CN102008308B - Multi-b value diffusion tensor imaging sampling method - Google Patents

Abstract

A multi-b-value diffusion tensor imaging sampling method, comprising the following steps: scanning the entire field of view in a single pass at the first b-value, sampling to obtain a single-scan signal-to-noise ratio at the first b-value and a single scan at the first b-value Signal strength; the number of multiple scans under the first b value is calculated according to the signal-to-noise ratio of a single scan under the first b value and the multiple scan signal-to-noise ratio under the specified first b value; according to the single scan under the first b value Calculate the signal-to-noise ratio and signal strength of a single scan at the first b value to obtain the signal-to-noise ratio of a single scan at the second b value; The scan signal-to-noise ratio is calculated to obtain the number of multiple scans under the second b value. The above multi-b-value diffusion tensor imaging sampling method can adaptively select the number of repeated scans actually required for each b-value by implementing a single pre-scan on the first b-value and the relationship followed by the signal-to-noise ratio of different b-values , thereby reducing the total sampling times, greatly reducing the sampling time, and increasing the speed of diffusion tensor imaging.

Description

Multi-b value diffusion tensor imaging sampling method

【Technical field】

The invention relates to diffusion tensor imaging, in particular to a multi-b-value diffusion tensor imaging sampling method.

【Background technique】

The dispersion phenomenon involves the Brownian motion of molecules in the fluid, and the amount of dispersion is represented by the diffusion coefficient. In a homogeneous liquid, the diffusion coefficient is the same in all directions, that is to say, in a three-dimensional view, the diffusion process should be a spherical shape; however, in biological tissues, the diffusion coefficient in different directions is different For example, the diffusion of water in the direction perpendicular to the axon of the myelinated nerve fiber is more restricted than along the direction of the axon, and the diffusion process presents an ellipsoidal shape. For each voxel in the imaging area, the diffusion process of water molecules can be regarded as an ellipsoid. The diffusion process of water molecules can be represented by a diffusion tensor.

Diffusion tensor imaging is a special form of nuclear magnetic resonance imaging, and it is a new magnetic resonance imaging technology that has developed rapidly in recent years. Diffusion tensor imaging technology uses the diffusion anisotropy of water molecules for imaging. It can evaluate the integrity of tissue structure from the microscopic field, and can give disease status at the cellular and molecular levels. It is an important part of functional magnetic resonance imaging. . In diffusion tensor imaging, b is used to represent the diffusion factor, and the size of b depends on the waveform of the applied diffusion gradient. The value of b affects the process of diffusion. The larger the b value, the larger the dispersion and the smaller the signal-to-noise ratio of the signal. In practical applications of diffusion tensor imaging, multiple b-values are usually used to fit the single-exponential change rule followed by the diffusion process. In order to satisfy the signal-to-noise ratio, the traditional method is to improve the signal-to-noise ratio by scanning with the same number of repetitions for all b values. As a result, the sampling time is too long, which affects the speed of diffusion tensor imaging.

【Content of invention】

Based on this, it is necessary to provide a multi-b-value diffusion tensor imaging sampling method that can improve the diffusion tensor imaging speed.

A multi-b-value diffusion tensor imaging sampling method, comprising the following steps: A1, scanning the entire field of view in a single pass at the first b-value, sampling to obtain the signal-to-noise ratio of a single scan at the first b-value and placing an order at the first b-value Second scan signal strength; A2, according to the single scan signal-to-noise ratio under the first b value and the multiple scan signal-to-noise ratio specified under the first b value, the number of multiple scans under the first b value is calculated; A3, according to The signal-to-noise ratio of a single scan at the first b value and the signal strength of a single scan at the first b value are calculated to obtain the signal-to-noise ratio of a single scan at the second b value; A4, according to the second b value The number of multiple scans under the second b value is obtained by calculating the single scan signal-to-noise ratio and the multiple scan signal-to-noise ratio specified under the second b value;

The fitting formula of the single scan signal intensity under the first b value described in step A1 is:

