Tissue pure dispersion measurement method and device based on slow dispersion coefficient

Abstract

The invention discloses a tissue pure dispersion measurement method and device based on a slow dispersion coefficient, and the method comprises the steps: building b value distribution, carrying out the dispersion imaging magnetic resonance scanning at each b value based on an MRI scanner, obtaining an image corresponding to each b value, and enabling the b values to reflect the intensity of a dispersion imaging gradient magnetic field; images corresponding to a first b value and a second b value are determined respectively, image signal intensity values of the first b value and the second b value are obtained, and the first b value and the second b value are both higher than a preset value; a preset calculation and analysis program is called, a slow dispersion coefficient is obtained based on the image signal intensity values of the first b value and the second b value, and the slow dispersion coefficient is used for reflecting the movement condition of dispersed water molecules in the tissue and organ. According to the invention, the slow dispersion coefficient is provided, and the slow dispersion coefficient can more truly reflect the movement condition of dispersed water molecules in tissues and organs.

Description

Technical Field

[0001] The present invention relates to the technical field of magnetic resonance diffusion imaging, and in particular, to a method and device for measuring pure diffusion of tissue based on a slow diffusion coefficient. Background Art

[0002] In magnetic resonance diffusion imaging, currently, the movement rate of water molecules in living tissues is represented by the Apparent Diffusion Coefficient (ADC), which is used to quantify the diffusion movement of water molecules in tissues. A diffusion imaging series generally consists of an imaging without a diffusion imaging gradient magnetic field at first (b = 0) and an imaging with one or several diffusion imaging gradient magnetic fields of different intensities (or different durations) (such as b = 50 s / mm 2 , 800 s / mm 2 ). The higher the diffusion imaging gradient magnetic field (the larger the b value), the lower the signal of the imaging. The relationship between the larger b value and the lower image signal is related to the speed of water molecule movement in the tissue. The faster the water molecules move, the faster the image signal decreases.

[0003] In the prior art, the calculation formula of ADC is as follows:

[0004]

[0005] where b2 and b1 respectively represent the high b value and the low b value, and S(b2) and S(b1) respectively represent the image signal intensities acquired at the high b value and the low b value. When the low b value is 0, the ADC is calculated as follows

[0006]

[0007] where b2 and b0 respectively represent the high b value and b = 0, and S(b0) and S(b2) respectively represent the image signal intensities acquired when the b value is b = 0 and the high b value.

[0008] Although theoretically, ADC can reflect the degree of water molecule diffusion activity in living tissues, providing information for disease diagnosis and pathological mechanism research. For example, in cerebral infarction, liver fibrosis, etc., the degree of water molecule diffusion activity in the tissue decreases. However, in practice, the measurement of ADC is severely interfered by the T2 relaxation time of the tissue and often cannot reliably measure the diffusion in living tissues. The ADC measured in tissues with extremely short or extremely long T2 will be falsely elevated. For example, most hepatocellular carcinomas are related to increased blood supply, increased proportion of arterial blood supply, and higher water content (i.e., edema, manifested as increased signal on T2-weighted images and decreased density on X-ray computed tomography). However, the ADC value of liver cancer is lower than that of liver tissue. Compared with liver tissue, the spleen contains more water (longer T2 time, higher free water content, lower density on CT images). It is more reasonable that the spleen has higher tissue diffusion, but in the actual measurement process, the ADC value of the spleen is lower than that of the liver. As Figure 2 shown in the colon cancer liver metastasis patient in Figure 2 , the fat-suppressed T2-weighted image (A) shows a high-signal focus indicated by the arrow; the ADC pixel map (B) shows a low signal of the small metastasis (arrow). The spleen shows a high signal on the T2-weighted image, indicating more water. However, the spleen with more water shows a significantly low signal on the ADC pixel map, indicating diffusion limitation, which does not conform to the physiological characteristics. The blood supply of colon cancer liver metastasis is generally fast in and fast out, with increased blood flow velocity. The high signal on T2W during this period indicates edema. It can be seen that the low signals of the spleen and colon cancer liver metastasis on the ADC pixel map do not conform to the physiological characteristics. Therefore, ADC cannot correctly and reasonably reflect the degree of water molecule movement activity in tissues and organs.

