TWI550268B  Method of Improving Sensitivity of Quantitative Tissue Characteristic of Ultrasonic  Google Patents
Method of Improving Sensitivity of Quantitative Tissue Characteristic of Ultrasonic Download PDFInfo
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 TWI550268B TWI550268B TW104116574A TW104116574A TWI550268B TW I550268 B TWI550268 B TW I550268B TW 104116574 A TW104116574 A TW 104116574A TW 104116574 A TW104116574 A TW 104116574A TW I550268 B TWI550268 B TW I550268B
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 ultrasonic
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Description
The invention relates to a method for improving the sensitivity of ultrasonic quantitative tissue characteristics, in particular to a method for improving the sensitivity of ultrasonic quantitative tissue characteristics by using the original RF signal amplitude as a weighting factor to calculate the weighted entropy and making the variation of the scattering subdensity more distinct. .
Press, ultrasonic inspection is a radiological technique that uses highfrequency sound waves to produce images of body organs and structures. Ultrasonic detection has become the most important medical instant image because of its immediate, low cost, easy to use, noninvasive, nondissociative radiation and high security.
In general, physicians use ultrasonic detectors to detect ultrasound motion images of body organs and try to capture the best angle for capturing realtime images. In practical applications, the ultrasonic detector includes an image recognition system, and the image recognition system can read the content of the captured instant image and provide several image analysis tools.
However, since ultrasound is an image that provides immediacy, it does not provide quantitative data related to tissue characteristics. Therefore, the interpretation of ultrasound images relies on the personal experience of the physician and the subjective recognition, which is less objective. For example, a physician should visually inspect the image brightness characteristics or texture, and different physicians will have different interpretations for the same image. Or use different system gain to scan images, the same tissue will also produce different brightness or feature images, making it difficult for doctors to judge or interpret the nature of the tissue.
Many studies and literatures have confirmed that raw radiofrequency signals of ultrasonic images are related to tissue microstructure characteristics (such as scattering density, arrangement, concentration, etc.). Therefore, many methods use mathematical statistical models to fit the envelope of the original signal, and obtain the quantitative information of the tissue characteristics through the signal encapsulation probability density function description.
But the disadvantage of using a statistical model is that the data that is used to compute itself must follow the mathematical statistical model used. If the data acquisition is not the original RF signal, or the acquired data is subjected to nonlinear processing (such as the logarithmic compression calculation commonly used for grayscale imaging), the quantitative parameter established by the data, or the parameter image, will lose the physicality of the parameter itself. significance.
In addition, some scholars have proposed the use of message theory entropy to interpret the complexity of ultrasonic original signals, such as Shannon Entropy, and the probability density function of signals. The advantages of using message theory entropy include: (1) The theoretical entropy of the message is not a mathematical model. The signal itself does not need to follow a certain distribution, and the actual application is more flexible and extensive. (2) The information entropy of the message is calculated by using the RF signal itself. Compared with the ultrasonic statistical model, the theoretical entropy of the message can truly reflect the signal information of the signal waveform. (3) Due to the limitation of the theoretical entropy of the message theory, this pair cannot provide the ultrasonic original RF. For the ultrasonic system of signal data, there is an opportunity to develop an image of the ultrasonic function parameter.
However, the message theory entropy still has its shortcomings. In the actual tissue microstructure, the difference in the concentration of the scatterer is not obvious enough, and its sensitivity still needs to be strengthened.
Therefore, how to use the information theory entropy to improve the differentiation of the scatterer concentration is not obvious enough, and the sensitivity still needs to be strengthened. It has become the goal of related manufacturers and related R&D personnel, and it must become a future trend. Question.
The main purpose of this creation is to improve the quantitative informationization of the wellknown technical characteristics of ultrasonic images. The mathematical statistical model must be followed, and the use of information theory entropy to interpret the original RF signal of the ultrasonic wave is not sufficiently distinguishable. Obviously, its sensitivity still needs to be strengthened, and it is actively developing, in order to improve the abovementioned shortcomings. After continuous experimentation and efforts, the present invention has finally been developed.
In order to achieve the above object, the present invention is achieved by the following technical means, wherein the method for improving the sensitivity of the ultrasonic quantitative tissue characteristic of the present invention comprises at least the following steps: transmitting an ultrasonic wave; the ultrasonic instrument receiving the ultrasonic wave An echo of the echo; the ultrasonic instrument generates at least one RF signal based on the echo and transmits the signal to an operation core; the operation core respectively generates a corresponding weighting factor based on the amplitude value of the RF signal; the operation core is based on The RF signal generates a probability density function; the computing core calculates the weighted entropy by using the probability density function and the weighting factor; and outputs the weighted entropy result to quantify the tissue characteristics.
