially obtained. In other words, as shown in FIG. 7B, a plurality of waveform data obtained by CAB processing can be displayed as a plurality of two-dimensional data for each heartbeat.

  Furthermore, by averaging the waveform data for each of a plurality of heartbeats, averaged representative waveform data is obtained as shown at the right end of FIG. 7B. In the example of FIG. 7B, the representative waveform data and the waveform data corresponding to the latest heartbeat are superimposed and displayed. Then, a plurality of waveform data and representative waveform data can be displayed in parallel with the Doppler spectrum data on which the trace waveform is superimposed and displayed as shown in FIG. However, a plurality of waveform data and representative waveform data may be switched and displayed in different windows.

  Such extraction of waveform data for each heartbeat and calculation of representative waveform data can be automatically performed in real time at a desired timing such as end diastole or PS. In this case, a plurality of waveform data and representative waveform data for each heartbeat in time series are sequentially updated at a desired timing such as the end diastole. Thus, if diagnostic information such as representative waveform data is automatically updated and displayed in real time, the diagnostic throughput of the circulatory organ or the like can be greatly improved.

  On the other hand, extraction of waveform data for each heartbeat and calculation of representative waveform data are performed during playback of cine image data after freeze of Doppler spectrum data generation operation, as well as trace waveform extraction and PS and ED detection. be able to.

  When representative waveform data is calculated by averaging processing in real time or after freezing, a plurality of waveform data used in the averaging processing can be identified and displayed. In the example of FIG. 7B, the waveform data corresponding to four consecutive heartbeats used for the moving average is highlighted. The highlight display can be performed, for example, by changing elements such as the display color, luminance, and line thickness of specific waveform data. Therefore, the user can easily visually recognize from which waveform data the representative waveform data is obtained.

  Similarly, a trace waveform portion on Doppler spectrum data corresponding to a plurality of waveform data used in the averaging process and a trace waveform portion corresponding to the latest heartbeat can be highlighted. Thereby, it becomes easy to associate which heartbeat region is the measurement target on the Doppler spectrum data, and the operability can be improved.

  When a plurality of waveform data is displayed in a sub-window different from the window for Doppler spectrum data, the sweep speed of Doppler spectrum data and the update speed of the plurality of waveform data can be set independently. For example, when the sweep speed of Doppler spectrum data is high, only Doppler spectrum data of about 1 to 2 heartbeats may be displayed on the display unit 8. Therefore, if the update speed of multiple waveform data is set to several tens of seconds without depending on the sweep speed of the Doppler spectrum data, the representative waveform highlighted in the sub window even if the sweep speed of the Doppler spectrum data is high. It becomes possible to visually recognize a range of a plurality of waveform data used for creating data. For this reason, it leads to the improvement of operativity.

  In addition, immediately after freeze of the Doppler spectrum data collection operation, the cine image data recorded in the data storage unit 6 is retrospectively read and the Doppler spectrum data is automatically scrolled back to the desired time phase such as the nearest ED or end diastole. Can be made. Therefore, it is possible to automatically set the measurement cursor indicating the measurement range of the representative waveform data on the trace waveform of a desired time phase and measure the representative waveform data.

  By automatically setting the measurement range of the representative waveform data, the representative waveform data corresponding to the latest time phase is always created in a fixed manner without manual setting of the measurement cursor by the user when collecting Doppler spectrum data. Can be displayed. Such automatic setting of the measurement cursor that defines the range of the averaging process can also be set in advance as a condition for the averaging process.

  FIG. 8 is a diagram illustrating an example in which the representative waveform data after the averaging process is displayed in parallel in time series.

  In each graph of FIG. 8, the horizontal axis indicates time, and the vertical axis indicates frequency. Each graph shown in FIG. 8 is a diagram comparing representative waveform data obtained by averaging using waveform data for the latest four heartbeats and the latest instantaneous waveform data. Are lined up. As shown in FIG. 8, it is possible to easily confirm the shift amount from the average value of the instantaneous waveform data by comparing the representative waveform data and the instantaneous waveform data by the averaging process.

  Next, modeling based on a plurality of waveform data obtained by CAB processing will be described.