[0009] Therefore, there are still defects in the prior art. Summary of the Invention

[0010] The technical problem to be solved by the present invention is to provide a method and device for measuring pure diffusion of tissues based on the slow diffusion coefficient in view of the above-mentioned defects of the prior art. The technical solutions adopted by the present invention are as follows:

[0011] In the first aspect, an embodiment of the present invention further provides a method for measuring pure diffusion of tissues based on the slow diffusion coefficient. The method includes:

[0012] Establish a b-value distribution, and perform diffusion-weighted magnetic resonance scanning at each b-value based on an MRI scanner to obtain an image corresponding to each b-value, where the b-value reflects the intensity of the diffusion-weighted gradient magnetic field;

[0013] Respectively determine the images corresponding to the first b-value and the second b-value, and obtain the image signal intensity values of the first b-value and the second b-value, where both the first b-value and the second b-value are higher than a preset value;

[0014] Call a preset calculation and analysis program to obtain a slow diffusion coefficient based on the image signal intensity values of the first b value and the second b value, where the slow diffusion coefficient is used to reflect the movement of diffusing water molecules in an organ.

[0015] In one implementation, the preset value is 200 s / mm 2 , and both the first b value and the second b value are greater than 200 s / mm 2 .

[0016] In one implementation, the interval difference between the first b value and the second b value is not significantly greater than a threshold.

[0017] In one implementation, the calculation formula of the calculation and analysis program is:

[0018] SDC = [SI(b1) – SI(b2)] / (b2 – b1), where SDC is the slow diffusion coefficient, b1 is the first b value, b2 is the second b value, and SI(b1) and SI(b2) are the image signal intensity values corresponding to the first b value and the second b value.

[0019] In one implementation, for establishing the b value distribution and performing diffusion - weighted magnetic resonance imaging scans at each b value by an MRI scanner to obtain an image corresponding to each b value, where the b value reflects the intensity of the diffusion - weighted gradient magnetic field, it further includes:

[0020] Perform multiple diffusion - weighted magnetic resonance imaging scans at each b value by the MRI scanner, collect the image signal intensity values of each scan, and take the average to identify the image signal intensity value at that b value.

[0021] In one implementation, for obtaining the image signal intensity values of the first b value and the second b value, it includes:

[0022] Respectively determine regions of interest from the image of the first b value and the image of the second b value;

[0023] Measure the signal intensity from the region of interest of the image of the first b value and measure the signal intensity from the region of interest of the image of the second b value to obtain the image signal intensity values of the first b value and the second b value.

[0024] In a second aspect, an embodiment of the present invention further provides a tissue pure diffusion measurement device based on a slow diffusion coefficient. The device is used to implement the steps of the tissue pure diffusion measurement method based on a slow diffusion coefficient described in the above solution. The device includes:

[0025] A scanning imaging module, which is used to establish a b-value distribution, and perform diffusion-weighted magnetic resonance imaging scans at each b-value based on an MRI scanner to obtain an image corresponding to each b-value, where the b-value reflects the intensity of the diffusion imaging gradient magnetic field;

[0026] An image signal determination module, which is used to respectively determine the images corresponding to the first b-value and the second b-value, and obtain the image signal intensity values of the first b-value and the second b-value, where both the first b-value and the second b-value are higher than a preset value;

[0027] A slow diffusion coefficient determination module, which is used to call a preset calculation and analysis program, and obtain a slow diffusion coefficient based on the image signal intensity values of the first b-value and the second b-value, where the slow diffusion coefficient is used to reflect the movement of diffusing water molecules in tissues and organs.

[0028] In one implementation, the preset value is 200 s / mm 2 and both the first b-value and the second b-value are greater than 200 s / mm 2 ;

[0029] The interval difference between the first b-value and the second b-value is not significantly greater than the threshold;

[0030] The calculation formula of the calculation and analysis program is:

[0031] SDC = [SI(b1) – SI(b2)] / (b2 – b1), where SDC is the slow diffusion coefficient, b1 is the first b-value, b2 is the second b-value, and SI(b1) and SI(b2) are the image signal intensity values corresponding to the first b-value and the second b-value.

[0032] In a third aspect, an embodiment of the present invention further provides a terminal, where the terminal includes a memory, a processor, and a tissue pure diffusion measurement program based on the slow diffusion coefficient stored in the memory and executable on the processor. When the processor executes the tissue pure diffusion measurement program based on the slow diffusion coefficient, the steps of the tissue pure diffusion measurement method based on the slow diffusion coefficient in any one of the above solutions are implemented.