In the preferred embodiment of the present creation, the weighted entropy is calculated as: ; among them, For weighted entropy; y is the amplitude of the RF signal (as the weighting factor body); Is the probability density function; m is the power of the weighting factor.
In a preferred embodiment of the present invention, the ultrasonic wave is a singleelement ultrasound system.
In a preferred embodiment of the present invention, the ultrasonic wave is an array of ultrasonic systems.
In the preferred embodiment of the present invention, the computing core is a cloud computing core.
By the above method, the present invention uses the amplitude of the RF signal as a weighting factor to calculate the weighted entropy, so that when the parameters of the original RF signal data of the ultrasonic image are imaged, the variation of the concentration of the scattering subconcentration is more obvious, and the sensitivity is improved. To help clinical interpretation.
In order to achieve the above objects and effects, the technical means and the structure of the present invention will be described in detail with reference to the preferred embodiments of the present invention.
Please refer to FIG. 1 and FIG. 2 at the same time, which is a flow chart and a schematic diagram of a preferred embodiment of the method for improving the sensitivity of the ultrasonic quantitative tissue characteristics. The method for improving the sensitivity of the ultrasonic quantitative tissue characteristic of the present invention is applied to an ultrasonic instrument 1. The ultrasonic instrument 1 can be a conventional ultrasonic instrument 1 structure, which can include an ultrasonic probe 11 and a processing device 12. And a display device 13. The ultrasonic probe 11 can be a singleelement ultrasonic probe or an array ultrasonic probe, which can be applied to a singleelement ultrasonic system and an array ultrasonic system for transmitting and receiving ultrasonic waves, respectively. The processing device 12 may include an amplifier, a filter, an analog/digital converter, and a beamforming unit, etc., but is not limited thereto.
Step 110: Transmitting an ultrasonic wave 110. The ultrasonic instrument 1 uses the ultrasonic probe 11 to emit an ultrasonic wave 110 into a body 4 to be tested.
Step 120: The ultrasonic instrument 1 receives an echo 111 reflected by the ultrasonic wave 110. After the ultrasonic wave 110 enters the object to be tested 4, a reflected echo 111 is generated, and the echo 111 is received by the ultrasonic probe 11 and transmitted to the ultrasonic instrument 1.
Step 130: The ultrasonic instrument 1 generates at least one RF signal 5 based on the echo 111 and transmits it to an operation core 2. Please refer to FIG. 3 at the same time, which is a schematic diagram of the RF signal of the preferred embodiment of the method for improving the sensitivity of the ultrasonic quantitative tissue characteristics. The ultrasonic instrument 1 can generate at least one radiofrequency data (RF data) by analyzing, amplifying, filtering, and analog/digital conversion of the echo 111 by using the internal processing device 12, and the radio frequency signal 5 is not An original RF signal 5 processed and compressed. The ultrasonic instrument 1 transmits the RF signal 5 to an operation core 2, which can be a computer computing device, which can be equipped with a central processing unit (CPU) and a graphics processing unit (GPU), which can quickly calculate and The RF signal 5 is processed.
Step 140: The computing core 2 generates a corresponding one of the weighting factors 21 based on the amplitude value of the RF signal 5. The computing core 2 analyzes the RF signal 5. Since the RF signal 5 is aggregated by a myriad of points representing amplitudes, the value is taken out according to the magnitude of all amplitude values on the time axis of the RF signal 5, and is taken as A weighting factor of 21.
Step 141: The computing core 2 generates a probability density function 22 based on the RF signal 5. Some scholars have studied and published the use of Fourier analysis to calculate the RF signal 5, which can be expressed as shown in the formula (1): (1) where, Is the probability density function 22; δ = ( y _{max} – y _{min} ) / 2 ; μ = ( y _{max} + y _{min} ) / 2; and a _{n} is a Fourier coefficient, which can be expressed as shown in formula (2) : ; (2)
The dynamic range ( y _{max} ~ y _{min} ) of the RF signal 5 generated by different ultrasonic systems is not the same. Therefore, the normal amplitude signal should be limited to between 1 and 1, and the result of infinite accumulation can be approximated by the Fourier series. Therefore, formulas (1) and (2) can be corrected as shown in equations (3) and (4): ; and (3) ; (4)
Step 150: The computing core 2 calculates a weighted entropy 23 by using the probability density function 22 and the corresponding weighting factor 21. Through the probability density function 22 and the weighting factor 21, it can be used to bring in the calculation of the weighting entropy 23, which can be expressed as shown in the formula (5): (5) where, For this weighted entropy 23; y is the weighting factor 21 body (amplitude value of the radio frequency signal 5); m is the power of the weighting factor, which is any number other than zero.