  Modeling is performed in the mathematical modeling unit 25. Modeling can be performed by a general method such as Burg (MEM) method, geometric lattice method, Yule-Walker method, and modified covariance processing method. Specific examples of mathematical models used for modeling include AR (auto regressive) models, ARX (auto regressive exogenous) models, FIR (finite impulse response) models, ARMAX (auto regressive moving average exogenous) models, ARARX models, and ARARMAX models. , OE (output error) model, BJ (Box and Jenkins) model, and other parametric models.

Modeling calculation processing using the AR model is expressed as shown in Equation (2).

  In Equation (2), n is a discrete time, x (n) is representative waveform data, u (n) is a residual, αi is a coefficient series of an AR model, and p is a model order. The observation time of x (n) can be set as a parameter for AR modeling conditions. The modeling condition can be set as a parameter in the modeling condition setting unit 26 based on information from the input device 32. The set modeling conditions are given from the modeling condition setting unit 26 to the mathematical modeling unit 25.

  When modeling a plurality of waveform data using a mathematical model, representative waveform data simplified as a mathematical expression using a smaller number of parameters can be obtained. For this reason, it is also possible to predict the representative waveform data using mathematical expressions. The representative waveform data created by modeling can be displayed on the display unit 8 through the display processing unit 31 as diagnostic information.

  By the way, the waveform data that takes a singular value such that the feature amount representing the waveform such as the heartbeat period and the shift amount from the averaged or modeled representative waveform data exceeds a certain value is used to create representative waveform data. It is desirable to exclude from the averaging process and modeling from the viewpoint of improving the accuracy of the representative waveform data. Therefore, in the data reject unit 28, an averaging process and / or a rejection process from modeling of abnormal waveform data is executed as necessary. This rejection process can be performed automatically or manually.

  When performing the reject process, in step S7, a reject condition such as a threshold for determining waveform data to be excluded is set by the reject condition setting unit 30. A threshold value as a rejection condition is set for a measurement parameter for identifying waveform data having a singular value.

  As described above, the measurement parameter indicates whether or not the waveform data such as the PS position, the HR value, the shift amount from the averaged representative waveform data, and the shift amount from the modeled representative waveform data have a singular value. It can be any parameter that can be determined. Therefore, the measurement parameter can also be defined as a parameter for evaluating a deviation amount from a reference value such as PRD (Percent Root Mean Square Difference) or residual sum of squares.

  FIG. 9 is a diagram showing an example of threshold values set as waveform data rejection conditions.

  In FIG. 9, the horizontal axis indicates the value of the measurement parameter, and the vertical axis indicates the frequency. When the values of the measurement parameters are plotted, a distribution represented by a probability density function as shown in FIG. 9 is generally obtained. Therefore, as shown in FIG. 9, the threshold for the measurement parameter can be variably set as a reject condition. For example, when it is determined that the waveform data value corresponding to the measurement parameter PRD exceeds 10% is an abnormal value or a singular value, PRD = 10% may be set as the threshold value.

  However, from the viewpoint of simplification of processing, a threshold for the feature value itself such as the PS position may be set as a rejection condition without performing statistical processing.

  The set reject condition is notified from the reject condition setting unit 30 to the data reject unit 28.

  Next, in step S8, the waveform data to be excluded from the averaging process and / or modeling is determined by performing error detection processing in accordance with the rejection condition in the data rejection unit 28.

  When the reject process is automatically performed, waveform data having a singular value to be excluded is automatically detected by an error detection process using a threshold value. That is, when a measurement parameter such as a PS position or HR representing the characteristics of waveform data exceeds a threshold value, it is automatically detected as waveform data to be excluded. If representative waveform data obtained by modeling is used, unsteady waveform data far from the mathematical model can be automatically detected by evaluating the residual of the mathematical expression.

  When the reject process is automatically performed, the reject process may be performed before the averaging process and modeling in step S6.

  On the other hand, when the reject process is performed manually, candidates for waveform data having singular values to be excluded are automatically detected by an error detection process using a threshold value. Also in this case, the feature amount of the waveform data can be obtained using the representative waveform data. However, when representative waveform data is created in real time, the reject process is automatically performed. In the case where the rejection process is manually performed after freezing, when the waveform data candidates to be rejected are detected by the data reject unit 28, the detected waveform data candidates are displayed on the display unit 8.

