CN113679418B - Ultrasonic imaging method based on compressed signal-to-noise ratio coefficient weighting - Google Patents
Ultrasonic imaging method based on compressed signal-to-noise ratio coefficient weighting Download PDFInfo
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- CN113679418B CN113679418B CN202110975053.5A CN202110975053A CN113679418B CN 113679418 B CN113679418 B CN 113679418B CN 202110975053 A CN202110975053 A CN 202110975053A CN 113679418 B CN113679418 B CN 113679418B
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- 238000003384 imaging method Methods 0.000 title claims abstract description 119
- 239000013598 vector Substances 0.000 claims abstract description 27
- 230000006835 compression Effects 0.000 claims abstract description 16
- 238000007906 compression Methods 0.000 claims abstract description 16
- 239000000523 sample Substances 0.000 claims abstract description 6
- 238000000034 method Methods 0.000 claims description 9
- 230000035945 sensitivity Effects 0.000 claims description 4
- 230000001629 suppression Effects 0.000 description 4
- 238000012285 ultrasound imaging Methods 0.000 description 4
- 230000007547 defect Effects 0.000 description 2
- 238000002604 ultrasonography Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B8/00—Diagnosis using ultrasonic, sonic or infrasonic waves
- A61B8/52—Devices using data or image processing specially adapted for diagnosis using ultrasonic, sonic or infrasonic waves
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10132—Ultrasound image
Abstract
The invention discloses a high-quality ultrasonic imaging method based on compressed signal-to-noise ratio coefficient weighting, which comprises the steps of firstly storing data after delay of echo signals received by an ultrasonic probe as vectors, then calculating high-sensitivity signal-to-noise ratio coefficients of the vectors, adjusting and outputting the coefficients as compressed signal-to-noise ratio coefficients through a compression adjustment function, and finally multiplying the coefficients with imaging results output by a wave beam former in an ultrasonic imaging system to obtain images with high contrast and high resolution. The invention can inhibit stronger noise and has little loss of imaging intensity, thereby obtaining high-resolution and high-contrast ultrasonic images.
Description
Technical Field
The invention is suitable for the field of medical ultrasonic imaging, in particular to a high-quality ultrasonic imaging method based on compressed signal-to-noise ratio coefficient weighting.
Background
Medical ultrasound imaging equipment is one of the commonly used imaging equipment and has important value in many important disease diagnoses. In the medical ultrasonic imaging process, an ultrasonic probe firstly transmits ultrasonic waves to enter an imaging area, then receives echo signals scattered back by the imaging area, then determines echo signals of imaging points according to the space position of each imaging point of the space area, delays the echo signals to obtain imaging vectors, and directly overlaps the imaging vectors to obtain an imaging result. The direct superposition imaging method has the defects of low resolution, low contrast, and the like. High quality ultrasound imaging methods are a core technology of medical ultrasound imaging equipment. Imaging methods based on coefficient weighting are currently commonly used imaging methods to improve ultrasound imaging quality. The method designs a weighting coefficient according to the characteristics of imaging vectors, weights directly overlapped imaging results and improves imaging quality. The common weighting coefficients are currently mainly coherence coefficients designed according to coherence properties. Although the coherence coefficient weighted imaging method has better imaging noise suppression capability, the improvement of resolution and contrast ratio is still insufficient. The snr weighting imaging method, although having higher resolution, reduces the imaging intensity, resulting in limited contrast enhancement.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides an ultrasonic imaging method based on the weighting of the compressed signal-to-noise ratio coefficient, so that the image noise can be effectively restrained, the loss of the imaging intensity is less, and the ultrasonic image with high resolution and high contrast is obtained, thereby improving the imaging quality of an ultrasonic imaging system.
In order to achieve the aim of the invention, the invention adopts the following technical scheme:
the invention discloses an ultrasonic imaging method based on compressed signal-to-noise ratio coefficient weighting, which is characterized by comprising the following steps:
step one: in the ultrasonic imaging process, an echo signal of each imaging point in an original image is obtained by utilizing an ultrasonic probe, an imaging vector of each imaging point is obtained after delay, and then the intensity values in the imaging vectors of each imaging point are summed to obtain an imaging result of each imaging point respectively;
step two: after the imaging vector of each imaging point is respectively normalized, calculating the mean value and standard deviation of the imaging vector of each imaging point, and calculating the high-sensitivity signal-to-noise ratio coefficient of each imaging point according to the standard deviation and the mean value;
step three: establishing a compression adjustment function for performing compression adjustment on the high-sensitivity signal-to-noise ratio coefficient and obtaining a compression signal-to-noise ratio coefficient of each imaging point;
step four: multiplying the compressed signal-to-noise ratio coefficient of each imaging point with the corresponding imaging result respectively, thereby obtaining a weighted imaging result of each imaging point;
step five: and carrying out gray processing on weighted imaging results of all imaging points to obtain high-quality ultrasonic images.
