CN112331259A - Tissue metabolite information evaluation method, device and medium based on Bloch-McConnell equation simulation - Google Patents
Tissue metabolite information evaluation method, device and medium based on Bloch-McConnell equation simulation Download PDFInfo
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Abstract
The invention discloses a tissue metabolite information evaluation method, device and medium based on Bloch-McConnell equation simulation. According to the invention, the contribution of metabolites to CEST signals of normal tissues and lesion tissues is evaluated through two-stage Bloch-McConnell simulation, and the defects of large calculated amount and unstable result of a commonly adopted full-parameter Bloch-McConnell fitting method are avoided. And evaluating the contribution of the parameters to the normal tissue CEST signals and the lesion abnormal CEST signals according to the contribution percentage of the metabolites in the first stage, the minimum root mean square error of the simulation difference values in the second stage and the experimental difference values and the corresponding parameter values. The method provided by the invention can evaluate the contribution of the metabolite to the CEST signal in the normal tissue and the lesion tissue more quickly and stably, and can provide a research method for evaluating the change of the metabolite in the lesion area.
Description
Technical Field
The invention relates to the field of magnetic resonance Chemical Exchange Saturation Transfer (CEST) imaging image analysis, in particular to a living tissue metabolite information evaluation method based on two-stage simulation of a Bloch-McConnell equation.
Background
Magnetic resonance chemical exchange saturation transfer imaging can reflect metabolite information in tissues, and researches show that CEST signals in lesion areas of many diseases show abnormally high or abnormally low compared with signals of normal tissues. However, due to the complexity of biological tissues, the analysis of the contribution of potential metabolites from the obtained CEST images has been a difficult point. Many studies have been conducted to evaluate metabolite information by multi-pool fitting of Bloch-McConnell equation, however, this method usually requires a lot of raw data, the calculation process is complicated and time-consuming, and it is difficult to calculate stable parameter values because the multi-parameter fitting solution is not unique.
Disclosure of Invention
The invention aims to provide a tissue metabolite information evaluation method based on Bloch-McConnell equation simulation, which is performed based on simulation, avoids huge calculation amount and instability of results required by fitting, and can relatively quantitatively evaluate metabolite information in normal tissues and lesion tissues.
In order to achieve the purpose, the invention adopts the following technical scheme to realize the purpose:
in a first aspect, the invention provides a tissue metabolite information evaluation method based on Bloch-McConnell equation simulation, which comprises the following steps:
s1: aiming at the tissue type to be evaluated, acquiring the interested region of normal tissue from the CEST image of the target individual, and respectively converting the CEST image signal into MTRasymSpectrum to obtain the first MTR of normal tissueasymA spectrum;
s2: from all candidates for MTRasymDetermining a group of optimal multi-pool parameter combinations representing parameter values of normal tissues in multi-pool parameter combinations of spectrum Bloch-McConnell simulation, and enabling a second MTR obtained through simulationasymSpectrum closest to the first MTRasymA spectrum;
s3: based on the optimal multi-pool parameter combination, sequentially generating each generation in normal tissuesMTR with metabolite as target productasymSpectrum Bloch-McConnell simulation; when each target product is simulated, the third MTR of the target product is calculated through simulationasymFourth MTR in the absence of the target product and the spectraasymThe spectrum is obtained, and then the MTR before and after deletion of each metabolite is obtainedasymThe signal difference of the spectrum is used as the CEST signal contributed by the metabolite;
s4: and taking the absolute value of the CEST signal contributed by each metabolite as the contribution degree characterization of the corresponding metabolite to the CEST signal of the normal tissue to obtain the contribution degrees of different metabolites to the CEST signal of the normal tissue.
In a second aspect, the invention provides a tissue metabolite information evaluation method based on Bloch-McConnell equation simulation, which comprises the following steps:
s1: aiming at the tissue type to be evaluated, acquiring the interested regions of normal tissue and lesion tissue from a CEST image of a target individual, and respectively converting CEST image signals into MTRs (maximum Transmission Rate) signalsasymSpectrum to obtain the first MTR of normal tissueasymSecond MTR of spectra and focal tissuesasymSpectrum at first MTRasymSpectrum and second MTRasymThe signal difference of the spectrum is used as a first CEST contrast;
s2: from all candidates for MTRasymDetermining a group of optimal multi-pool parameter combinations representing parameter values of normal tissues in multi-pool parameter combinations of spectrum Bloch-McConnell simulation, and enabling a third MTR obtained through simulationasymSpectrum closest to the first MTRasymA spectrum;
s3: based on the optimal multi-pool parameter combination, sequentially taking each parameter in a parameter set of CEST signal contribution to be evaluated as a variable parameter to perform MTRasymSpectrum Bloch-McConnell simulation; when each variable parameter is simulated, the rest parameters in the optimal multi-pool parameter combination are kept unchanged, and fourth MTRs corresponding to different values of the variable parameter in a value change range are obtained through simulation calculationasymSpectrum at fourth MTRasymSpectra and the third MTRasymSignal difference of spectrum as second CEST pairA ratio; calculating the root mean square error of second CEST contrast and first CEST contrast corresponding to different values in the value variation range of each variable parameter, and determining the minimum root mean square error;
s4: and aiming at each parameter in the parameter set, the minimum root mean square error is used as a positive correlation contribution degree characterization of the parameter to the abnormal change of the CEST signal of the lesion tissue, and the contribution degrees of different parameters in the parameter set to the abnormal change of the CEST signal of the lesion tissue of the target individual are obtained.