  FIG. 10 is a diagram showing an example of identifying and displaying candidate waveform data to be rejected.

  FIG. 10A shows a trace waveform of the maximum frequency fp and the average frequency fm automatically traced on the Doppler spectrum data. That is, in FIG. 10A, the vertical axis represents frequency and the horizontal axis represents time. FIG. 10B shows an example in which a plurality of waveform data obtained by CAB processing is displayed as thumbnails as reduced images for preview.

  As shown in FIG. 10B, the candidates of waveform data to be rejected that have been error-detected by the threshold processing can be identified and displayed. For example, in the example of FIG. 10B, when averaging processing is performed using waveform data of 10 heartbeats immediately before freezing, unstable waveform data of 1, 2, 4, and 6 heartbeats are rejected as reject candidates. Light is displayed. The user can manually select the waveform data to be rejected from the identified waveform data candidates while referring to the waveform data displayed as thumbnails together with the Doppler spectrum data and the trace waveform.

  Specifically, waveform data selection information to be rejected is input to the reject data selection unit 29 by operating the input device 32. When the waveform data to be rejected is selected, the waveform data selection information is notified from the reject data selection unit 29 to the data reject unit 28. In the data reject unit 28, the waveform data corresponding to the selection information is determined as the waveform data to be excluded.

  When the waveform data to be excluded is determined, identification information of the waveform data to be excluded is notified from the data reject unit 28 to the averaging processing unit 23 and / or the mathematical modeling unit 25. Then, the averaging process and / or the modeling process are executed again, excluding the waveform data notified from the data reject unit 28 in step S6. Thereby, the representative waveform data is recalculated.

  FIG. 11 is a diagram illustrating the effect of reject processing of waveform data having a singular value.

  11A and 11B, the horizontal axis indicates time, and the vertical axis indicates frequency. FIG. 11A shows representative waveform data obtained by averaging waveform data for 10 heartbeats, and FIG. 11B rejects waveform data for 4 unstable heartbeats and performs averaging processing. The representative waveform data obtained by performing is shown.

  It can be confirmed that more stable representative waveform data can be obtained by rejecting unstable waveform data and performing an averaging process as shown in FIG.

  Then, the representative wave meter data that is recalculated by rejecting the waveform data is displayed on the display unit 8. When the reject process is automatically performed and the averaging process or the modeling process is executed, the excluded waveform data can be identified and displayed. Thereby, the user can easily confirm the waveform data excluded from the averaging process or the modeling process.

  Further, when the waveform data used in the averaging process is identified and displayed at an arbitrary update rate using the sub-window, the identification display on the sub-window can be changed in conjunction with the rejected waveform data. For example, the highlighted display of the rejected waveform data is turned off.

  Further, the representative waveform data that is recalculated by rejecting the waveform data is displayed on the display unit 8, and the decision instruction information of the representative waveform data is operated by the operation of the input device 32 and the averaging processing unit 23 and / or the mathematical modeling unit 25. Until it is inputted to the input, the selection of the waveform data to be rejected in step S8 and the recalculation of the representative waveform data in step S6 may be repeated. In this case, the measurement of the representative waveform data is completed by inputting the confirmation instruction information of the representative waveform data to the averaging processing unit 23 and / or the mathematical modeling unit 25 by operating the input device 32.

  With such a waveform data reject function, the user can interactively select or reject the waveform data used to create the representative waveform data, thereby improving operability.

  When the representative waveform data is determined, in step S9, the parameter calculation unit 27 performs automatic measurement of diagnostic parameters related to blood flow such as LV Inflow E wave based on the representative waveform data.

  FIG. 12 is a diagram illustrating an example in which LV Inflow E wave, A wave, and DCT are measured based on representative waveform data.

  In FIG. 12, the horizontal axis represents time, ECG represents an ECG signal, PCG represents a PCG signal, ED TRIGGER automatically detected ED as a trigger from the trace waveform, and ED / PS PAIR automatically detected sequentially from the trace waveform. A pair of ED and PS, VELOCITY OF LV INFLOW, respectively, represents representative waveform data representing the speed of LV Inflow.

