Dynamic contrast enhanced magnetic resonance image analysis method suitable for prostate

Abstract

The invention discloses a dynamic contrast enhanced magnetic resonance image analysis method suitable for prostate. The method comprises the following steps: extracting a concentration time signal of a contrast agent in a blood vessel from an iliac artery area of a dynamic contrast enhanced magnetic resonance image; performing nonlinear least square fitting on the DCE-MRI time signal intensity curve of each pixel point on the prostate by using a double-chamber exchange model to obtain a distribution diagram of three groups of physiological parameters of Ktrans, po and kio; and carrying out error analysis on kio to obtain a final distribution diagram of kio parameters and a distribution diagram of Ktrans and po parameters. According to the analysis method, the accuracy of physiological parameter estimation can be improved, and quantitative visual characterization of physiological characteristics of prostate tumors and normal tissues is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of magnetic resonance imaging, and in particular to a dynamic contrast-enhanced magnetic resonance image analysis method applicable to prostate. Background Art

[0002] Prostate cancer is a common malignancy in men. Globally, there are 1.5 million new cases and 397,000 deaths from prostate cancer, making it the second most common cancer in men and the fifth most common in terms of mortality. China is expected to see 134,200 new cases in 2022, creating a serious situation. Research has demonstrated the importance of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for prostate cancer detection (Rosenkrantz et al., 2013). This technique uses dynamic, continuous scanning to capture the dynamic distribution of contrast agents within tissues, extracting signal intensity-time curves that reflect perfusion characteristics at each pixel and enabling visualization and quantitative analysis. Currently, DCE-MRI imaging analysis methods are categorized into two types: semi-quantitative and quantitative. The semi-quantitative approach extracts characteristic parameters from the curve, offering a simple method but lacking physiological interpretation. The semi-quantitative approach, incorporating pharmacokinetic models, fits the signal curve to extract parameters such as tissue microstructure and vascular permeability, enabling more in-depth diagnostic analysis. The accuracy of quantitative analysis is highly dependent on the model selected. The Tofts model is the most widely used classical pharmacokinetic model (Stephanie et al., 2012), but it ignores the effect of water molecule exchange across cell membranes on DCE-MRI signals (Springer et al., 2018), which can easily lead to biased model parameter estimation and incorrect estimation of physiological conditions.

[0003] In recent years, a DCE-MRI quantitative analysis model that includes information on water molecule exchange across cell membranes has been proposed to compensate for the limitation of the classical pharmacokinetic model that ignores the water molecule exchange rate (Bai et al., 2020; Li et al., 2005). Studies have shown that the water exchange rate across cell membranes (k io ) can more accurately reflect tissue metabolic status (Bai et al., 2020). However, DCE-MRI pharmacokinetic models are often tissue or organ specific. The DCE-MRI method developed above primarily targets the brain tumor environment. Pharmacokinetic models that incorporate transcellular water exchange, which is specific to prostate tissue characteristics, including high vascular permeability and high perfusion, are still lacking. Summary of the Invention

[0004] The purpose of the present invention is to provide a quantitative analysis method for dynamic contrast-enhanced magnetic resonance images of the prostate, which improves the contrast agent volume transfer rate K in DCE-MRI. trans and interstitial water mole fraction p oThe estimated accuracy of water exchange across the cell membrane k io A new parameter specifically characterizes the rate of water molecule exchange across cell membranes.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] A dynamic contrast-enhanced magnetic resonance image analysis method for prostate, comprising the following steps:

[0007] (1) Extract the time-varying curve of the contrast agent concentration in the blood vessels from the iliac artery region of the dynamic contrast-enhanced magnetic resonance image as the arterial input function;

[0008] (2) According to the arterial input function of step (1), the DCE-MRI time signal intensity curve of each pixel point in the region of interest of the tissue to be tested is fitted with the double-chamber exchange model nonlinear least square method to obtain three sets of physiological parameters: contrast agent volume transfer rate K trans , interstitial water mole fraction p o and the transcellular membrane water exchange rate k io ; Wherein, the tissue to be tested is the prostate;

[0009] (3) For the k obtained in step (2) io The parameters are analyzed for errors to generate the final three sets of physiological parameters: K trans 、p o and k io .