[0033] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, where a tissue pure diffusion measurement program based on the slow diffusion coefficient is stored on the computer-readable storage medium. When the tissue pure diffusion measurement program based on the slow diffusion coefficient is executed by a processor, the steps of the tissue pure diffusion measurement method in any one of the above solutions are implemented.

[0034] Beneficial effects: Compared with the prior art, the present invention provides a method for measuring pure diffusion of tissue based on a slow diffusion coefficient. First, the present invention establishes a b-value distribution, and performs diffusion-weighted magnetic resonance imaging scans at each b-value based on an MRI scanner to obtain an image corresponding to each b-value, where the b-value reflects the intensity of the diffusion-weighted gradient magnetic field. Then, the images corresponding to the first b-value and the second b-value are respectively determined, and the image signal intensity values of the first b-value and the second b-value are obtained, where both the first b-value and the second b-value are higher than a preset value. Finally, a preset calculation and analysis program is called, and based on the image signal intensity values of the first b-value and the second b-value, a slow diffusion coefficient is obtained, and the slow diffusion coefficient is used to reflect the movement of diffusing water molecules in tissue organs. The present invention proposes a slow diffusion coefficient, which can more truly reflect the movement of diffusing water molecules in tissue organs. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is a flowchart of a preferred embodiment of the method for measuring pure diffusion of tissue based on a slow diffusion coefficient provided by an embodiment of the present invention.

[0036] Figure 2 It is a fat-suppressed T2-weighted image and an ADC pixel map of a patient with liver metastasis of colon cancer.

[0037] Figure 3 It is a schematic diagram of the relationship between spleen DDVD and liver DDVD under different b-value conditions.

[0038] Figure 4 It is a schematic diagram of the signal intensity of liver diffusion imaging under different b-value conditions.

[0039] Figure 5 It is a T2-weighted magnetic resonance image, an ADC pixel map and an SDC pixel map of the liver and liver cancer tissue in the first example.

[0040] Figure 6 It is an ADC pixel map and an SDC pixel map of the liver and liver cancer tissue in the second example.

[0041] Figure 7 It is an SDC pixel map of the liver and spleen.

[0042] Figure 8 It is an ADC pixel map and an SDC pixel map for reflecting cysts and hemangiomas.

[0043] Figure 9 It is a schematic diagram of the relationship between spleen ADC and liver ADC under different b-value conditions.

[0044] Figure 10 It is a schematic diagram of the architecture of the device for measuring pure diffusion of tissue based on a slow diffusion coefficient provided by an embodiment of the present invention.

[0045] Figure 11 This is a schematic block diagram of the terminal provided by an embodiment of the present invention. Detailed implementation manners

[0046] To make the objectives, technical solutions and effects of the present invention clearer and more definite, the following further describes the present invention in detail with reference to the accompanying drawings and by way of examples. It should be understood that the specific embodiments described herein are only used to explain the present invention, but not to limit the present invention.

[0047] The flowcharts shown in the accompanying drawings are only illustrative examples, and do not necessarily include all the content, operations or steps, nor do they necessarily need to be executed in the described order. For example, some operations or steps can also be decomposed, combined or partially merged, so the actual execution order may be changed according to the actual situation.

[0048] It should be understood that the terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the specification of the present invention and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include the plural forms.

[0049] It should be understood that, for the convenience of clearly describing the technical solutions of the embodiments of the present invention, in the embodiments of the present invention, terms such as "first" and "second" are used to distinguish the same items or similar items with basically the same functions and effects. For example, the first control information and the second control information are only used to distinguish different control information, and do not limit their sequence.

[0050] Those skilled in the art can understand that the terms such as "first" and "second" do not limit the quantity and execution order, and the terms such as "first" and "second" do not necessarily mean different.