The high sensitivity signal-to-noise ratio SF in the second step h Is obtained by using the formula (1):
in the formula (1), μ is the average value of imaging vectors of any imaging point, and represents the intensity of effective signals in the imaging vectors; σ is the standard deviation of the imaging vector for any imaging point and represents the intensity of noise in the imaging vector.
The compression adjustment function in the third step is shown in the formula (2):
in the formula (2), f (SF) h ) Representing the compressed signal-to-noise ratio coefficient.
Compared with the prior art, the invention has the beneficial effects that:
the compressed signal-to-noise ratio coefficient in the invention can effectively identify noise and clutter in an imaging region, the value of the compressed signal-to-noise ratio coefficient for the noise and the clutter is lower, and the value of the compressed signal-to-noise ratio coefficient of an effective signal is higher. The compressed signal-to-noise ratio coefficient has excellent noise suppression capability, clutter and noise can be effectively suppressed, and the strength of an effective signal is maintained. The compressed signal-to-noise ratio coefficient is multiplied by the directly superimposed ultrasonic imaging result to obtain an ultrasonic image with high contrast and high resolution, and has certain clinical application value.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a graph showing the input and output relationship of the compression tone function of the present invention;
FIG. 3 is a graph of direct superimposed imaging results of simulated scattering points of the present invention;
FIG. 4 is a graph of the compressed SNR coefficient weighted imaging result of the simulated scattering point of the present invention;
FIG. 5 is a graph of direct superimposed imaging results of simulated circular plaques of the present invention;
FIG. 6 is a graph of the result of the compressed SNR weighting imaging of the simulated circular plaque of the present invention.
Detailed Description
In this embodiment, as shown in fig. 1, an ultrasound imaging method based on weighting of compressed signal-to-noise ratio coefficients includes:
step one: setting the signal value obtained by delaying echo signals received by the nth array element in the ultrasonic probe at p point position in the imaging area as v n (p) to obtain an imaging vector V (p) = [ V ] 1 (p),v 2 (p),…,v n (p),…,v N (p)]N represents the total number of receiving array elements, and the p-point direct superposition ultrasonic imaging result I (p) is calculated by using the formula (1):
step two: normalizing the imaging vector by using the formula (2), and obtaining a normalized imaging vector V' (p):
calculating the high sensitivity signal-to-noise ratio coefficient SF of the p point by using the method (3) h (p)：
In the formula (3), E is the average value, and sigma is the standard deviation. The noise suppression capability can be improved by taking the signal to noise ratio coefficient to 3 times in the formula (3).
Step three: the designed compression adjustment function is shown in the formula (4). The compression function may compress the value of the input variable X to between 0 and 1. The function input-output relationship is shown in fig. 2.
SF of high sensitivity signal-to-noise ratio coefficient h (p) is substituted into the compression adjustment function to obtain a compressed signal-to-noise ratio coefficient f (SF) by using equation (5) h )：
For imaging vectors with noise as a main component, SF is applied to imaging vectors with noise as a main component h Compared with the traditional signal-to-noise ratio coefficient, the method has stronger pressing capability. However, for imaging vectors with effective signals as the main component, SF h The estimated value of (2) may be too high, resulting in a deviation in the imaging result. The noise suppression capability of the high-sensitivity signal-to-noise ratio coefficient is maintained by compressing the signal-to-noise ratio coefficient after being regulated by the compression function, and the signal-to-noise ratio coefficient is used as SF h When the estimated value is too high, compression adjustment is performed, so that the distortion of the image is avoided.
Step five: the obtained weighting coefficient f (SF h ) Multiplying the direct imaging result I (p) to obtain a weighted imaging result I of the p-point position by using the formula (6) f (p)：
I f (p)＝f(SF h (p))×I(p) (6)
Repeating the steps one to four to obtain an imaging result I of the whole imaging area f . Will I f After logarithmic compression and graying, high-quality ultrasonic images can be obtained.