  By searching the ED and PS pairs in the time direction from any trigger such as R wave of ECG signal, S1 wave or S2 wave of PCG signal, ED, PS, and manually set reference position as shown in FIG. E-wave and A-wave coordinates and data values and DCT can be measured.

  In addition, it is also possible to measure the coordinates and data values of the S wave, D wave, and AR wave from the representative waveform data representing the PV inflow speed using an arbitrary trigger in the same manner.

  FIG. 13 is a diagram illustrating an example in which the PV Inflow S wave, D wave, and AR wave are measured based on the representative waveform data.

  The upper right image in FIG. 13 is a color Doppler tomographic image in which the scanning range, the scanning direction, and the range gate are superimposed and displayed. In the lower image of FIG. 13, the horizontal axis indicates time, and the vertical axis indicates relative speed (relative frequency).

  The lower image in FIG. 13 shows that the S wave peak position and peak value are automatically measured by selecting the R wave of the ECG signal as a trigger from the representative waveform data representing the maximum speed of PV inflow, and the measured S wave is Doppler. It is the figure superimposed and displayed on the trace waveform of the maximum flow velocity Vp on spectrum data. In the image of FIG. 13, an ECG signal is also displayed for reference.

  Furthermore, diagnostic parameters can be measured using representative waveform data representing the blood flow velocity and tissue Doppler imaging waveform data (TDI WAVE) representing the moving speed of the leaflets collected separately by TDI. When performing TDI, the spectrum data generation unit 14 obtains tissue Doppler spectrum data based on the tissue clutter component of the Doppler signal.

  As a concrete example, the amplitude of the LV Inflow E wave and the peak of TDI WAVE using representative waveform data representing the speed of LV inflow and TDI WAVE representing the speed of the mitral valve (MV) annulus. It is possible to automatically measure a certain e 'wave amplitude ratio E / e'. This E / e 'is known as one of useful blood flow diagnostic parameters.

  Note that the ratio E / e 'of E wave and e' wave is the ratio of the maximum value Ve of the E wave of the representative waveform data of LV inflow and the maximum value of the TDI WAVE Vma of the mitral valve [Ve] max / It may be expressed as [Vma] max. In addition, when the time axis is normalized by the period of one heartbeat and expressed in% units, the measurement of E / e 'is simplified.

  FIG. 14 is a diagram for explaining a method for measuring the diagnostic parameter E / e ′ based on the representative waveform data and the velocity data corresponding to the clutter component of the mitral valve, and FIG. 15 is an automatic method by the method shown in FIG. It is a figure which shows the example which displayed the peak [Ve] max of measured representative waveform data Ve, and the peak [Vma] max of TDI WAVE Vma of a mitral valve.

  In FIG. 14, the horizontal axis represents time, ECG represents the ECG signal, PCG represents the PCG signal, and VELOCITIES OF LV INFLOW AND MV represents TDI WAVE representing the representative waveform data of the LV inflow velocity and the moving speed of the mitral valve. , Respectively.

  As shown in FIG. 14, LV inflow representative waveform data and mitral are triggered by the reference position of ED, PS, etc. obtained from the ECG signal R wave, PCG signal S1 or S2 wave, and blood flow velocity trace waveform. It can be overlapped with the TDI WAVE of the valve. Then, the peak [Ve] max of the LV inflow E wave Ve and the peak [Vma] max of the e ′ wave Vma of the mitral valve TDI WAVE can be detected and displayed as shown in FIG. FIG. 15 shows an example in which the peak [Ve] max of the E wave Ve of LV inflow and the peak [Vma] max of the e ′ wave Vma of TDI WAVE of the mitral valve are detected using the ECG signal as a trigger.

  By calculating the ratio of the peak [Ve] max of the E wave Ve of the representative waveform data of LV inflow thus detected to the peak [Vma] max of the e 'wave Vma of the mitral valve TDI WAVE, E / e 'can be obtained.

  The Doppler signal for Doppler spectrum data and the Doppler signal for tissue Doppler data can be collected in a common range. In this case, the representative waveform data representing the maximum flow velocity Vp of LV inflow and the TDI WAVE of the mitral valve are superimposed and displayed using a common velocity range.