${\mathrm{<span\; class="notranslate">\; S</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; S</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}{\mathrm{<span\; class="notranslate">\; e</span>}}^{<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; b</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}\mathrm{<span\; class="notranslate">\; D.</span>}}$

Wherein, S ₁ is the signal intensity of a single scan under the first b value, S ₀ is the signal intensity when there is no diffusion gradient, b ₁ is the first b value, and D is the diffusion coefficient;

The formula for calculating the number of times of multiple scans under the first b value in step A2 is:

$\frac{{\mathrm{<span\; class="notranslate">\; SNR</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}}{\sqrt{\mathrm{<span\; class="notranslate">\; 1</span>}}}<span\; class="notranslate">\; =</span>\frac{{\mathrm{<span\; class="notranslate">\; SNR</span>}}_{\mathrm{<span\; class="notranslate">\; N</span>}\mathrm{<span\; class="notranslate">\; 1</span>}}}{\sqrt{\mathrm{<span\; class="notranslate">\; N</span>}\mathrm{<span\; class="notranslate">\; 1</span>}}}$

Wherein, SNR ₁ is the signal-to-noise ratio of a single scan under the first b value, SNR _N1 is the signal-to-noise ratio of multiple scans under the first b value, and N1 is the number of multiple scans under the first b value;

Step A3 includes the following steps: A31. Calculate the signal strength of a single scan at the second b value according to the signal strength of the single scan at the first b value; A32. Calculate the signal strength of the single scan at the first b value and The relationship between the signal intensity of a single scan at the second b value is calculated to obtain the signal-to-noise ratio of a single scan at the second b value;

The formula for calculating the signal strength of a single scan at the second b value in step A31 is:

$\frac{\mathrm{<span\; class="notranslate">\; ln</span>}{\mathrm{<span\; class="notranslate">\; S</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; ln</span>}{\mathrm{<span\; class="notranslate">\; S</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}}{\mathrm{<span\; class="notranslate">\; ln</span>}{\mathrm{<span\; class="notranslate">\; S</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; ln</span>}{\mathrm{<span\; class="notranslate">\; S</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}}<span\; class="notranslate">\; =</span>\frac{<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; b</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}}{<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; b</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}}$

Wherein, b ₁ is the first b value, b ₂ is the second b value, S ₁ is the signal strength of a single scan under the first b value, and S ₂ is a single scan signal strength under the second b value. Scanning signal intensity, S ₀ is the signal intensity when there is no diffusion gradient;

The fitting formula of the single scan signal intensity under the second b value described in step A31 is:

${\mathrm{<span\; class="notranslate">\; S</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; S</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}{\mathrm{<span\; class="notranslate">\; e</span>}}^{<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; b</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}\mathrm{<span\; class="notranslate">\; D.</span>}}$

Wherein, S ₂ is the signal intensity of a single scan under the second b value, S ₀ is the signal intensity when there is no diffusion gradient, b ₂ is the second b value, and D is the diffusion coefficient;

The formula for the relationship between the single-scan signal-to-noise ratio at the first b value and the single-scan signal-to-noise ratio at the second b value calculated in step A32 is:

$\frac{{\mathrm{<span\; class="notranslate">\; S</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}}{{\mathrm{<span\; class="notranslate">\; S</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}}<span\; class="notranslate">\; =</span>\frac{{\mathrm{<span\; class="notranslate">\; SNR</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}}{{\mathrm{<span\; class="notranslate">\; SNR</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}}$

Wherein, S ₁ is the signal strength of a single scan at the first b value, S ₂ is the signal strength of a single scan at the second b value, and SNR ₁ is the signal-to-noise ratio of a single scan at the first b value , SNR ₂ is the signal-to-noise ratio of a single scan under the second b value;

The formula for calculating the number of times of multiple scans under the second b value in step A4 is:

$\frac{{\mathrm{<span\; class="notranslate">\; SNR</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}}{\sqrt{\mathrm{<span\; class="notranslate">\; 1</span>}}}<span\; class="notranslate">\; =</span>\frac{{\mathrm{<span\; class="notranslate">\; SNR</span>}}_{\mathrm{<span\; class="notranslate">\; N</span>}\mathrm{<span\; class="notranslate">\; 2</span>}}}{\sqrt{\mathrm{<span\; class="notranslate">\; N</span>}\mathrm{<span\; class="notranslate">\; 2</span>}}}$

Wherein, SNR ₂ is the signal-to-noise ratio of a single scan at the second b value, SNR _N2 is the signal-to-noise ratio of multiple scans at the second b value, and N2 is the number of multiple scans at the second b value.

The above multi-b-value diffusion tensor imaging sampling method can adaptively select the number of repeated scans actually required for each b-value by implementing a single pre-scan on the first b-value and the relationship followed by the signal-to-noise ratio of different b-values , thereby reducing the total sampling times, greatly reducing the sampling time, and increasing the speed of diffusion tensor imaging.

【Description of drawings】

Fig. 1 is the flow chart of multi-b-value diffusion tensor imaging sampling method in an embodiment;

Fig. 2 is that in the multi-b-value diffusion tensor imaging sampling method shown in Fig. 1, the second b is calculated according to the single-scan signal-to-noise ratio under the first b-value and the single-scan signal intensity under the first b-value The flow chart of single-scan signal-to-noise ratio at .

【Detailed ways】

The following description will be made in conjunction with the accompanying drawings and specific implementation manners.

As shown in Figure 1, a multi-b-value diffusion tensor imaging sampling method comprises the following steps:

S100. Scan the entire field of view in a single pass at the first b value, and obtain a single-scan signal-to-noise ratio at the first b-value and a single-scan signal intensity at the first b-value by sampling. b is the diffusion factor. The field of view refers to the area that the coil can scan with the area of interest as the center, such as the lesion area, organ area, etc. When sampling, the b value is fixed at a value, and then the coil performs a single scan on the entire field of view to obtain the signal intensity of a single scan at the first b value.

S200. Calculate and obtain the number of multiple scans at the first b value according to the signal-to-noise ratio of a single scan at the first b value and the signal-to-noise ratio of multiple scans at the specified first b value. According to the single-scan signal-to-noise ratio under the first b value and the multi-scan signal-to-noise ratio under the specified first b value, the formula for calculating the number of multiple scans under the first b value is:

$\frac{{\mathrm{<span\; class="notranslate">\; SNR</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}}{\sqrt{\mathrm{<span\; class="notranslate">\; 1</span>}}}<span\; class="notranslate">\; =</span>\frac{{\mathrm{<span\; class="notranslate">\; SNR</span>}}_{\mathrm{<span\; class="notranslate">\; N</span>}\mathrm{<span\; class="notranslate">\; 1</span>}}}{\sqrt{\mathrm{<span\; class="notranslate">\; N</span>}\mathrm{<span\; class="notranslate">\; 1</span>}}}$

Wherein, SNR ₁ is the signal-to-noise ratio of a single scan at the first b value, SNR _N1 is the signal-to-noise ratio of multiple scans at the first b value, and N1 is the number of multiple scans at the first b value.

S300. Calculate and obtain a single-scan signal-to-noise ratio at a second b-value according to the single-scan signal-to-noise ratio at the first b-value and the single-scan signal strength at the first b-value. In this embodiment, the first b value b ₁ is 500 seconds/per square millimeter, and the second b value b ₂ is 750 seconds/per square millimeter. As shown in FIG. 2, step S300 includes the following steps.