[0010] In step (2), the two-compartment exchange model 2SX divides the entire tissue space into two compartments: the extracellular interstitial space (subscript o) and the intracellular space (subscript o), ignoring the vascular space, which accounts for a small proportion and is highly permeable to contrast agents: the molar fractions of water molecules in the interstitial space and the intracellular space are p and p, respectively. o and p i There is water molecule exchange between compartments, that is, the exchange process of water molecules inside and outside the cell membrane, and the outflow rate of water molecules from inside the cell to outside the cell k io Describe the process of contrast agent flowing from blood vessels to extracellular matrix using the volume transfer rate K trans describe;

[0011] The 2SX model assumes that the system is in dynamic equilibrium and the finite rate exchange process of water molecules on the cell membrane satisfies the principle of microscopic reversibility. The water molecule exchange rate and the water molecule molar fraction satisfy:

[0012] k io p i =k oi p o

[0013] Among them, k io represents the rate of water molecules flowing out of the cell, k oi represents the influx rate from the extracellular space to the intracellular space, p o represents the molar fraction of water in the interstitial space, p i represents the molar fraction of water in the cell; ignoring the proportion of blood vessels, p o +p i =1.

[0014] In step (2), specifically:

[0015] (2-1) Calculate the time-varying curve of the contrast agent concentration in the tissue, denoted as C(t);

[0016] (2-2) Based on C(t), the function of the longitudinal relaxation rate of the interstitial space changing with time is calculated and recorded as R 1o (t);

[0017] (2-3) According to the Bloch equation, R 1o (t) is substituted into the analytical solution of the coupling equation to obtain two apparent relaxation rates R 1L and R 1S , and the corresponding apparent coefficient a L and a S ;

[0018] (2-4) According to R 1L 、R 1S 、a L and a S , obtain the time signal intensity curve S(t) fitted by the dual-chamber exchange model;

[0019] (2-5) Compare the signal intensity S(t) of the dual-chamber exchange model fitting with the DCE-MRI time signal intensity curve obtained by scanning to determine whether the fitting result meets the nonlinear least squares fitting error requirement. If so, the p value of the dual-chamber exchange model fitting for the corresponding pixel point is obtained. o , K trans 、k io optimal value;

[0020] (2-6) Repeat the above steps (2-1)-(2-5) to obtain the p value of the double-chamber exchange model fitting for the entire prostate region of interest. o , K trans 、k io Parameter distribution plot.

[0021] In step (2-1), the arterial input function AIF, i.e., the time-varying curve of the contrast agent concentration in the blood vessel, is represented by Cp(t), the time signal of the contrast agent concentration in the entire tissue is represented by C(t), and the contrast agent concentration in the interstitial space is represented by Co (t) indicates that the relationship among the three satisfies:

[0022]

[0023] Among them, v o is the volume fraction of the interstitial space and is linearly proportional to p o :v o =p o f w , f w is the volume percentage of free water molecules, C o (t) is the contrast agent concentration in the interstitial space, t is the time period, and u is the duration.

[0024] Among them, f w It can be set to a fixed value of 0.80.

[0025] In step (2-2), since the proportion of vascular volume is ignored, the longitudinal relaxation rate R 1o (t) is linearly related to the concentration of contrast agent in the tissue C(t), satisfying:

[0026] R 1o (t) = R 10 +C(t)r1

[0027] where R 10 is the initial longitudinal relaxation rate of the tissue before contrast agent is injected, which is also equal to the longitudinal relaxation rate R in the intracellular space. 1i , R 10 Obtained from the corresponding longitudinal relaxation time T1 quantitative image, r1 is the longitudinal relaxation rate of the contrast agent.

[0028] Here, r1 is determined by the type of contrast agent used.

[0029] In step (2-3), the 2SX model takes into account the change in longitudinal relaxation rate caused by contrast agent exchange and the water molecule exchange effect into the Bloch equation, and the formula for the change in magnetization intensity in the two compartments of the intracellular space and the interstitial space is as follows. According to the Bloch equation, the longitudinal magnetization vector M in the intracellular and interstitial space is i and M o satisfy:

[0030]

[0031] Among them, R 1i is the longitudinal relaxation rate of the intracellular space, R 1o (t) is the longitudinal relaxation rate of the interstitial space. Solving the equation, we can get two apparent relaxation rates R 1L and R 1S, and two coefficients a L and a S . R 1L and R 1S Does not describe a specific physical component.