Examples
In this embodiment, a simulated ultrasound imaging system is first constructed, using a 128-element linear array probe. The center frequency of the transmitted signal is 5MHz, and the sampling frequency of the system is 80MHz. The sound velocity of the imaging region was 1540m/s, the focal depth was 40mm, and the number of effective array elements at the time of imaging per scanning line was 64. To verify the effectiveness of the algorithm, the simulated echo signals add gaussian white noise of a certain intensity. There are two kinds of imaging targets, one is two scattering points of strong echo with a distance of 14mm, the imaging area is 10mm by 5mm, the coordinates of the imaging points are (-0.7 mm,25.5 mm) and (0.7 mm,25.5 mm), and the imaging result is shown in fig. 3. Another object is a simulated sound absorbing circular spot. The imaging area was 10mm by 9mm, the diameter within the circular spot was 4mm, the depth was 30mm, and the imaging results are shown in FIG. 4.
Fig. 3 is a conventional direct superimposed imaging result, and it is seen that there is significant noise in the background, which makes it difficult to distinguish between two imaging points due to poor resolution. Fig. 4 shows the imaging result weighted by the compressed snr coefficient, and it can be seen that the background noise is significantly reduced, and the resolution of two points is significantly improved, so that the two points can be accurately distinguished.
Fig. 5 shows the direct superposition imaging result of the sound-absorbing circular spots, which can be seen to have larger noise and poorer contrast. Fig. 6 is a result of directly overlapping imaging results and multiplying the compressed signal-to-noise ratio coefficient, and it can be seen that noise inside the circular spot is removed, and contrast between the circular spot and background tissue is significantly improved.
Claims (1)
1. An ultrasonic imaging method based on compressed signal-to-noise ratio coefficient weighting is characterized by comprising the following steps:
step one: in the ultrasonic imaging process, an echo signal of each imaging point in an original image is obtained by utilizing an ultrasonic probe, an imaging vector of each imaging point is obtained after delay, and then the intensity values in the imaging vectors of each imaging point are summed to obtain an imaging result of each imaging point respectively;
step two: after the imaging vector of each imaging point is respectively normalized, the mean value and standard deviation of the imaging vector of each imaging point are calculated, so that the high sensitivity signal-to-noise ratio coefficient SF of each imaging point is calculated according to the standard deviation and the mean value h As shown in formula (1):
in the formula (1), μ is the average value of imaging vectors of any imaging point, and represents the intensity of effective signals in the imaging vectors; sigma is the standard deviation of the imaging vector of any imaging point and represents the intensity of noise in the imaging vector;
step three: establishing a compression adjustment function by using the formula (2), wherein the compression adjustment function is used for carrying out compression adjustment on the high-sensitivity signal-to-noise ratio coefficient and obtaining a compression signal-to-noise ratio coefficient of each imaging point;
in the formula (2), f (SF) h ) Representing the compressed signal-to-noise ratio coefficient;
step four: multiplying the compressed signal-to-noise ratio coefficient of each imaging point with the corresponding imaging result respectively, thereby obtaining a weighted imaging result of each imaging point;
step five: and carrying out gray processing on weighted imaging results of all imaging points to obtain high-quality ultrasonic images.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105982694A (en) * | 2015-01-27 | 2016-10-05 | 无锡祥生医学影像有限责任公司 | Signal processing method for inhibiting ultrasonic noises |
| CN106780407A (en) * | 2017-03-01 | 2017-05-31 | 成都优途科技有限公司 | A kind of denoising system and denoising method for ultrasound pattern speckle noise |
| CN107843305A (en) * | 2017-10-31 | 2018-03-27 | 合肥工业大学 | A kind of Ultrasonic Wave Flowmeter signal processing method based on echo signal envelope fitting |
| CN110622034A (en) * | 2017-05-11 | 2019-12-27 | 皇家飞利浦有限公司 | Reverberation artifact cancellation in ultrasound diagnostic images |
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| DE102006005804A1 (en) * | 2006-02-08 | 2007-08-09 | Siemens Ag | Method for noise reduction in tomographic image data sets |
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Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105982694A (en) * | 2015-01-27 | 2016-10-05 | 无锡祥生医学影像有限责任公司 | Signal processing method for inhibiting ultrasonic noises |
| CN106780407A (en) * | 2017-03-01 | 2017-05-31 | 成都优途科技有限公司 | A kind of denoising system and denoising method for ultrasound pattern speckle noise |
| CN110622034A (en) * | 2017-05-11 | 2019-12-27 | 皇家飞利浦有限公司 | Reverberation artifact cancellation in ultrasound diagnostic images |
| CN107843305A (en) * | 2017-10-31 | 2018-03-27 | 合肥工业大学 | A kind of Ultrasonic Wave Flowmeter signal processing method based on echo signal envelope fitting |
Non-Patent Citations (1)
| Title |
|---|
| 基于自适应加权算法的远聚焦超声成像;徐祥，汪亚中，郑驰，超彭虎;生物医学工程研究;20201231;319-329 * |
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