S310. Calculate and obtain the single-scan signal strength at the second b-value according to the single-scan signal strength at the first b-value. Since the diffusion process decays exponentially, the fitting formula for the signal intensity of a single scan at the first b value is,

${\mathrm{<span\; class="notranslate">\; S</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; S</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}{\mathrm{<span\; class="notranslate">\; e</span>}}^{<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; b</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}\mathrm{<span\; class="notranslate">\; D.</span>}}$

Wherein, S ₁ is the signal intensity of a single scan at the first b value, S ₀ is the signal intensity when there is no diffusion gradient, b ₁ is the first b value, and D is the diffusion coefficient. Similarly, the fitting formula for the signal intensity of a single scan at the second b value is:

${\mathrm{<span\; class="notranslate">\; S</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; S</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}{\mathrm{<span\; class="notranslate">\; e</span>}}^{<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; b</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}\mathrm{<span\; class="notranslate">\; D.</span>}}$

Among them, S ₂ is the signal intensity of a single scan at the second b value, S ₀ is the signal intensity without a diffusion gradient, b ₂ is the second b value, and D is the diffusion coefficient. The formula for calculating the single-scan signal strength at the second b-value based on the single-scan signal strength at the first b-value is:

$\frac{\mathrm{<span\; class="notranslate">\; ln</span>}{\mathrm{<span\; class="notranslate">\; S</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; ln</span>}{\mathrm{<span\; class="notranslate">\; S</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}}{\mathrm{<span\; class="notranslate">\; ln</span>}{\mathrm{<span\; class="notranslate">\; S</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; ln</span>}{\mathrm{<span\; class="notranslate">\; S</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}}<span\; class="notranslate">\; =</span>\frac{<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; b</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}}{<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; b</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}}$

Among them, b ₁ is the first b value, b ₂ is the second b value, S ₁ is the single scan signal strength under the first b value, S ₂ is the single scan signal strength under the second b value, S ₀ is no Signal intensity when diffusing the gradient.

S320. Calculate and obtain the single-scan signal-to-noise ratio at the second b-value according to the relationship between the single-scan signal intensity at the first b-value and the single-scan signal intensity at the second b-value. According to the relationship between the signal intensity of a single scan at the first b value and the signal intensity of a single scan at the second b value, the formula for the relationship between the signal-to-noise ratio of a single scan at the second b value is:

$\frac{{\mathrm{<span\; class="notranslate">\; S</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}}{{\mathrm{<span\; class="notranslate">\; S</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}}<span\; class="notranslate">\; =</span>\frac{{\mathrm{<span\; class="notranslate">\; SNR</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}}{{\mathrm{<span\; class="notranslate">\; SNR</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}}$

Among them, S ₁ is the signal strength of a single scan at the first b value, S ₂ is the signal strength of a single scan at the second b value, SNR ₁ is the signal-to-noise ratio of a single scan at the first b value, and SNR ₂ is the second The signal-to-noise ratio of a single scan at the b value.

S400. Calculate and obtain the number of multiple scans at the second b value according to the signal-to-noise ratio of a single scan at the second b value and the signal-to-noise ratio of multiple scans at the specified second b value. According to the single-scan signal-to-noise ratio under the second b value and the multi-scan signal-to-noise ratio under the specified second b value, the formula for calculating the number of multiple scans under the second b value is:

$\frac{{\mathrm{<span\; class="notranslate">\; SNR</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}}{\sqrt{\mathrm{<span\; class="notranslate">\; 1</span>}}}<span\; class="notranslate">\; =</span>\frac{{\mathrm{<span\; class="notranslate">\; SNR</span>}}_{\mathrm{<span\; class="notranslate">\; N</span>}\mathrm{<span\; class="notranslate">\; 2</span>}}}{\sqrt{\mathrm{<span\; class="notranslate">\; N</span>}\mathrm{<span\; class="notranslate">\; 2</span>}}}$

Wherein, SNR ₂ is the signal-to-noise ratio of a single scan at the second b value, SNR _N2 is the signal-to-noise ratio of multiple scans at the second b value, and N2 is the number of multiple scans at the second b value.

The multi-b-value diffusion tensor imaging sampling method described above can adaptively select the number of repeated scans actually required for each b-value by performing a single pre-scan on the first b-value and the relationship followed by the signal-to-noise ratio of different b-values ,, thereby reducing the total number of sampling times, greatly reducing the sampling time, and improving the speed of diffusion tensor imaging.