Step 160: Output the weighted entropy 23 result to quantify tissue characteristics. The computational core outputs the weighted entropy 23 back to the ultrasound instrument 1 to quantify tissue characteristics. The quantitative tissue characteristic refers to data of the tissue characteristics, and the user can refer to the parameter data of the tissue characteristics of the test object 4 to determine a shadow, a hard block or other lesions for the purpose of clinical medical interpretation. Preferably, the computing core outputs the weighted entropy 23 result to a mobile device 3, so that the user can view the analysis result at any time.
It is worth mentioning that, please refer to FIG. 4 at the same time, which is a schematic diagram of another embodiment of the method for improving the sensitivity of the quantitative ultrasonic tissue characteristics. When the ultrasonic probe 11 transmits the array ultrasonic wave, the ultrasonic instrument 1 uses the internal processing device 12 and the sound beam to synthesize an RF signal image 51 and transmits it to the computing core. The computing core can divide the RF signal image 51 into a plurality of block units 52 and perform calculations for each of the block units 52.
The computing core 2 can be a cloud computing core. Since the processing of the RF signal image 51 may consume more resources and time, the ultrasonic instrument 1 transmits the RF signal 5 to the cloud computing core by using the Internet. Not only can the cost and space of the computing core 2 be eliminated, but the cloud computing core can also collect relevant data to feed back to the supplier.
Please refer to FIG. 5a to FIG. 5d at the same time, which is a comparison diagram of the variation of the number of scatterers in the preferred embodiment of the method for improving the sensitivity of the quantitative quantitative tissue characteristics of the present invention. Fig. 5d is a variation diagram of the conventional message theory entropy at different probability density function cutting intervals (cutting pitch dy is 0.01~0.04). It can be seen that the slope of the dotted line is small, and the change of the theoretical entropy of the message is not obvious. Figure 5a to Figure 5c show the variation of the weighted entropy of the creation of the probability density of 23 different probability density functions (the weights of the entropy are 0.01, 0.02 and 0.04, respectively). It can be seen that the slope of the dotted line is large and the weighted entropy is obtained. The change in 23 is more obvious. Through the calculation results of the weighted entropy, it can be seen that the theoretical entropy of the known information is not obvious with different number of scatterers, and the sensitivity is limited. However, when the weighted entropy 23 proposed by the present invention increases from 2 to 32 (scatterer/mm ^{2} ), the weighted entropy 23 increases from 0.08 to 0.23, and the sensitivity is improved.
In summary, the method for improving the sensitivity of the quantitative quantitative tissue characteristics proposed by the present invention is better than the conventional technique for the tissue characteristics with different scattering subdensities, and the weighted entropy results are output to quantify the tissue characteristics. It can effectively improve the sensitivity and ability of quantitative organization, and it is clearer and more accurate in interpretation.
Therefore, it can be fully demonstrated that the object and effect of the present invention are both progressive in implementation, extremely valuable in industrial use, and are new inventions that have not been seen before on the market, fully comply with the requirements of invention patents, and apply in accordance with the law. .
The above description is only the preferred embodiment of the present invention, and is not intended to limit the scope of the invention. All changes and modifications made in accordance with the scope of the invention shall fall within the scope of the patents of the invention. I would like to ask your review committee to give a clear explanation and pray for it.