[0051] It should also be understood that the term " / and" used in the specification of the present invention and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0052] In the prior art, magnetic resonance diffusion imaging is used to observe the diffusion-derived vessel density (DDVD) in vivo. The DDVD tissue microperfusion (in vivo vessel density) can be measured by a diffusion-weighted imaging-derived marker:

[0053] DDVD(b0b2) = Sb0 / ROIarea0 - Sb2 / ROIarea2, where ROIarea0 and ROIarea2 respectively represent b = 0 s / mm 2and b = 2 s / mm 2 The number of pixels in the selected region of interest (ROI) on the image. Sb0 is the total signal intensity measured within the image ROI when b = 0 s / mm 2 and Sb2 is the total signal intensity measured within the image ROI when b = 2 s / mm 2 Therefore, Sb / ROIarea is equal to the average signal intensity within the ROI. Sb2 and ROIarea2 can also be approximately replaced with data from other low b-value diffusion images. DDVD can be interpreted as a physiological marker of the microvascular area per unit tissue area, and if multiple sections are integrated, it can conceptually be converted into a marker of the microvascular volume per voxel. Based on this, this embodiment provides a method for measuring pure diffusion of tissue based on the slow diffusion coefficient, which can be applied to a terminal, and the terminal can be an intelligent product terminal such as a computer or a mobile phone. Specifically, as Figure 1 shown in

[0054] Step S100, establish a b-value distribution, and perform diffusion imaging magnetic resonance scanning at each b-value based on an MRI scanner to obtain an image corresponding to each b-value, where the b-value reflects the intensity of the diffusion imaging gradient magnetic field;

[0055] Step S200, respectively determine the images corresponding to the first b-value and the second b-value, and obtain the image signal intensity values of the first b-value and the second b-value, where both the first b-value and the second b-value are higher than a preset value;

[0056] Step S300, call a preset calculation and analysis program, and obtain a slow diffusion coefficient based on the image signal intensity values of the first b-value and the second b-value, where the slow diffusion coefficient is used to reflect the movement of diffusing water molecules within an organ tissue.

[0057] In specific applications, this embodiment can use magnetic resonance diffusion imaging to observe the movement speed of water molecules in organ tissues. Since after applying an external diffusion imaging gradient magnetic field to the tissue, the more freely the water molecules move, the faster the speed and the more the image signal drops. This external diffusion imaging gradient magnetic field is represented by the b-value, with the unit of s / mm 2 (seconds per square millimeter). The larger the b-value, the higher the gradient magnetic field intensity and the more the image signal drops. The movement speed of water molecules in the tissue includes fast-moving water molecules (blood perfusion) within the vascular structure and slower-moving water molecules (diffusion) within normal cells or cell gaps, where the latter is caused by Brownian motion.

[0058] Based on the prior art, the ratio of spleen DDVD to liver DDVD is observed using the DDVD method. Combining Figure 3 as shown in Figure 3The Y-axis in the figure is the spleen DDVD value divided by the liver DDVD value. The blue line is the result of 1.5T magnetic resonance scanning, and the blue line is the result of 3.0T magnetic resonance scanning. When calculating DDVD, the first b value is 0, and the second b value is the value shown on the X-axis. DDVD is obtained by subtracting the magnetic resonance signal intensity at b = 0 from the magnetic resonance signal intensity at a higher b value. However, in principle, the second b value is required to be a low b value. In this embodiment, an attempt is also made to measure DDVD with a relatively large second b value. Analysis of existing literature shows that the blood flow velocity of the spleen is slightly higher than that of the liver, while the vascular volume of the spleen is slightly lower than that of the liver. When DDVD(b0b1), its value is contributed by blood flow velocity and vascular volume, and the spleen DDVD(b0b1) is slightly larger than the liver DDVD(b0b1). Between DDVD(b0b2) and DDVD(b0b30), its value is mainly contributed by vascular volume. Therefore, the spleen DDVD is slightly lower than the liver DDVD, which is consistent with the existing literature. For the DDVD results with b values greater than 30, the meaning of DDVD diffusing vascular volume is actually lost because the component reflecting tissue diffusion becomes heavier. After the b value is greater than 200, it can be seen that the spleen DDVD is again and significantly higher than the liver DDVD, which is consistent with the prediction results of physiology because the spleen contains more free water and the diffusion of water molecules is more free.

[0059] Based on this, this embodiment proposes a method for measuring pure diffusion of tissues based on the slow diffusion coefficient. Specifically, this embodiment pre-sets a b value distribution, where the b value represents the intensity of the diffusion imaging gradient magnetic field, and then performs diffusion imaging magnetic resonance scanning at each b value based on an MRI (Magnetic Resonance Imaging) scanner, and outputs an image corresponding to each b value. For example, 2 b values are set. For example, b = 400, 600 s / mm 2 . Preferably, when imaging, in this embodiment, multiple diffusion imaging magnetic resonance scans can be performed at each b value, and the number of excitations or the number of signal averaging greater than 1 (number of excitations or number of signal averaging > 1) is used to identify the image signal at that b value, overcoming the instability of magnetic resonance diffusion imaging, thereby improving the quality of the image signal collected at each b value.