  FIG. 16 is a diagram illustrating an example in which representative waveform data representing the maximum flow velocity Vp of LV inflow and TDI WAVE of the mitral valve are superimposed and displayed using a common velocity range.

  In FIG. 16, the horizontal axis represents time, and the vertical axis represents the frequency corresponding to the speed. As shown in FIG. 16, representative waveform data representing the maximum frequency fp of LV inflow and TDI WAVE of the mitral valve are superimposed and displayed using a common frequency range (or velocity range).

  However, in order to obtain the amplitude of the E wave and the amplitude of the e 'wave with better accuracy, it is desirable to display in the frequency range or speed range suitable for the E wave and the e' wave. Therefore, the representative waveform data and the TDI WAVE can be extracted from the data obtained by superimposing the representative waveform data and the TDI WAVE using different filters suitable for obtaining the amplitude of the E wave and the amplitude of the e ′ wave, respectively.

  FIG. 17 shows the LV inflow maximum frequency fp and the monk extracted from the data in which representative waveform data representing the maximum frequency fp of LV inflow and TDI WAVE representing the velocity of the mitral valve are superimposed with different filtering processes. It is a figure which shows the example which measured the diagnostic parameter E / e 'from TDI WAVE of the cap valve.

  In FIG. 17, the horizontal axis represents time, the vertical axis of (A) represents the representative waveform data fp of the maximum frequency of LV inflow, the vertical axis of (B) represents the frequency ftdi corresponding to the speed of the mitral valve, (C) The vertical axis of E shows the amplitude of the E wave and e ′ wave, and the vertical axis of (D) shows the value of the diagnostic parameter E / e ′.

  As shown in FIG. 17A, representative waveform data fp of the maximum frequency of LV inflow in an appropriate frequency range in which the low frequency component is removed from the superimposed data shown in FIG. 16 by filtering using HPF (high pass filter). Can be extracted. On the other hand, as shown in FIG. 17B, the frequency data ftdi of the mitral valve in the appropriate frequency range in which the high frequency component is removed from the superimposed data shown in FIG. 16 is extracted by filtering using LPF (low pass filter). be able to.

  Next, the peak fpmax is detected in the representative waveform data fp of the maximum frequency of LV inflow shown in FIG. Also, the peak ftdimax is detected in the mitral valve frequency data ftdi shown in FIG. Then, by converting the peak fpmax of the representative waveform data fp of the maximum frequency of LV inflow and the peak ftdimax of the mitral valve frequency data ftdi to a speed scale, an E wave and an e ′ wave are obtained as shown in FIG. Can be obtained. Further, by dividing the amplitude of the E wave by the amplitude of the e ′ wave, the value of the diagnostic parameter E / e ′ can be calculated as shown in FIG.

  In addition, Tei-Index, which is known as one of blood flow diagnosis parameters, can be automatically measured.

  FIG. 18 is a diagram illustrating the definition of the diagnostic parameter Tei-Index.

  In FIG. 18, the horizontal axis represents time, ECG represents an ECG signal, LV INFLOW represents LV Inflow spectrum waveform data, and LV OUTFLOW represents LV Outflow spectrum waveform data.

As shown in FIG. 18, Tei-Index is a time between the end of the A wave of the heartbeat in the spectrum waveform data of LV Inflow and the tip of the E wave of the next heartbeat, and the spectrum waveform data of LV Outflow. If the time from the top of the peak to the end of the peak is b, it is expressed as shown in Equation (3). [Equation 3]
Tei-Index = (ab) / b (3)
Therefore, if one or both of a and b are calculated from the LV Outflow spectrum data and the representative waveform data of the LV Outflow spectrum data, a representative value of a and / or b can be obtained. This representative value is an average value of a and b measured a plurality of times for a plurality of heartbeats. For this reason, if Tei-Index is calculated using the representative values of a and / or b, a stable Tei-Index with little variation can be easily obtained. For this reason, the user can use Tei-Index for evaluation of cardiac function.