1‧‧‧Ultrasonic instrument
11‧‧‧Ultrasonic probe
110‧‧‧Supersonic
111‧‧‧ echo
12‧‧‧Processing device
13‧‧‧Display device
2‧‧‧ computing core
21‧‧‧weighting factor
22‧‧‧ probability density function
23‧‧‧ Weighted Entropy
3‧‧‧Mobile devices
4‧‧‧Subjects
5‧‧‧RF signal
51‧‧‧RF signal image
52‧‧‧ Block unit step 110‧‧‧ emit an ultrasonic step 120‧‧‧ The ultrasonic instrument receives an echo of the ultrasonic reflection step 130‧‧‧ The ultrasonic instrument generates at least one based on the echo The RF signal is transmitted to an operation core step 140. The operation core generates a corresponding weighting factor based on the magnitude of the amplitude of the RF signal. Step 141. The operation core generates a probability density function step based on the RF signal. 150‧‧‧ The computing core uses the probability density function and the corresponding weighting factor to calculate a weighted entropy step 160‧‧‧ Output the weighted entropy result to quantify the tissue characteristics
1 is a flow chart showing a preferred embodiment of a method for improving the sensitivity of ultrasonic quantitative tissue characteristics according to the present invention. FIG. 2 is a schematic view showing a preferred embodiment of a method for improving the sensitivity of ultrasonic quantitative tissue characteristics according to the present invention. FIG. 3 is a schematic diagram of an RF signal according to a preferred embodiment of the method for improving the sensitivity of the quantitative quantitative tissue characteristics of the present invention. FIG. 4 is a schematic view showing another embodiment of a method for improving sensitivity of ultrasonic quantitative tissue characteristics according to the present invention. 5a to 5d are graphs showing a comparison of the variation of the number of scatterers in a preferred embodiment of the method for improving the sensitivity of the quantitative structure of ultrasonic waves of the present invention.
Step 110‧‧‧ emit an ultrasonic wave
Step 120‧‧‧ The ultrasonic instrument receives an echo of the ultrasonic reflection
Step 130‧‧ The ultrasonic instrument generates at least one RF signal based on the echo and transmits it to an operation core
Step 140‧‧‧ The computing core generates a corresponding one of the weighting factors based on the magnitude of the amplitude of the RF signal
Step 141‧‧ The computing core generates a probability density function based on the RF signal
Step 150‧‧ The computing core calculates the weighted entropy by using the probability density function and the corresponding weighting factor
Step 160‧‧‧ Output the weighted entropy results to quantify the tissue characteristics
Claims (9)
 A method for improving the sensitivity of ultrasonic quantitative tissue characteristics, which is applied to an ultrasonic instrument, comprising at least the steps of: transmitting an ultrasonic wave; the ultrasonic instrument receiving an echo reflected by the ultrasonic wave; the ultrasonic instrument is based on the echo The wave generates at least one RF signal and transmits it to an operation core, and the RF signal is generated by the echo analysis, amplification, filtering, and analog/digital conversion; the operation core is generated based on the magnitudes of all the amplitude values of the RF signal on the time axis. Corresponding to one weighting factor; the computing core limits the amplitude of the RF signal to a range of plus or minus 1 and performs a Fourier analysis calculation on the interval to generate a probability density function; the computing core utilizes the probability density function and the corresponding The weighting factor calculates a weighted entropy; and outputs the weighted entropy result to quantify the tissue characteristics.
 The method for improving the sensitivity of the ultrasonic quantitative tissue characteristic as described in claim 1 is wherein the weighted entropy is calculated as:
 The method for improving the sensitivity of ultrasonic quantitative tissue characteristics as described in claim 1, wherein the quantitative tissue property means that the tissue characteristics are digitized for interpretation.
 The method for improving the sensitivity of the ultrasonic quantitative tissue characteristic as described in claim 1, wherein the ultrasonic wave is a singleelement ultrasonic system.
 The method for improving the sensitivity of ultrasonic quantitative tissue characteristics as described in claim 1, wherein the ultrasonic wave is an array type ultrasonic system.
 The method for improving the sensitivity of the ultrasonic quantitative tissue characteristic described in claim 1 is wherein the computing core is a cloud computing core.
 The method for improving the sensitivity of ultrasonic quantitative tissue characteristics as described in claim 1, wherein the method further comprises outputting a weighted entropy result to the ultrasonic instrument.
 The method for improving the sensitivity of the ultrasonic quantitative tissue characteristic as described in claim 1, wherein the method further comprises outputting the weighted entropy result to a mobile device.
 The method for improving the sensitivity of the ultrasonic quantitative tissue characteristic as described in claim 2, wherein the weighting factor power is an arbitrary number other than zero.
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CN1433559A (en) *  19991210  20030730  杜兰德技术有限公司  Improvements in/or relating to applications of fractal and/or chaotic techniques 
TW201416062A (en) *  20121019  20140501  Univ Nat Taiwan Science Tech  An image recognition system and method of 

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CN1433559A (en) *  19991210  20030730  杜兰德技术有限公司  Improvements in/or relating to applications of fractal and/or chaotic techniques 
TW201416062A (en) *  20121019  20140501  Univ Nat Taiwan Science Tech  An image recognition system and method of 
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