[0032] In step (2-3), R 1L 、R 1S 、a L and a S Satisfies the following formula:

[0033]

[0034] Among them, R 1i is the longitudinal relaxation rate of the intracellular space, R 1o (t) is the longitudinal relaxation rate of the interstitial space.

[0035] In step (2-4), for DCE-MRI based on gradient spin echo sequence, the sequence signal intensity S(t) is composed of the signals S of two components with different apparent relaxation rates. L and S S Combined, both signals satisfy the gradient spin echo sequence signal formula (ignoring the influence of transverse relaxation):

[0036] S(t)=S0(a L S L +a S S S )

[0037]

[0038] where TR and α are the repetition time and flip angle of the gradient spin echo sequence, respectively.

[0039] In step (2-5), if the fitting result does not meet the nonlinear least squares fitting error requirement, the p set in the dual-chamber exchange model is adjusted according to the parameter fitting range and the nonlinear least squares algorithm iteration. o , K trans and k io The initial values ​​of the three parameters are adjusted until the fitting results meet the nonlinear least squares fitting error requirements.

[0040] As a preference, in step (2): remove the pixels with longitudinal relaxation time T1 value greater than 3500 milliseconds in the region of interest, and exclude the interference of non-tissue areas such as the urethra; in the least squares fitting, the calculation time of the model fitting can be reduced by setting a suitable iteration step, maximum number of iterations and suitable parameter fitting range; in order to avoid the fitting falling into a local optimal solution, a multi-starting point fitting method can be used, such as in the fitting of K trans 、po 、k io Set multiple groups of different initial values, use multiple processes to perform fitting, and select the quantitative parameters with the smallest fitting residual as the final result.

[0041] In step (3), only the 95% confidence intervals in [0s -1 , 6s -1 ] interval or the lower limit of the 95% confidence interval is greater than 6s -1 The pixel result is:

[0042] Pixels with a 95% confidence interval that is too large (i.e., k io From 0s -1 to 20s -1 The range of values ​​of χ 2 Increased to reach 95% confidence level) is excluded, but the K of this pixel trans and p o For those values ​​that have a wide confidence interval but still give a higher lower limit (>6s -1 ) pixels are not excluded, and their k io Assigned a high exchange rate (6s -1 ), although the exact value of this exchange rate is difficult to determine. io In the case of high estimates, k is set in the final parameter map. io The value is set to 6s -1 .

[0043] k io The specific method for error analysis is: fix k io The other parameters are fitted by 2SX model based on nonlinear least squares method, and then the value is calculated in [0s -1 20s -1 ]Change k within the interval io The fitting is repeated until the following values ​​are satisfied:

[0044]

[0045] Among them, χ 2 It is here io The fitted chi-square value at the value, is the chi-square value of the fit after optimizing all parameters, F is the F distribution function, K is the independent parameter of the fitted model, N is the number of measurement points of the DCE-MRI data, and 0.95 is the confidence interval.

[0046] The invention also discloses the application of the method to dynamic contrast-enhanced magnetic resonance images of the prostate.

[0047] Compared with the prior art, the present invention has the following excellent effects:

[0048] The present invention provides a method for quantitative analysis of prostate DCE-MRI (a quantitative analysis method applicable to dynamic contrast-enhanced magnetic resonance images of the prostate): by constructing a two-chamber exchange model (2SX), this method is more consistent with real physiological characteristics, more accurately describes DCE-MRI signals, and corrects the systematic deviations existing in the classic DCE-MRI quantitative analysis model; this method can quantitatively measure the volume transfer rate K of the prostate region. trans , interstitial water mole fraction p o , transcellular membrane water exchange rate k io Three physiological parameters; this method measures the rate of water exchange across the cell membrane, k io By introducing this parameter that conforms to the physiological reality, the contrast agent volume transfer rate K is improved. trans and interstitial water mole fraction p o The estimation accuracy can achieve quantitative visualization of the physiological characteristics of prostate tumors and normal tissues; this method can also remove k with large estimation errors through error analysis. io value, further improving the reliability of parameter estimation. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 Schematic diagram of the two-compartment exchange model (2SX);

[0050] Figure 2 The specific fitting process for the two-compartment exchange model (2SX);

[0051] Figure 3 This is a parameter diagram showing the DCE-MRI data of a prostate cancer patient after being fitted with the 2SX model in the embodiment;

[0052] Figure 4 The ΔAIC of the two models after the DCE-MRI data of prostate cancer patients in the embodiment were processed by the Tofts model and the 2SX model. c picture. DETAILED DESCRIPTION

[0053] In order to make the purpose, technical solutions and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings and examples.