The above-mentioned embodiments only express several implementation modes of the present invention, and the descriptions thereof are relatively specific and detailed, but should not be construed as limiting the patent scope of the present invention. It should be pointed out that those skilled in the art can make several modifications and improvements without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent for the present invention should be based on the appended claims.

Claims (1)

1. the dispersion tensor of b value more than kind imaging method of sampling may further comprise the steps:

A1, under a b value single sweep operation whole visual field, sampling obtain a b value down the single sweep operation noise when a b value descend single sweep signal intensity;

A2, according to a said b value down the single sweep operation noise when time specified snr computation that repeatedly scans of a b value obtain under the b value repeatedly scanning times;

A3, according to a said b value down the when said b value of single sweep operation noise time single sweep signal intensity calculate single sweep operation signal to noise ratio under the 2nd b value;

A4, according to said the 2nd b value down the single sweep operation noise when time specified snr computation that repeatedly scans of the 2nd b value obtain under the 2nd b value repeatedly scanning times;

A b value described in the steps A 1 fitting formula of single sweep signal intensity down is:

$S_1=S_0{e}^{-b_{1}D}$

Wherein, S ₁Be single sweep signal intensity under the said b value, S ₀Signal intensity when not having the disperse gradient, b ₁Be a said b value, D is a dispersion coefficient;

Calculating under the b value repeatedly in the steps A 2, the formula of scanning times is:

$\frac{{\mathrm{SNR}}_1}{\sqrt{1}}=\frac{{\mathrm{SNR}}_{N1}}{\sqrt{N1}}$

Wherein, SNR ₁Be single sweep operation signal to noise ratio under the said b value, SNR _N1For a said b value repeatedly scans down signal to noise ratio, N1 is a scanning times repeatedly under the said b value;

Steps A 3 comprises the steps:

A31, according to a said b value down single sweep signal intensity calculate single sweep signal intensity under the 2nd b value;

A32, according to a said b value down the relation of single sweep signal intensity and said the 2nd b value time single sweep signal intensity calculate single sweep operation signal to noise ratio under said the 2nd b value;

The formula that calculates single sweep signal intensity under the 2nd b value in the steps A 31 is:

$\frac{\ln S_1-\ln S_0}{\ln S_2-\ln S_0}=\frac{-b_1}{-b_2}$

Wherein, b ₁Be a said b value, b ₂Be said the 2nd b value, S ₁Be single sweep signal intensity under the said b value, S ₂Be single sweep signal intensity under said the 2nd b value, S ₀Signal intensity when not having the disperse gradient;

The 2nd b value described in the steps A 31 fitting formula of single sweep signal intensity down is:

$S_2=S_0{e}^{-b_{2}D}$

Wherein, S ₂Be single sweep signal intensity under said the 2nd b value, S ₀Signal intensity when not having the disperse gradient, b ₂Be said the 2nd b value, D is a dispersion coefficient;

The formula that calculates the relation between the single sweep operation signal to noise ratio under said the 2nd b value in the steps A 32 is:

$\frac{S_1}{S_2}=\frac{{\mathrm{SNR}}_1}{{\mathrm{SNR}}_2}$

Wherein, S ₁Be single sweep signal intensity under the said b value, S ₂Be single sweep signal intensity under said the 2nd b value, SNR ₁Be single sweep operation signal to noise ratio under the said b value, SNR ₂Be single sweep operation signal to noise ratio under said the 2nd b value;

Calculating under the 2nd b value repeatedly in the steps A 4, the formula of scanning times is:

$\frac{{\mathrm{SNR}}_2}{\sqrt{1}}=\frac{{\mathrm{SNR}}_{N2}}{\sqrt{N2}}$

Wherein, SNR ₂Be single sweep operation signal to noise ratio under said the 2nd b value, SNR _N2For said the 2nd b value repeatedly scans down signal to noise ratio, N2 is a scanning times repeatedly under said the 2nd b value.

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