[0060] Next, in this embodiment, regions of interest are respectively determined from the image with the first b-value and the image with the second b-value. Then, the signal intensity is measured from the region of interest of the image with the first b-value, and the signal intensity is measured from the region of interest of the image with the second b-value, to obtain the image signal intensity values of the first b-value and the second b-value. Further, in this embodiment, the images corresponding to the first b-value and the second b-value are respectively determined, and the image signal intensity values of the first b-value and the second b-value are obtained. In this embodiment, both the first b-value and the second b-value are higher than a preset value. In practical applications, this preset value is 200 s / mm 2 , therefore, both the first b-value and the second b-value are greater than 200 s / mm 2 . Moreover, the interval difference between the first b-value and the second b-value is not significantly greater than the threshold value, that is to say, the first b-value and the second b-value are not very different. After determining the first b-value and the second b-value, this embodiment can call a preset calculation and analysis program, and based on the image signal intensity values of the first b-value and the second b-value, obtain the slow diffusion coefficient, which is used to reflect the motion of diffusing water molecules in tissues and organs.

[0061] Specifically, in this embodiment, the calculation formula of the calculation and analysis program is:

[0062] SDC = [SI(b1) – SI(b2)] / (b2 – b1), where SDC is the slow diffusion coefficient, the unit is arbitrary unit (au) / s, b1 is the first b-value, b2 is the second b-value, and SI(b1) and SI(b2) are the image signal intensity values corresponding to the first b-value and the second b-value.

[0063] In this embodiment, the first b-value for SDC calculation needs to be at least greater than 200 s / mm 2 or other large b-values, such as Figure 3 shown in. According to the theory of Intra Voxel Incoherent Motion (IVIM), when the b-value is greater than 200 s / mm 2After that, the information provided by the rapid blood perfusion of the tissue is very little, and it mainly reflects the diffusion of water molecules (because the rapid perfusion information is removed, and the signal reflects the slow movement of water molecules. This parameter is the Slow Diffusion Coefficient, that is, the slow diffusion coefficient, and the corresponding blood perfusion is called fast Diffusion). Moreover, in order to reduce the T2 relaxation effect in the signal difference between two b values, the difference between the two b values cannot be too large. However, if the difference between the two b values is too small, the noise in the SDC calculation will be too high. Generally speaking, for structures with longer T2, such as body fluids, the T2 relaxation effect is more obvious. The too high noise in the SDC calculation can be compensated by increasing the number of acquisitions of magnetic resonance signals. Therefore, in order to achieve high-quality SDC, users need to flexibly design the signal acquisition. In this embodiment, the interval difference between the first b value and the second b value is controlled to be less than a threshold value, for example, the threshold value is 300 s / mm 2 , so the first b value can be selected as 500 s / mm 2 , and the second b value is 800 s / mm 2 . As Figure 4 shown Figure 4 , it is a schematic diagram of the signal intensity of liver diffusion imaging under different b values. The larger the b value, the lower the signal. As Figure 4 shown in the right figure of Figure 4 , the region of interest of the liver is shown in yellow and avoids the vascular structures in the liver. 2 The available b values in 2 are 500 s / mm

[0064] For example, as Figure 5 shown in Figure 5 , in Figure A of Figure 5 , it is a T2-weighted magnetic resonance image, in which the liver cancer mass shows a significantly high signal compared with the surrounding liver tissue, indicating edema in the liver cancer tissue. The spleen also shows a high signal compared with the liver, indicating that the water content of the spleen is higher than that of the liver tissue. 2 In the figure, B is an ADC pixel map (calculated from b1 = 0 and b2 = 600 s / mm Figure 5 , showing that the ADC values of liver cancer and spleen are lower than those of the liver tissue. 2 In Figure C of 2 is an SDC pixel map (calculated from b1 = 400 s / mm

[0065] ADC 肝癌 / ADC 肝脏 = 0.822, ADC 脾脏 / ADC 肝脏= 0.811,

[0066] SDC 肝癌 / SDC 肝脏 = 5.48, SDC 脾脏 / SDC 肝脏 = 6.11,

[0067] Thus, it can be seen that the SDC value of liver cancer calculated according to SDC is greater than that of the surrounding liver. Most hepatocellular carcinomas are related to increased blood supply, increased proportion of arterial blood supply, and edema, and their diffusion should be much higher than that of liver tissue. Compared with the liver, the spleen tissue contains more free water, and its physiological diffusion is higher than that of liver tissue. Therefore, SDC can better reflect the real physiological tissue water molecule movement situation.