[0054] Step 1: Extract the time-varying curve of the contrast agent concentration in the blood vessel from the iliac artery region of the dynamic contrast-enhanced magnetic resonance image as the arterial input function.

[0055] Step 2: Use the double-chamber exchange model to fit the signal intensity-time curve of each pixel in the prostate region of interest to obtain p o , Ktrans 、k io Three parameters, the specific fitting process is as follows Figure 2 As shown:

[0056] (1) First, the DCE-MRI time signal intensity curve signal corresponding to the pixel point, the longitudinal relaxation time T1 value and the arterial input function AIF (i.e., C p (t)).

[0057] (2) Set p in 2SX model o , K trans 、k io Initial values ​​and fitting ranges of the three parameters. In this embodiment, the initial values ​​of the three parameters are 0.2, 0.1min -1 、3s -1 , the fitting ranges are 0.001~0.8,10 -5 ~1 minute -1 ,0~20s -1 .

[0058] (3) Change p o , K trans 、k io Substitute the parameter values ​​into the 2SX model.

[0059] (4) The time-varying curve of the contrast agent concentration in the tissue is calculated according to the following formula, which is represented by C(t):

[0060]

[0061] Among them, v o is the volume fraction of the interstitial space and is linearly proportional to p o (v o =p o f w ), f w is the volume fraction of free water molecules (here it is fixed at 0.80), where t is the time period and u is the duration.

[0062] (5) The longitudinal relaxation rate of the interstitial space is calculated as a function of time according to the following formula, denoted as R 1o (t), since the proportion of blood vessel volume is ignored, it is linearly related to the concentration of contrast agent in the tissue C(t), satisfying:

[0063] R 1o (t) = R 10 +C(t)r1

[0064] where R 10is the initial longitudinal relaxation rate of the tissue before contrast agent is injected, which is also equal to the longitudinal relaxation rate R in the intracellular space. 1i , which is obtained from the corresponding longitudinal relaxation time T1 quantitative image, and r1 is the longitudinal relaxation rate of the contrast agent, which is determined by the type of contrast agent used.

[0065] (6) According to the Bloch equation, substituting it into the analytical solution of the coupling equation, we can obtain two apparent relaxation rates (R 1L and R 1S ) Different components and their proportion coefficients a L and a S . R 1L and R 1S It does not describe a specific component in the physical sense, but corresponds to the parameter combination in the two-chamber exchange model 2SX, which specifically satisfies the following formula:

[0066]

[0067] a S =1-a L

[0068] Among them, R 1i is the longitudinal relaxation rate of the intracellular space, R 1o (t) is the longitudinal relaxation rate of the interstitial space.

[0069] (7) Substitute into R 1L 、R 1S 、a L 、a S , the time signal intensity curve S(t) fitted by the 2SX model is obtained. For DCE-MRI based on gradient spin echo sequence, the sequence signal intensity S(t) is composed of the signal S of two components with different apparent relaxation rates. L and S S Combined, both signals satisfy the gradient spin echo sequence signal formula (ignoring the influence of transverse relaxation):

[0070] S(t)=S0(a L S L +a S S S )

[0071]

[0072] where TR and α are the repetition time and flip angle of the gradient spin echo sequence, respectively.

[0073] (8) Compare the signal intensity S(t) obtained by fitting the 2SX model with the DCE-MRI time signal intensity curve obtained by scanning. Determine whether the fitting result meets the nonlinear least squares fitting error requirements.

[0074] (9) If step (8) is not satisfied, adjust p according to the parameter fitting range and nonlinear least squares algorithm iteration. o , K trans 、k io The values ​​of the three parameters start again from step (3) until they meet the requirements of step (8).

[0075] (10) If step (8) is satisfied, the p of the 2SX model fitting for this pixel can be obtained. o , K trans 、k io The optimal value, through the above method, can be obtained for the 2SX model fitting of the entire prostate region of interest. o , K trans 、k io Parameter distribution plot.