In a third aspect, the invention provides a tissue metabolite information evaluation method based on Bloch-McConnell equation simulation, which comprises the following steps:
s1: aiming at the tissue type to be evaluated, acquiring the interested regions of normal tissue and lesion tissue from a CEST image of a target individual, and respectively converting CEST image signals into MTRs (maximum Transmission Rate) signalsasymSpectrum to obtain the first MTR of normal tissueasymSecond MTR of spectra and focal tissuesasymSpectrum at first MTRasymSpectrum and second MTRasymThe signal difference of the spectrum is used as a first CEST contrast;
s2: from all candidates for MTRasymDetermining a group of optimal multi-pool parameter combinations representing parameter values of normal tissues in multi-pool parameter combinations of spectrum Bloch-McConnell simulation, and enabling a third MTR obtained through simulationasymSpectrum closest to the first MTRasymA spectrum;
s3: based on the optimal multi-pool parameter combination, sequentially taking each metabolite in normal tissues as a target product to perform MTR of the first stageasymSpectrum Bloch-McConnell simulation; when each target product is simulated, the fourth MTR when the target product exists is calculated through simulationasymSpectrum and fifth MTR in absence of the target productasymThe spectrum is obtained, and then the MTR before and after deletion of each metabolite is obtainedasymThe signal difference of the spectrum is used as the CEST signal contributed by the metabolite;
s4: based on the optimal multi-pool parameter combination, sequentially taking each parameter in a parameter set of CEST signal contribution to be evaluated as a variableQuantitative parameters, MTR for the second phaseasymSpectrum Bloch-McConnell simulation; when each variable parameter is simulated, the rest parameters in the optimal multi-pool parameter combination are kept unchanged, and sixth MTRs corresponding to different values of the variable parameter in a value change range are obtained through simulation calculationasymSpectrum at sixth MTRasymSpectra and the third MTRasymThe signal difference of the spectrum is used as a second CEST contrast; calculating the root mean square error of second CEST contrast and first CEST contrast corresponding to different values in the value variation range of each variable parameter, and determining the minimum root mean square error;
s5: evaluating the contribution of the parameter to the normal tissue CEST signal and the lesion-abnormality CEST signal separately in two aspects with respect to the results obtained in the first stage and the second stage, wherein:
for the result obtained in the first stage, the absolute value of the CEST signal contributed by each metabolite is used as the contribution degree characterization of the corresponding metabolite to the CEST signal of the normal tissue, and the contribution degrees of different metabolites to the CEST signal of the normal tissue are obtained;
and regarding the result obtained in the second stage, regarding each parameter in the parameter set, taking the minimum root mean square error as a positive correlation contribution characterization of the parameter to the abnormal change of the CEST signal of the lesion tissue, and obtaining the contribution of different parameters in the parameter set to the abnormal change of the CEST signal of the lesion tissue of the target individual.
It should be noted that the preceding references of "first", "second", "third", etc. in the description of the different aspects of the present invention are only used for distinguishing purposes, but do not indicate their importance or other meanings. In the technical scheme of different aspects, the preamble marks are not related, and the preamble marks in each aspect are self-organized.
As a further preferred mode of the first, second, and third aspects, the converting the CEST image signal into MTR is performed separatelyasymThe specific method of the spectrum is as follows:
firstly, CEST signals corresponding to different saturation frequencies Δ w are calculated according to the following formula:
wherein: s (- Δ w) is the signal intensity corresponding to the saturation frequency- Δ w, S (Δ w) is the signal intensity corresponding to the saturation frequency Δ w, S0Is the signal strength in the absence of saturation;
then, the MTR at the saturation frequency Δ w in the region of interest is calculatedasymMTR of mean, different saturation frequencies Δ wasymMean value formation MTRasymSpectra.
As a further preferred mode of the first, second, and third aspects, the method for determining the optimal multi-pool parameter combination includes:
aiming at each group of multi-pool parameter combination to be selected, through MTRasymSpectrum Bloch-McConnell simulation to obtain a group of MTRsasymSpectra, then calculating the MTR obtained by simulationasymSpectra and the first MTRasymPearson's correlation coefficient between spectra; selecting MTR with highest Pearson correlation coefficient and obtained through simulationasymSpectra and the first MTRasymAnd a group of multi-pool parameter combinations with the nearest spectrum are used as the optimal multi-pool parameter combination.
As a further preferred feature of the second and third aspects, the set of parameters includes the concentration of each metabolite pool, and the longitudinal relaxation time T of the free water pool1And transverse relaxation time T2And (4) parameters.
As a further preferable mode of the second and third aspects, according to the degree of contribution of different parameters in the parameter set to the abnormal CEST signal change of the lesion tissue of the individual target, the parameter with the largest degree of contribution is determined as the most probable parameter causing the abnormal CEST signal change of the lesion tissue of the individual target.
As a further preferred mode of the first and third aspects, the contribution degree of the different metabolites to the CEST signal of normal tissue is converted into percentage form, and the percentage of the contribution degree distinguishes between positive and negative.
In a fourth aspect, the invention provides a tissue metabolite information evaluation device based on Bloch-McConnell equation simulation, which comprises a memory and a processor;
the memory for storing a computer program;
the processor is used for realizing the tissue metabolite information assessment method based on Bloch-McConnell equation simulation in any scheme when the computer program is executed.