[0068] The pure diffusion measurement method of tissue based on SDC proposed in this embodiment is different from the method based on ADC. The two b values calculated by ADC generally have a relatively large interval. For example, use 0 and 800 s / mm 2 , or 50 s / mm 2 and 800 s / mm 2 . While the interval between the two b values calculated by SDC is generally required to be relatively small. For example, 400 s / mm 2 and 600 s / mm 2 .

[0069] Taking another example, as Figure 6 shown, Figure 6 in Figure A is an ADC pixel map (calculated from b = 0 and b = 600 s / mm 2 ), showing that the ADC values of liver cancer and spleen are lower than those of liver tissue. Figure 6 in Figure B is an SDC pixel map (calculated from b = 400 and b = 600 s / mm 2 ). Most hepatocellular carcinomas are related to increased blood supply, increased proportion of arterial blood supply, and edema, and their diffusion should be much higher than that of liver tissue. It can be seen that SDC shows that the diffusion value of hepatocellular carcinoma is higher than that of the liver, while the ADC pixel map is just the opposite. Therefore, SDC can better reflect the real physiological tissue water molecule movement situation.

[0070] Figure 7 is the SDC pixel map of the liver and spleen (Figure B is calculated from b = 400 s / mm 2 and b = 600 s / mm 2 ). Compared with the liver, the spleen tissue contains more free water, and its physiological diffusion is higher than that of liver tissue. It can be seen that the spleen value on SDC is higher than that of the liver. Therefore, SDC can reflect the real physiological tissue water molecule movement situation, which is in line with the physiological characteristics that the spleen has more free water than the liver.

[0071] Such asFigure 8 As shown in Figure 8 the ADC pixel maps (i.e., Figure A and Figure C), the diffusion of cysts shows higher, while on the SDC pixel maps (i.e., Figure B and Figure D), the signal of hemangiomas shows higher. Compared with the background liver, the SDC of cysts is also a high signal, but the signal is higher on ADC. Compared with the background liver, the ADC of hemangiomas is also a high signal, but the signal is higher on SDC. Due to the presence of blood flow in hemangiomas, it is reasonable that the movement of water molecules in hemangiomas is faster than that in cysts. Therefore, it is more reasonable for SDC to show high diffusion in hemangiomas. The high signal of ADC will be affected by the T2 relaxation time. The T2 relaxation time of cysts is generally higher than that of hemangiomas. Therefore, the high signal on the ADC of cysts is affected by its particularly long T2 time. As can be seen from Figure 8 SDC is more superior in measuring diffusion.

[0072] Figure 9 As shown in Figure 9 the Y-axis in 2 is the ADC value of the spleen divided by the ADC value of the liver. The blue line is the result of 1.5T magnetic resonance scanning, and the blue line is the result of 3.0T magnetic resonance scanning. Based on the existing ADC calculation formula, the low b-values are 1, 2, 4, 7 s / mm 2 etc. (excluding 0), the high b-value of 3.0T is 600 s / mm 2 . Figure 9 It is shown in 2 that when using two high b-values to calculate ADC, such as using 400 s / mm 2 and 600 s / mm

[0073] the ADC of the spleen is still lower than that of the liver, which does not conform to the physiological characteristics.

[0074] Therefore, the measurement of the diffusion movement of water molecules in organs based on the SDC parameter proposed in this embodiment will have great clinical applications. Although the examples in this embodiment are limited to the liver and spleen, SDC can be used in any organ, tissue, and various pathological conditions in the body. SDC can also be used in combination with existing diffusion parameters to increase the overall efficiency for the qualitative analysis of living tissues. For example, the combination of DDVD and IVIM parameters can increase the discrimination of liver fibrosis and also improve the subtype classification of gliomas.