  Such diagnostic parameters such as E waves can be calculated by the parameter calculation unit 27 in real time or after freezing, as with parameters such as PS, ED, and HR. The calculated diagnostic parameter value can be displayed as a numerical value on the display unit 8 from the parameter calculation unit 27 through the display processing unit 31.

  When displaying the diagnostic parameter value in real time, the diagnostic parameter value can be updated with a desired delay time each time a trigger such as PS is detected. Therefore, instead of displaying the diagnostic parameter corresponding to the heartbeat in which the latest PS is detected, the diagnostic parameter corresponding to the heartbeat in which the PS for which a desired time has elapsed may be displayed.

  When displaying the value of a diagnostic parameter immediately after freezing, the diagnostic parameter corresponding to the nearest heartbeat or a heartbeat after a desired time can be displayed. In this case, as described above, the part of the trace waveform of the heartbeat used for calculating the diagnostic parameter, the waveform data for each heartbeat, and the part of the Doppler spectrum data are automatically scrolled back and displayed. Or identification display.

  That is, the ultrasonic diagnostic apparatus 1 as described above creates a plurality of waveform data for each heartbeat as diagnostic information by performing CAB processing on a trace waveform obtained from Doppler spectrum data. Furthermore, the ultrasonic diagnostic apparatus 1 creates representative waveform data from a plurality of waveform data for each heartbeat by averaging processing or modeling processing, and can automatically measure diagnostic parameters from the created representative waveform data. .

(effect)
Therefore, according to the ultrasonic diagnostic apparatus 1, it is possible to acquire diagnostic information representing a blood flow as simple data in a simpler and shorter time.

  In particular, in the past, diagnostic parameters such as HR, PS, ED, PI and RI could be automatically measured if the time cursor for setting the diagnostic parameter measurement range could be set automatically. However, it is difficult to automatically measure diagnostic parameters such as E waves.

  In contrast, the ultrasonic diagnostic apparatus 1 can automatically measure diagnostic parameters such as LV Inflow E wave, A wave, DCT, PV S wave, D wave, and AR wave. Therefore, the inspection throughput can be improved.

  Also, if PS or ED detected from the trace waveform is used as a trigger to automatically measure diagnostic parameters such as E waves within the specified search range of the trace waveform, noise increases and diagnostic parameters cannot be obtained with sufficient accuracy. .

  In contrast, the ultrasonic diagnostic apparatus 1 automatically measures diagnostic parameters such as E waves using representative waveform data created from a plurality of waveform data obtained by CAB processing. Etc. can be obtained.

DESCRIPTION OF SYMBOLS 1 Ultrasonic diagnostic apparatus 2 Ultrasound probe 3 Reference signal generation part 4 Transmission / reception part 5 Data generation part 6 Data storage part 7 Blood flow evaluation part 8 Display part 9 ECG unit 10 PCG unit 11 B mode data generation part 12 Doppler signal detection part 13 Color Doppler Data Generation Unit 14 Spectrum Data Generation Unit 15 Trace Waveform Generation Unit 16 PS / ED Detection Unit 17 Parameter Measurement Unit 20 CAB Processing Unit 21 CAB Condition Setting Unit 22 Range Setting Unit 23 Averaging Processing Unit 24 Averaging Condition Setting Unit 25 Mathematical Modeling Unit 26 Modeling Condition Setting Unit 27 Parameter Calculation Unit 28 Data Reject Unit 29 Reject Data Selection Unit 30 Reject Condition Setting Unit 31 Display Processing Unit 32 Input Device

Claims (16)