In a fifth aspect, the present invention provides a computer-readable storage medium, wherein the storage medium stores a computer program, and when the computer program is executed by a processor, the method for evaluating tissue metabolite information based on Bloch-McConnell equation simulation according to any one of the above aspects is implemented.
Compared with the prior art, the invention has the following good effects: according to the invention, the contribution of metabolites to CEST signals of normal tissues and lesion tissues is evaluated through two-stage Bloch-McConnell simulation, and the defects of large calculated amount and unstable result of a commonly adopted full-parameter Bloch-McConnell fitting method are avoided. The first phase of the simulation was performed on normal tissue, with and without some metabolite in the simulation MTRasymTo obtain the MTR produced by the metaboliteasymAnd then the contribution percentage is obtained. And the simulation of the second stage is carried out aiming at the metabolite change in the lesion tissue, a certain parameter value is changed on the basis of the simulation of the normal tissue of the first stage, and the minimum root mean square error between the CEST contrast between the lesion and the normal tissue and the real experiment contrast caused by the parameter value change is searched in a simulation mode. The smaller the minimum root mean square error corresponding to the parameter is, the closer the CEST contrast obtained by the parameter change is to the real contrast, so that the larger the contribution of the parameter to the CEST contrast is reflected. The method provided by the invention can evaluate the contribution of the metabolite to the CEST signal in the normal tissue and the lesion tissue more quickly and stably, and can provide a research method for evaluating the change of the metabolite in the lesion area.
Drawings
FIG. 1 is a general flow chart of a two-stage simulation living tissue metabolite information evaluation method based on a Bloch-McConnell equation provided by the invention.
FIG. 2(a) shows the MTR of the normal brain tissue acquired by the 3T magnetic resonance CEST experiment provided by the present inventionasymA drawing; FIG. 2(b) is a 7-pool simulated MTR of normal brain tissueasymFigure (a).
FIG. 3(a) is a MTR of brain nodes and normal brain tissue of patients with tuberous sclerosis, which is collected by experiments provided by the present inventionasymFIG. 3(b-e) shows the parameter T1、T2And the variation of the concentration of the macromolecule pool and the concentration of the amido group ensures that the simulation MTR under the condition of minimum root mean square error between the simulation CEST contrast ratio and the experiment real CEST contrast ratioasymFigure (a).
Detailed Description
The invention will be further elucidated and described with reference to the drawings and the detailed description. The technical features of the embodiments of the present invention can be combined correspondingly without mutual conflict.
Magnetic resonance Chemical Exchange Saturation Transfer (CEST) imaging, which can reflect changes in metabolites in tissues, has been used in the diagnosis of a variety of diseases. However, obtaining tissue metabolite information from CEST images has been a difficult problem due to the complexity of biological tissues and the instability of multi-parameter fits. Aiming at the problem, the invention designs a living tissue metabolite information evaluation method based on Bloch-McConnell equation two-stage simulation, which mainly comprises the following steps: first, the MTR of the lesion obtained by magnetic resonance scanning at a plurality of saturation frequencies is calculatedasymValue and Normal tissue MTRasymThe value is obtained. Then, according to the MTR of the normal tissueasymSpectrum, a set of multi-pool simulation parameters suitable for normal tissue is selected. The first stage of normal tissue simulation follows, and for a certain metabolite, at a certain saturation frequency, the MTR simulated when the metabolite is presentasymValue minus the simulated MTR without the metaboliteasymValues, as the contribution of the metabolite to the CEST signal at the saturation frequency of normal tissue, the contribution of the plurality of metabolites is calculated and the respective percentage of the contribution is obtained. Second stage targeting MTR in lesion tissueasymSpectral simulation based onSelecting a normal tissue simulation parameter, changing one parameter within the range of 20% -500% of the parameter value of the normal tissue, fixing other parameters unchanged, and simulating MTR in the simulated focusasymSpectra and calculating the MTR at different saturation frequencies between the simulated lesion and normal tissueasymAnd (4) difference, and changing the parameters until the root mean square error between the simulation difference and the difference obtained by the real experiment is minimum. And finally, evaluating the contribution of the parameters to normal tissue CEST signals and lesion abnormal CEST signals according to the contribution percentage of the metabolites in the first stage, the minimum root mean square error of the simulation difference values in the second stage and the experimental difference values and the corresponding parameter values. However, the two-stage evaluation process can be performed step by step or simultaneously, and can be selected according to actual conditions.
Referring to FIG. 1, in accordance with a preferred embodiment of the present invention, a method for evaluating metabolite information of living tissue based on two-stage simulation of Bloch-McConnell equation is provided, which evaluates contributions of parameters such as metabolite of living tissue to normal CEST signal and lesion abnormal CEST signal in two aspects. The specific method comprises the following steps:
s1: acquiring a region of interest (ROI) of normal tissue and lesion tissue from a CEST image of a target individual aiming at a tissue type to be evaluated, and respectively converting CEST image signals into MTRs (maximum likelihood ratio)asymSpectrum to obtain the first MTR of normal tissueasymSecond MTR of spectra and focal tissuesasymSpectrum at first MTRasymSpectrum and second MTRasymThe signal difference of the spectra is taken as the first CEST contrast.