[0074] Based on the above embodiments, the present invention also provides a tissue pure diffusion measurement device based on the slow diffusion coefficient, which is used to implement the steps of the above method embodiments, specifically as Figure 10As shown in the figure, the device of this embodiment includes: a scanning imaging module 10, an image signal determination module 20, and a slow diffusion coefficient determination module 30. Specifically, the scanning imaging module 10 is configured to establish a b-value distribution and perform diffusion imaging magnetic resonance scanning based on an MRI scanner at each b-value to obtain an image corresponding to each b-value, where the b-value reflects the intensity of the diffusion imaging gradient magnetic field. The image signal determination module 20 is configured to respectively determine the images corresponding to the first b-value and the second b-value, and obtain the image signal intensity values of the first b-value and the second b-value, where both the first b-value and the second b-value are higher than a preset value. The slow diffusion coefficient determination module 30 is configured to call a preset calculation and analysis program, and obtain a slow diffusion coefficient based on the image signal intensity values of the first b-value and the second b-value, where the slow diffusion coefficient is used to reflect the movement of diffused water molecules in a tissue or organ.

[0075] In one implementation, the preset value of this embodiment is 200 s / mm 2 , and both the first b-value and the second b-value are greater than 200 s / mm 2 ; the interval difference between the first b-value and the second b-value is not significantly greater than a threshold; the calculation formula of the calculation and analysis program is:

[0076] SDC = [SI(b1) – SI(b2)] / (b2 – b1), where b1 is the first b-value, b2 is the second b-value, and SI(b1) and SI(b2) are the image signal intensity values corresponding to the first b-value and the second b-value.

[0077] The working principles of the modules in the tissue pure diffusion measurement device based on the slow diffusion coefficient in this embodiment are the same as those of the steps in the above method embodiment, and will not be elaborated here.

[0078] Each module in the above tissue pure diffusion measurement device based on the slow diffusion coefficient can be implemented in whole or in part by software, hardware, and their combination. The above modules can be embedded in the processor in the terminal in hardware form or be independent of the processor, or can be stored in the memory in the terminal in software form, so as to facilitate the processor to call and execute the operations corresponding to the above modules.

[0079] Based on the above embodiments, the present invention also provides a terminal, and the principle block diagram of the terminal can be as Figure 11 shown. The terminal may include one or more processors 100( Figure 11Only one is shown in the figure), a memory 101, and a computer program 102 stored in the memory 101 and executable on one or more processors 100. For example, a tissue pure diffusion measurement program based on a slow diffusion coefficient. When the one or more processors 100 execute the computer program 102, each step in the embodiment of the tissue pure diffusion measurement method based on the slow diffusion coefficient can be implemented. Alternatively, when the one or more processors 100 execute the computer program 102, the functions of each module / unit in the embodiment of the tissue pure diffusion measurement device based on the slow diffusion coefficient can be implemented, which is not limited herein.

[0080] In one embodiment, the so-called processor 100 may be a central processing unit (CPU), or may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), off-the-shelf programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor, or the processor may also be any conventional processor, etc.

[0081] In one embodiment, the memory 101 may be an internal storage unit of the electronic device, such as the hard disk or memory of the electronic device. The memory 101 may also be an external storage device of the electronic device, such as a plug-in hard disk equipped on the electronic device, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. Further, the memory 101 may also include both an internal storage unit and an external storage device of the electronic device. The memory 101 is used to store the computer program and other programs and data required by the terminal. The memory 101 may also be used to temporarily store the data that has been output or will be output.

[0082] Those skilled in the art can understand that Figure 11 The principle block diagram shown in the figure is only a block diagram of a part of the structure related to the solution of the present invention, and does not constitute a limitation on the terminal to which the solution of the present invention is applied. The specific terminal may include more or fewer components than those shown in the figure, or combine certain components, or have a different component layout.

[0083] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, operational database, or other medium used in the embodiments provided by the present invention can include non-volatile and / or volatile memories. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM), etc.

[0084] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments or equivalently replace some of the technical features. These modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for measuring pure diffusion of tissue based on a slow diffusion coefficient, characterized in that The method includes: Establishing a b-value distribution, and performing diffusion-weighted magnetic resonance imaging (DWI) scans at each b-value based on an MRI scanner to obtain an image corresponding to each b-value, where the b-value reflects the intensity of the diffusion-weighted gradient magnetic field; Respectively determining the images corresponding to the first b-value and the second b-value, and obtaining the image signal intensity values of the first b-value and the second b-value, where both the first b-value and the second b-value are higher than a preset value; Invoking a preset calculation and analysis program, and obtaining a slow diffusion coefficient based on the image signal intensity values of the first b-value and the second b-value, where the slow diffusion coefficient is used to reflect the movement of diffusing water molecules in tissues and organs.