By transmitting / receiving ultrasonic waves to / from a subject, a Doppler signal in a specified range gate is extracted, blood flow Doppler spectrum data is collected based on the Doppler signal, and a tissue clutter component of the Doppler signal is collected. Data collection means for collecting Doppler imaging waveform data of tissue based on
A plurality of waveform data is generated by performing processing of cutting out and arranging Doppler spectrum envelope waveforms for a plurality of heartbeats automatically traced from the Doppler spectrum data for each heartbeat, and one representative waveform data is generated from the plurality of waveform data. Representative data generation means for generating;
Measuring means for measuring a first diagnostic index related to blood flow velocity based on the representative waveform data, and measuring a second diagnostic index related to tissue motion velocity based on the Doppler imaging waveform data;
Blood flow diagnostic information calculating means for calculating a third diagnostic index as blood flow diagnostic information based on the first diagnostic index and the second diagnostic index;
An ultrasonic diagnostic apparatus comprising:   The measurement means measures the first diagnostic index by applying HPF to the Doppler signal and measures the second diagnostic index by applying LPF to the Doppler signal. The ultrasonic diagnostic apparatus according to 1.   The ultrasonic diagnostic apparatus according to claim 1, wherein the measuring unit measures the first diagnostic index and the second diagnostic index in a common speed range.   The ultrasonic diagnostic apparatus according to claim 1, further comprising display means for highlighting the plurality of waveform data to display on the display unit.   5. The ultrasonic diagnostic apparatus according to claim 4, wherein, when displaying the Doppler spectrum data and the plurality of waveform data in parallel, the display means displays the plurality of waveform data at a slower sweep speed than the Doppler spectrum data.   The ultrasonic diagnostic apparatus according to claim 1, wherein the representative data generation unit is configured to acquire averaged waveform data as the representative waveform data by performing an averaging process on the plurality of waveform data.   The ultrasonic diagnostic apparatus according to claim 1, wherein the representative data generation unit is configured to acquire waveform data obtained by modeling the plurality of waveform data using a mathematical model as the representative waveform data. .   The ultrasonic diagnostic apparatus according to claim 1, wherein the representative data generation unit is configured to cut out the Doppler spectrum envelope waveform for each heartbeat at a timing determined based on the Doppler spectrum envelope waveform. Peak detecting means for detecting at least one of a systolic waveform peak and a diastole waveform peak based on the Doppler spectrum envelope waveform,
The representative data generation unit is configured to cut out the Doppler spectrum envelope waveform for each heartbeat at a timing determined based on at least one of the waveform peak in the systole and the waveform peak in the diastole. Ultrasound diagnostic equipment.   The ultrasonic diagnostic apparatus according to claim 1, wherein the representative data generation unit is configured to cut out the Doppler spectrum envelope waveform for each heartbeat at a timing determined based on an electrocardiogram waveform or a heart sound waveform.   The ultrasonic diagnostic apparatus according to claim 1, wherein the representative data generation unit is configured to normalize a time of the plurality of waveform data with a period of one heartbeat.   The ultrasonic diagnostic apparatus according to claim 1, further comprising display means for displaying the plurality of waveform data together with at least one of the Doppler spectrum data and the Doppler spectrum envelope waveform on a display unit. The ultrasonic diagnostic apparatus according to claim 12 , wherein the display unit is configured to display the plurality of waveform data as a plurality of two-dimensional data for each heartbeat.   The ultrasonic diagnostic apparatus according to claim 1, wherein the representative data generation unit is configured to generate the representative waveform data by excluding waveform data taking a singular value from the plurality of waveform data.   The representative data generating means displays the waveform data taking the singular value detected by the threshold processing for the amount representing the characteristics of the plurality of waveform data, and the waveform selected from the displayed waveform data taking the singular value The ultrasonic diagnostic apparatus according to claim 14, configured to exclude data and recalculate the representative waveform data. Computer
By transmitting / receiving ultrasonic waves to / from a subject, a Doppler signal in a specified range gate is extracted, blood flow Doppler spectrum data is collected based on the Doppler signal, and a tissue clutter component of the Doppler signal is collected. Data collection means for collecting Doppler imaging waveform data of tissue based on
A plurality of waveform data is generated by performing processing of cutting out and arranging Doppler spectrum envelope waveforms for a plurality of heartbeats automatically traced from the Doppler spectrum data for each heartbeat, and one representative waveform data is generated from the plurality of waveform data. Representative data generation means to generate,
Measuring means for measuring a first diagnostic index related to blood flow velocity based on the representative waveform data, and measuring a second diagnostic index related to tissue motion speed based on the Doppler imaging waveform data; and the first diagnosis Blood flow diagnostic information calculating means for calculating a third diagnostic index as blood flow diagnostic information based on the index and the second diagnostic index;
A data processing program for an ultrasonic diagnostic apparatus that functions as a computer program.