In this step, the type of tissue to be evaluated is the corresponding focal tissue, depending on the type of disease to be evaluated, such as brain tissue. The CEST image of the individual of interest is a CEST imaging of the object under evaluation, acquired by a magnetic resonance imaging device.
In the invention, the CEST image signals in the ROI are respectively converted into MTRs (maximum transmission rate) according to the CEST image signalsasymThe specific method of spectrum is selected as follows:
firstly, CEST signals corresponding to different saturation frequencies Δ w are calculated according to the following formula:
wherein: s (- Δ w) is the signal intensity corresponding to the saturation frequency- Δ w, S (Δ w) is the signal intensity corresponding to the saturation frequency Δ w, S0Is the signal strength in the absence of saturation;
then, the MTR of different voxels at the saturation frequency Δ w in the region of interest is calculatedasymMean value, MTR by different saturation frequencies Δ wasymThe mean value can be used to draw the MTRasymSpectra. Therefore, the MTR of the region of interest of the normal tissue and the lesion tissue of the patient at different saturation frequencies can be calculated in the stepasymMean and obtain the first MTR of normal tissueasymSpectrum (as MTR)asym(Normal tissue)) and second MTR of focal tissueasymSpectrum (as MTR)asym(lesions)). The first CEST contrast calculation can therefore be expressed as Δ MTRExperiment of＝MTRasym(Focus) -MTRasym(normal tissue).
S2: the parameters of each pool of the living normal tissues in the existing literature are set or summarized according to experience, and all the candidate MTRs are constructed by using the known parameters in combinationasymMTR is carried out by multi-pool parameter combination of spectrum Bloch-McConnell simulationasymAnd (5) performing spectrum simulation calculation. Determining a group of optimal multi-pool parameter combinations representing parameter values in normal tissues from all the multi-pool parameter combinations to be selected, wherein the optimal multi-pool parameter combinations can enable a third MTR obtained through simulation to be opposite to other multi-pool parameter combinationsasymSpectrum closest to first MTRasymSpectra.
In practical application, the method can be based on each group of multi-pool parameter combinations to be selected, and MTR is passed under the parametersasymSpectrum Bloch-McConnell simulation calculation obtains a group of MTRsasymSpectra, then calculate the set of simulated MTRsasymMTR of spectra and normal tissuesasymSpectrum (i.e., the aforementioned first MTR)asymSpectrum) of the spectrum; selecting MTR with highest Pearson correlation coefficient and obtained through simulationasymThe spectrum and the positiveFrequently organized MTRasymAnd a group of multi-pool parameter combinations with the nearest spectrum are used as the optimal multi-pool parameter combination. The Bloch-McConnell equations used for the simulation can be summarized as:
m is a vector for describing the evolution of each proton pool along with time, and the A and B matrixes contain information such as saturation pulse intensity, exchange rate of each proton pool, longitudinal relaxation rate of each proton pool and the like; t represents the saturation time, w1Is the saturation pulse intensity.
S3: the first stage simulation, namely the normal tissue metabolite information simulation:
based on the optimal multi-pool parameter combination determined in S2, sequentially taking each metabolite in the normal tissue as a target product to perform MTR of the first stageasymThe spectrum Bloch-McConnell simulation, calculated to determine the contribution of each metabolite to CEST signal in normal tissue under experimental conditions.
In the simulation process, when each target product is simulated, the fourth MTR of the target product in existence is calculated through simulationasymSpectrum and fifth MTR in absence of the target productasymThe spectrum is obtained, and then the MTR before and after deletion of each metabolite is obtainedasymThe signal difference of the spectrum is taken as the CEST signal contributed by the metabolite.
For example, for a metabolite x to be evaluated, the MTR under the condition of x pools is simulated based on the optimal multi-pool parameter combinationasymSpectrum (MTR)asym1) And MTR without x poolasymSpectrum (MTR)asym0) MTR in both casesasymThe difference is the contribution MTR of metabolite x to CEST signalasym(x)＝MTRasym1-MTRasym0. Wherein, MTR in the case of x poolasymSpectra can be simulated directly based on optimal multi-pool parameter combinations, without MTR under x-pool free conditionsasymThe spectra were then taken to remove the metabolite x pool, i.e., set its concentration to 0, while the remaining parameters were kept at the optimal multi-pool parametersThe combination is the same, and then simulation is carried out.
In practical application, in order to compare the contribution of various metabolites to the total CEST signal more intuitively, the contribution degree of different metabolites to the CEST signal of normal tissues can be converted into percentage form, and the percentage of the contribution degree is differentiated between positive and negative. At the saturation frequency Δ w, the percentage contribution of each metabolite can be calculated according to the following formula:
considering that there are both positive and negative cases of metabolite contribution to the CEST signal, the denominator is the sum of the absolute values of all metabolite contributions (e.g. x-pool, y-pool, etc.).
S4: the second stage simulation, namely the information simulation of the lesion tissue metabolites:
based on the optimal multi-pool parameter combination determined in the step S2, sequentially taking each parameter in the parameter set of the CEST signal contribution degree to be evaluated as a variable parameter, and performing MTR of the second stageasymThe spectrum Bloch-McConnell simulation, calculated to determine the contribution of each metabolite to the CEST contrast of focal tissues compared to normal tissues with minimal root mean square error under experimental conditions.