2. The tissue pure diffusion measurement method based on slow diffusion coefficient according to claim 1, characterized in that: The preset value is 200 s / mm 2 , and both the first b value and the second b value are greater than 200 s / mm 2 .

3. The tissue pure diffusion measurement method based on the slow diffusion coefficient according to claim 1, characterized in that The interval difference between the first b-value and the second b-value is not significantly greater than a threshold.

4. The tissue pure diffusion measurement method based on slow diffusion coefficient according to claim 1, characterized in that: The calculation formula of the calculation and analysis program is: SDC = [SI(b1) – SI(b2)] / (b2 – b1), where SDC is the slow diffusion coefficient, b1 is the first b-value, b2 is the second b-value, and SI(b1) and SI(b2) are the image signal intensity values corresponding to the first b-value and the second b-value.

5. The tissue pure diffusion measurement method based on the slow diffusion coefficient according to claim 1, wherein The step of establishing a b-value distribution, and performing diffusion-weighted magnetic resonance imaging (DWI) scans at each b-value based on an MRI scanner to obtain an image corresponding to each b-value, where the b-value reflects the intensity of the diffusion-weighted gradient magnetic field, further includes: Performing multiple diffusion-weighted magnetic resonance imaging (DWI) scans at each b-value through an MRI scanner, collecting the image signal intensity values of each scan, and taking the average to identify the image signal intensity value at that b-value.

6. The method for measuring pure diffusion of tissue based on the slow diffusion coefficient according to claim 1, wherein The step of obtaining the image signal intensity values of the first b-value and the second b-value includes: Respectively determining regions of interest from the image of the first b-value and the image of the second b-value; Measuring the signal intensity from the region of interest of the image of the first b-value, and measuring the signal intensity from the region of interest of the image of the second b-value to obtain the image signal intensity values of the first b-value and the second b-value.

7. An apparatus for measuring pure diffusion of tissue based on a slow diffusion coefficient, characterized in that The device is used to implement the steps of the tissue pure diffusion measurement method based on the slow diffusion coefficient according to any one of claims 1-6. The device includes: A scanning and imaging module, configured to establish a b-value distribution, and perform diffusion-weighted magnetic resonance imaging (DWI) scans at each b-value based on an MRI scanner to obtain an image corresponding to each b-value, where the b-value reflects the intensity of the diffusion-weighted gradient magnetic field; An image signal determination module, configured to respectively determine the images corresponding to the first b-value and the second b-value, and obtain the image signal intensity values of the first b-value and the second b-value, where both the first b-value and the second b-value are higher than a preset value; A slow diffusion coefficient determination module, configured to invoke a preset calculation and analysis program, and obtain a slow diffusion coefficient based on the image signal intensity values of the first b-value and the second b-value, where the slow diffusion coefficient is used to reflect the movement of diffusing water molecules in tissues and organs.

8. The tissue pure diffusion measurement device based on the slow diffusion coefficient according to claim 7, characterized in that, The preset value is 200 s / mm 2 , both the first b value and the second b value are greater than 200 s / mm 2 ; The interval difference between the first b-value and the second b-value is not significantly greater than a threshold; The calculation formula of the calculation and analysis program is: SDC = [SI(b1) – SI(b2)] / (b2 – b1), where SDC is the slow diffusion coefficient, b1 is the first b value, b2 is the second b value, and SI(b1) and SI(b2) are the image signal intensity values corresponding to the first b value and the second b value.

9. A terminal, characterized in that, The terminal includes a memory, a processor, and a tissue pure diffusion measurement program based on the slow diffusion coefficient stored in the memory and executable on the processor. When the processor executes the tissue pure diffusion measurement program based on the slow diffusion coefficient, the steps of the tissue pure diffusion measurement method based on the slow diffusion coefficient according to any one of claims 1-6 are implemented.

10. A computer-readable storage medium, characterized in that, A tissue pure diffusion measurement program based on the slow diffusion coefficient is stored on the computer-readable storage medium. When the tissue pure diffusion measurement program based on the slow diffusion coefficient is executed by the processor, the steps of the tissue pure diffusion measurement method based on the slow diffusion coefficient according to any one of claims 1-6 are implemented.

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