[0076] Step 3: Since k io The fitting error is large, and this step is to correct the k obtained in step 2. io Parameter error analysis was performed, and pixels with a 95% confidence interval that was too large were deleted, and only pixels with a 95% confidence interval between [0s -1 , 6s -1 ] interval or the lower limit of the 95% confidence interval is greater than 6s -1 The pixel result of k is eliminated io k of pixels that may have large errors io value, and get the final k io Distribution plot of parameters and K trans 、p o Distribution plot of the parameters.

[0077] In error analysis, k io The 95% confidence interval of is determined by: fixing k io The other parameters are fitted by 2SX model based on nonlinear least squares method, and then the value is calculated in [0s -1 20s -1 ]Change k within the interval io The fitting is repeated until the following values ​​are satisfied:

[0078]

[0079] Among them, X 2 It is here io The fitted chi-square value at the value, is the chi-square value of the fit after optimizing all parameters, F is the F distribution function, K is the independent parameter of the fitted model, and N is the number of measurement points of the DCE-MRI data.

[0080] Figure 3is the p obtained after fitting the DCE-MRI data of a prostate cancer patient processed according to this embodiment with the 2SX model. o , K trans 、k io Parameter maps and enhanced images.

[0081] Performance verification: In order to compare the fitting performance of the 2SX model proposed in this invention with the Tofts model commonly used to analyze DCE-MRI data of the prostate, the modified Akaike information criterion (AIC) of the two models was calculated. c , the formula is as follows:

[0082]

[0083] Where N is the number of time points in the voxel signal, RSS is the residual sum of the model fit, p is the number of free parameters in the model, N is equal to the free parameters of the model, and AIC is c The smaller the value, the more accurately the model fits the real signal. c Defined as AIC of Tofts model and 2SX model c The difference is as follows:

[0084] ΔAIC c =AIC c (Tofts)-AIC c (2SX)

[0085] ΔAIC c >0, indicating that the 2SX model has a better fit than the Tofts model at that pixel; otherwise, it indicates that the Tofts model has a better fit. Figure 4 The ΔAIC calculated after fitting the Tofts model and the 2SX model to the DCE-MRI data of a prostate cancer patient is shown. c Figure, ΔAIC c The majority of pixels are >0, indicating that the 2SX model fits the DCE-MRI data of the prostate better.

[0086] The specific implementation methods described above provide a detailed description of the technical solutions and beneficial effects of the present invention. It should be understood that the above is only the most preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, supplements and equivalent substitutions made within the scope of the principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A dynamic contrast-enhanced magnetic resonance image analysis method for prostate, characterized in that: The method comprises the following steps: (1) Extract the time-varying curve of the contrast agent concentration in the blood vessels from the iliac artery region of the dynamic contrast-enhanced magnetic resonance image as the arterial input function; (2) According to the arterial input function of step (1), the DCE-MRI time signal intensity curve of each pixel point in the region of interest of the tissue to be tested is fitted with the double-chamber exchange model nonlinear least square method to obtain three sets of physiological parameters: contrast agent volume transfer rate K trans , interstitial water mole fraction p o and the transcellular membrane water exchange rate k io ; Wherein, the tissue to be tested is the prostate; (3) For the k obtained in step (2) io The parameters are analyzed for errors to generate the final three sets of physiological parameters: K trans 、p o and k io .

2. The dynamic contrast-enhanced magnetic resonance image analysis method for prostate according to claim 1, characterized in that: In step (2), the two-compartment exchange model divides the entire tissue space into two compartments: the extracellular interstitial space and the intracellular space, ignoring the vascular space: the molar fractions of water molecules in the interstitial space and the intracellular space are p o and p i There is water molecule exchange between compartments, and the efflux rate of water molecules from the cell to the outside of the cell is k io Describe the process of contrast agent flowing from blood vessels to extracellular matrix using the volume transfer rate K trans describe; The water molecule exchange rate and water molecule molar fraction satisfy: k io p i =k oi p o Among them, k io represents the rate of water molecules flowing out of the cell, k oi represents the influx rate from the extracellular space to the intracellular space, p o represents the molar fraction of water in the interstitial space, p i represents the molar fraction of water in the cell; ignoring the proportion of blood vessels, p o +p i =1.