In the simulation process, when each variable parameter is simulated, the rest parameters in the optimal multi-pool parameter combination are kept unchanged, and sixth MTRs corresponding to different values of the variable parameter in a value change range are obtained through simulation calculationasymSpectrum at sixth MTRasymSpectra and the third MTRasymThe signal difference of the spectrum serves as a second CEST contrast. And calculating the root mean square error of the second CEST contrast and the first CEST contrast corresponding to different values in the value change range of each variable parameter, and determining the minimum root mean square error and the change trend (such as the rise or fall of the parameter value) of the corresponding variable parameter value relative to the variable parameter value in the normal tissue.
The value change range of the variable parameter can be set as required, and the probability of the parameter change in the lesion is lower than that in the normal tissue by more than 5 times, so that the parameter change range is set to be 20% -500% of the parameter value in the normal tissue.
The simulation process of the second stage can be performed in a fixed-step iterative calculation mode, which specifically comprises the following steps:
(1) firstly, the MTR of the normal tissue is simulated and simulated by utilizing the optimal multi-pool parameter combination representing the metabolite parameters of the normal tissueasymSpectrum (i.e., the aforementioned third MTR)asymSpectra).
(2) And fixing other parameters and changing a certain parameter value. The parameter start value may be set to 20% of the value of the parameter in normal tissue.
(3) Simulation of MTR in lesions using varied multi-pool parametersasymSpectrum, MTR with simulated lesionsasymSpectral subtraction of MTR in simulated Normal tissueasymSpectrum, obtaining CEST contrast delta MTR between simulated focus and simulated normal tissueSimulation (Emulation). Then calculate Δ MTRSimulation (Emulation)And Δ MTRExperiment ofRoot mean square error between.
(4) Judging whether the parameter value is 5 times larger than that in normal tissue; if yes, the circulation is ended, and the minimum root mean square error and the corresponding parameter value obtained in the parameter change process are calculated; if not, continuously changing the parameter value, wherein the specific calculation formula of the changed parameter value is as follows: and (4) returning to the step (3) to continue the circulation execution until the circulation is finished, wherein the changed parameter value is the parameter value plus 5 percent multiplied by the parameter value in the normal tissue. After the circulation is finished, the delta MTR can be obtained after the amplitude of the change of a certain tested parameter is obtainedSimulation (Emulation)And Δ MTRExperiment ofThe closest, root mean square error takes the minimum.
(5) The longitudinal relaxation time T of the free water for the concentration parameter of each metabolite pool1Parameter, transverse relaxation time T2And (4) calculating the parameters in the steps (2) to (4) in a circulating mode respectively to obtain the corresponding minimum root mean square error and the parameter value corresponding to the minimum root mean square error.
After all the parameter searching cycle calculation is completed, a minimum root mean square error can be obtained for each parameter. And thus can be used to determine which of all parameters can cause a delta MTRSimulation (Emulation)And Δ MTRExperiment ofThe closest, i.e. the minimum of all the minimum root mean square errors is found; while obtaining how much the optimum parameter needs to be changed to enable Δ MTRSimulation (Emulation)And Δ MTRExperiment ofThe closest.
S5: evaluating the contribution of the parameter to the normal tissue CEST signal and the lesion-abnormality CEST signal separately in two aspects with respect to the results obtained in the first stage and the second stage, wherein:
and regarding the result obtained in the first stage, taking the absolute value of the CEST signal contributed by each metabolite as the contribution degree of the corresponding metabolite to the CEST signal of the normal tissue to characterize, and obtaining the contribution degrees of different metabolites to the CEST signal of the normal tissue. If the contribution percentage is calculated in the previous step, the contribution of various metabolites to the CEST signal at a specific saturation pulse intensity and saturation time can be visually evaluated according to the percentage.
And regarding the result obtained in the second stage, regarding each parameter in the parameter set, taking the obtained minimum root mean square error as a positive correlation contribution characterization of the parameter to the abnormal change of the CEST signal of the lesion tissue, and obtaining the contribution of different parameters in the parameter set to the abnormal change of the CEST signal of the lesion tissue of the target individual. That is, in this step, if the minimum root mean square error corresponding to each parameter is obtained according to the parameter variation loop calculation of S4, the contribution of the metabolite corresponding to the parameter to the CEST contrast between the lesion and the normal tissue is evaluated, and a smaller root mean square error indicates that the simulated CEST contrast is closer to the experimental true CEST contrast, thereby indicating that the parameter corresponding thereto is more likely to cause the CEST contrast between the lesion and the normal tissue. In practical use, the parameter with the largest contribution degree is determined as the most probable parameter causing the abnormal change of the CEST signal of the lesion tissue of the target individual, and the rest parameters can be ranked according to the probability of the contribution degree. Therefore, the invention can identify what parameter change may cause the abnormal change of the CEST signal of the patient only through the CEST image of the focus of the patient, so as to provide an auxiliary means for diagnosing the cause of the focus.
In the above embodiment, the first stage and the second stage are evaluated together, but in other embodiments, the evaluation of the two stages may be performed separately. The specific steps of performing separately and synchronously are similar, and the two schemes with only one stage are described separately below. It should be noted that, although the following two embodiments describe the schemes, the "first MTR" is also usedasymSpectrum "," second MTRasymSpectrum "," third MTRasymThe spectrum "and the like with preamble notation, but it is used for differentiation purposes only, and preamble notation within each scheme is self-organizing and not commonly used with other schemes. That is, for example, "third MTRasymSpectra "may represent different MTRs in two different scenariosasymSpectra, specific needs to be understood from the context.