3. The dynamic contrast-enhanced magnetic resonance image analysis method for prostate according to claim 2, characterized in that: In step (2), specifically: (2-1) Calculate the time-varying curve of tissue contrast agent concentration, denoted as C(t); (2-2) Based on c(t), calculate the function of the longitudinal relaxation rate of the interstitial space as a function of time, denoted as R 1o (t); (2-3) According to the Bloch equation, R 1o (t) is substituted into the analytical solution of the coupling equation to obtain two apparent relaxation rates R 1L and R 1S , and the corresponding apparent coefficient a L and a S ; (2-4) According to R 1L 、R 1S 、a L and a S , obtain the time signal intensity curve S(t) fitted by the dual-chamber exchange model; (2-5) Compare the signal intensity curve S(t) fitted by the dual-chamber exchange model with the DCE-MRI time signal intensity curve obtained by scanning to determine whether the fitting result meets the nonlinear least squares fitting error requirement. If so, the p value of the dual-chamber exchange model fitting for the corresponding pixel point is obtained. o , K trans 、k io optimal value; (2-6) Repeat the above steps (2-1)-(2-5) to obtain the p value of the double-chamber exchange model fitting for the entire prostate region of interest. o , K trans 、k io Parameter distribution plot.

4. The dynamic contrast-enhanced magnetic resonance image analysis method for prostate according to claim 3, characterized in that: In step (2-1), C(t) is expressed as: Among them, v o is the volume fraction of the interstitial space and is linearly proportional to p o :v o =p o f w , f w is the volume percentage of free water molecules, C o (t) is the contrast agent concentration in the interstitial space, t is the time period, and u is the duration.

5. The dynamic contrast-enhanced magnetic resonance image analysis method for prostate according to claim 3, characterized in that: In step (2-2), since the proportion of vascular volume is ignored, the longitudinal relaxation rate of the interstitial space R 1o (t) is linearly related to the concentration of contrast agent C(t), satisfying: R 1o (t)=R 10 +C(t)r1 where R 10 is the initial longitudinal relaxation rate of the tissue before contrast agent is injected, which is also equal to the longitudinal relaxation rate R in the intracellular space. 1i , R 10 Obtained from the corresponding longitudinal relaxation time T1 quantitative image, r1 is the longitudinal relaxation rate of the contrast agent.

6. The dynamic contrast-enhanced magnetic resonance image analysis method for prostate according to claim 3, characterized in that: In step (2-3), R 1L 、R 1S 、a L and a S Satisfies the following formula: Among them, R 1i is the longitudinal relaxation rate of the intracellular space, R 1o (t) is the longitudinal relaxation rate of the interstitial space.

7. The dynamic contrast-enhanced magnetic resonance image analysis method for prostate according to claim 3, characterized in that: In step (2-4), for DCE-MRI based on gradient spin echo sequence, the sequence signal intensity curve S(t) is composed of two components with different apparent relaxation rates, signal S L and S S Combined, both signals satisfy the gradient spin echo sequence signal formula: S(t)=S0(a L S L +a S S S ) where TR and α are the repetition time and flip angle of the gradient spin echo sequence, respectively.

8. The dynamic contrast-enhanced magnetic resonance image analysis method for prostate according to claim 3, characterized in that: In step (2-5), if the fitting result does not meet the nonlinear least squares fitting error requirement, the p set in the dual-chamber exchange model is adjusted according to the parameter fitting range and the nonlinear least squares algorithm iteration. o , K trans and k io The initial values ​​of the three parameters are adjusted until the fitting results meet the nonlinear least squares fitting error requirements.

9. The dynamic contrast-enhanced magnetic resonance image analysis method for prostate according to claim 1, characterized in that: In step (3), only the 95% confidence intervals in [0s -1 , 6s -1 ] interval or the lower limit of the 95% confidence interval is greater than 6s -1 The pixel result.

10. The dynamic contrast-enhanced magnetic resonance image analysis method for prostate according to claim 9, characterized in that: Specifically: Fixed k io The other parameters are fitted by 2SX model based on nonlinear least squares method, and then the value is calculated in [0s -1 20s -1 ]Change k within the interval io The fitting is repeated until the following values ​​are satisfied: Among them, χ 2 It is here io The fitted chi-square value at the value, is the chi-square value of the fit after optimizing all parameters, F is the F distribution function, K is the independent parameter of the fitted model, N is the number of measurement points of the DCE-MRI data, and 0.95 is the confidence interval.

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