For the case that the first stage is carried out separately, the tissue metabolite information evaluation method based on the Bloch-McConnell equation simulation comprises the following steps:
s1: aiming at the tissue type to be evaluated, acquiring the interested region of normal tissue from the CEST image of the target individual, and respectively converting the CEST image signal into MTRasymSpectrum to obtain the first MTR of normal tissueasymA spectrum;
s2: from all candidates for MTRasymDetermining a group of optimal multi-pool parameter combinations representing parameter values of normal tissues in multi-pool parameter combinations of spectrum Bloch-McConnell simulation, and enabling a second MTR obtained through simulationasymSpectrum closest to the first MTRasymA spectrum;
s3: based on the optimal multi-pool parameter combination, sequentially taking each metabolite in normal tissues as a target product to perform MTR (maximum transfer rate)asymSpectrum Bloch-McConnell simulation; when each target product is simulated, the third MTR of the target product is calculated through simulationasymFourth MTR in the absence of the target product and the spectraasymA spectrum, and thenObtaining MTR before and after deletion of each metaboliteasymThe signal difference of the spectrum is used as the CEST signal contributed by the metabolite;
s4: and taking the absolute value of the CEST signal contributed by each metabolite as the contribution degree characterization of the corresponding metabolite to the CEST signal of the normal tissue to obtain the contribution degrees of different metabolites to the CEST signal of the normal tissue.
For the case that the second stage is carried out separately, the tissue metabolite information evaluation method based on the Bloch-McConnell equation simulation comprises the following steps:
s1: aiming at the tissue type to be evaluated, acquiring the interested regions of normal tissue and lesion tissue from a CEST image of a target individual, and respectively converting CEST image signals into MTRs (maximum Transmission Rate) signalsasymSpectrum to obtain the first MTR of normal tissueasymSecond MTR of spectra and focal tissuesasymSpectrum at first MTRasymSpectrum and second MTRasymThe signal difference of the spectrum is used as a first CEST contrast;
s2: from all candidates for MTRasymDetermining a group of optimal multi-pool parameter combinations representing parameter values of normal tissues in multi-pool parameter combinations of spectrum Bloch-McConnell simulation, and enabling a third MTR obtained through simulationasymSpectrum closest to the first MTRasymA spectrum;
s3: based on the optimal multi-pool parameter combination, sequentially taking each parameter in a parameter set of CEST signal contribution to be evaluated as a variable parameter to perform MTRasymSpectrum Bloch-McConnell simulation; when each variable parameter is simulated, the rest parameters in the optimal multi-pool parameter combination are kept unchanged, and fourth MTRs corresponding to different values of the variable parameter in a value change range are obtained through simulation calculationasymSpectrum at fourth MTRasymSpectra and the third MTRasymThe signal difference of the spectrum is used as a second CEST contrast; calculating the root mean square error of second CEST contrast and first CEST contrast corresponding to different values in the value variation range of each variable parameter, and determining the minimum root mean square error;
s4: and aiming at each parameter in the parameter set, the minimum root mean square error is used as a positive correlation contribution degree characterization of the parameter to the abnormal change of the CEST signal of the lesion tissue, and the contribution degrees of different parameters in the parameter set to the abnormal change of the CEST signal of the lesion tissue of the target individual are obtained.
The specific implementation manner of each step in the two schemes can also refer to the scheme adopting the two-stage simulation, and is not described in detail. Furthermore, it should be noted that the methods provided in the present invention can be used for non-diagnostic or therapeutic purposes, such as providing commercial services for scientific research, and can also be used for the auxiliary diagnosis of lesions. However, when the method is used for auxiliary diagnosis, the evaluation result of the second stage cannot directly obtain the diagnosis result of the disease, and only can reflect the possible reasons of the lesion CEST abnormality at the parameter level, so that a doctor can further determine the corresponding disease by combining other diagnosis and treatment means based on the evaluation result.
In order to make the present invention more comprehensible, the above-described scheme using the two-stage simulation is taken as an example, and the following is further described in detail by using the corresponding example.
Examples
CEST data of 9 tuberous sclerosis epileptic patients acquired by 3T magnetic resonance instrument of university of Zhejiang 2019 are used as experiment B 11,2,3,4 μ T, and saturation time 1 s. Deleting case data with poor quality of head movement images, and combining with a figure 1, the method for evaluating the living tissue metabolite information based on two-stage simulation of a Bloch-McConnell equation provided by the invention comprises the following specific implementation steps:
the method comprises the following steps: four different B are calculated and drawn by using CEST data of 8 patients1MTR of Normal tissue region of brain under conditionsasymMean spectrum, as shown in FIG. 2 (a). Nodular regions differ from normal tissue regions by B1MTR under the conditionsasymThe mean spectrum is shown in FIG. 3 (a).
Step two: according to the prior literature reports that the metabolites in the normal tissue region of the brainParameters, simulation of MTR according to Bloch-McConnellasymSpectra and true MTR collected from the experimentasymThe similarity of the spectra and the final determination of the metabolite parameters suitable for the experiment normal tissue Bloch-McConnell simulation are shown in Table 1, and the simulated MTRasymThe spectrum is shown in FIG. 2 (b). Table 1 simulated MTR of parametersasymSpectral and experimental true MTRasymThe spectral shape is nearly uniform.
TABLE 1 simulation parameters of Normal brain tissue under the conditions of this example
For this experiment, the specific form of the 7-cell Bloch-McConnell equation is:
wherein k is1a＝1/T1a+kab+kac+kad+kae+kaf+kag，k1b＝1/T1b+kba，k1c＝1/T1c+kca，k1d＝1/T1d+kda，k1e＝1/T1e+kea，k1f＝1/T1f+kfa，k1g＝1/T1g+kga,k2a＝1/T2a+kab+kac+kad+kae+kaf+kag，k2b＝1/T2b+kba，k2c＝1/T2c+kca，k2d＝1/T2d+kda，k2e＝1/T2e+kea，k2f＝1/T2f+kfa，k2g＝1/T2g+kga,kijDenotes the exchange rate between the i pool and the j pool, T1iAnd T2iRespectively representing the transverse relaxation time and the longitudinal relaxation time of the i pool, i epsilon a, b, c, d, e, f and g (namely water, macromolecules, amide groups, guanidine groups, amine groups, hydroxyl groups and nuclear Oxyfluoro-Hao-effect). w represents the applied saturation pulse frequency, wiIs the resonant frequency of the i-cell, w1In order to saturate the intensity of the pulse,
denotes the magnetism in the x-, y-, z-direction of the i-cellThe strength of the mixture is changed into strength,
the initial magnetization of the i-cell.
Step three: the first phase of the simulation calculates the contribution of each metabolite to CEST signals in normal brain tissue under the experimental conditions. For MTR, the resonance frequency of the macromolecular pool is 0ppmasymThe contribution of (1) is 0, and the experiment needs to research the effect of amide group, guanidyl group, amino group, hydroxyl group and nuclear Oxyfluor on MTRasymThe contribution of (c). For the metabolite x to be evaluated, simulating MTR under the conditions of x pool and no x poolasymSpectrum, MTR in both casesasymThe difference is the contribution of metabolite x to the CEST signal. To more intuitively compare the magnitude of the contribution of the various metabolites to the total CEST signal, the percentage contribution of each metabolite was calculated under the present experimental conditions according to the following formula:
wherein x is in the form of { acylamino, guanidino, amino, hydroxyl and nuclear Oxyfluorovize effect }. Calculating the maximum MTR under different saturation intensity conditionsasymMTR of five metabolites to normal brain tissue at saturation frequency corresponding to valueasymThe contribution and percentage contribution of the values are shown in table 2.
TABLE 2 maximum MTR at different saturation pulse intensitiesasymAt the saturation frequency corresponding to the value, five metabolites to MTR of normal brain tissueasymThe contribution of the value.
In the table, the frequency of the largest CEST signal is not the same for different saturation pulse intensities, nor are the metabolite sources of the CEST signal. At low B1Under the conditions, the contribution of the amide and nuclear oruforenze effects to the CEST signal dominatesIs on B1The contribution of amine groups gradually increases. The method is proved to have feasibility and effectiveness in evaluating the contribution of the metabolites in the normal tissues to the CEST signal.
Step four: and the second stage of simulation, namely nodal tissue metabolite information simulation. And (3) simulating and calculating the contribution of each metabolite to the CEST contrast of the lesion tissues compared with the normal tissues under the experimental condition through the minimum root mean square error. Different B1MTR of tubercle and normal brain tissue under conditionsasymSpectra As shown in FIG. 3(a), the T of each pool was changed1、T2And 6 exchangeable proton pool concentrations, and the minimum root mean square error between the calculated simulation CEST contrast and the CEST contrast obtained by the experiment and the corresponding parameter variation condition are shown in table 3.
Table 3 minimum root mean square error and corresponding parameter variation multiples.
Table 3 shows that the variation of the amino group concentration can minimize the minimum root mean square error between the simulated CEST contrast and the experimental real CEST contrast, which indicates that the MTR obtained by simulation under the condition of variation of the amino group concentrationasymSpectral and true MTRasymThe spectra are closest as shown in fig. 3 (e). In the step, the simulation calculation time of each parameter is within 1 hour, and compared with the calculation cost which is required by fitting and takes days as a unit, the method provided by the invention saves time compared with large data fitting in the aspect of evaluating the main sources of the metabolites of abnormal CEST signals of lesions compared with normal tissues, and has stable result and good application feasibility.
Step five: according to the percentage contribution calculated in the third step, for normal brain tissue under the experimental condition, the B content is low1Under conditions the amide and nuclear orveha0 effects contribute most to the CEST signal, and with B1The effect of the amine group is gradually increased as the ratio increases. According to the root mean square error obtained by the variation of the parameters in the step four, the minimum increase of the concentration of the amino group can be calculated compared with other parametersThe minimum root mean square error shows that the CEST contrast between the simulated nodule and the normal tissue corresponding to the increase of the concentration of the amine group is closest to the contrast obtained by a real experiment, so that the contribution of the amine group to the CEST contrast of the lesion and the normal tissue is relatively large, and the result is consistent with the increase of the concentration of glutamic acid (a main source of the amine group) in the nodule reported in the past literature. In summary, the proposed method can evaluate the changes of major metabolites in tissues while avoiding unstable fitting and saving computation time.
In addition, in another embodiment, there is also provided a tissue metabolite information evaluation device based on Bloch-McConnell equation simulation, comprising a memory and a processor;
the memory for storing a computer program;
the processor is used for realizing the tissue metabolite information evaluation method based on Bloch-McConnell equation simulation when the computer program is executed.
In addition, in another embodiment, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the aforementioned tissue metabolite information evaluation method based on Bloch-McConnell equation simulation.
It should be noted that the Memory may include a Random Access Memory (RAM) or a Non-Volatile Memory (NVM), such as at least one disk Memory. The Processor may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but also Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components. Of course, the device should also have the necessary components to implement the program operation, such as power supply, communication bus, etc.
The above description is further detailed in connection with specific cases, and it is not intended to limit the type of disease to which the present invention is specifically applied. For those skilled in the art to which the present invention pertains, it is obvious that several simple deductions or substitutions can be made according to specific application cases without departing from the concept of the present invention, and therefore, the applications of the present invention in other diseases and the Bloch-McConnell equation with different pool numbers for different disease tissues belong to the extension made based on the content of the present invention, and should be considered as belonging to the patent claims filed by the present invention to determine the patent protection scope.
Claims (10)
1. A tissue metabolite information evaluation method based on Bloch-McConnell equation simulation is characterized by comprising the following steps:
s1: aiming at the tissue type to be evaluated, acquiring the interested region of normal tissue from the CEST image of the target individual, and respectively converting the CEST image signal into MTRasymSpectrum to obtain the first MTR of normal tissueasymA spectrum;
s2: from all candidates for MTRasymDetermining a group of optimal multi-pool parameter combinations representing parameter values of normal tissues in multi-pool parameter combinations of spectrum Bloch-McConnell simulation, and enabling a second MTR obtained through simulationasymSpectrum closest to the first MTRasymA spectrum;
s3: based on the optimal multi-pool parameter combination, sequentially taking each metabolite in normal tissues as a target product to perform MTR (maximum transfer rate)asymSpectrum Bloch-McConnell simulation; when each target product is simulated, the third MTR of the target product is calculated through simulationasymFourth MTR in the absence of the target product and the spectraasymThe spectrum is obtained, and then the MTR before and after deletion of each metabolite is obtainedasymThe signal difference of the spectrum is used as the CEST signal contributed by the metabolite;
s4: and taking the absolute value of the CEST signal contributed by each metabolite as the contribution degree characterization of the corresponding metabolite to the CEST signal of the normal tissue to obtain the contribution degrees of different metabolites to the CEST signal of the normal tissue.
2. A tissue metabolite information evaluation method based on Bloch-McConnell equation simulation is characterized by comprising the following steps:
s1: aiming at the tissue type to be evaluated, acquiring the interested regions of normal tissue and lesion tissue from a CEST image of a target individual, and respectively converting CEST image signals into MTRs (maximum Transmission Rate) signalsasymSpectrum to obtain the first MTR of normal tissueasymSecond MTR of spectra and focal tissuesasymSpectrum at first MTRasymSpectrum and second MTRasymThe signal difference of the spectrum is used as a first CEST contrast;
s2: from all candidates for MTRasymDetermining a group of optimal multi-pool parameter combinations representing parameter values of normal tissues in multi-pool parameter combinations of spectrum Bloch-McConnell simulation, and enabling a third MTR obtained through simulationasymSpectrum closest to the first MTRasymA spectrum;
s3: based on the optimal multi-pool parameter combination, sequentially taking each parameter in a parameter set of CEST signal contribution to be evaluated as a variable parameter to perform MTRasymSpectrum Bloch-McConnell simulation; when each variable parameter is simulated, the rest parameters in the optimal multi-pool parameter combination are kept unchanged, and fourth MTRs corresponding to different values of the variable parameter in a value change range are obtained through simulation calculationasymSpectrum at fourth MTRasymSpectra and the third MTRasymThe signal difference of the spectrum is used as a second CEST contrast; calculating the root mean square error of second CEST contrast and first CEST contrast corresponding to different values in the value variation range of each variable parameter, and determining the minimum root mean square error;
s4: and aiming at each parameter in the parameter set, the minimum root mean square error is used as a positive correlation contribution degree characterization of the parameter to the abnormal change of the CEST signal of the lesion tissue, and the contribution degrees of different parameters in the parameter set to the abnormal change of the CEST signal of the lesion tissue of the target individual are obtained.
3. A tissue metabolite information evaluation method based on Bloch-McConnell equation simulation is characterized by comprising the following steps:
s1: aiming at the tissue type to be evaluated, acquiring the interested regions of normal tissue and lesion tissue from a CEST image of a target individual, and respectively converting CEST image signals into MTRs (maximum Transmission Rate) signalsasymSpectrum to obtain the first MTR of normal tissueasymSecond MTR of spectra and focal tissuesasymSpectrum at first MTRasymSpectrum and second MTRasymThe signal difference of the spectrum is used as a first CEST contrast;
s2: from all candidates for MTRasymDetermining a group of optimal multi-pool parameter combinations representing parameter values of normal tissues in multi-pool parameter combinations of spectrum Bloch-McConnell simulation, and enabling a third MTR obtained through simulationasymSpectrum closest to the first MTRasymA spectrum;
s3: based on the optimal multi-pool parameter combination, sequentially taking each metabolite in normal tissues as a target product to perform MTR of the first stageasymSpectrum Bloch-McConnell simulation; when each target product is simulated, the fourth MTR when the target product exists is calculated through simulationasymSpectrum and fifth MTR in absence of the target productasymThe spectrum is obtained, and then the MTR before and after deletion of each metabolite is obtainedasymThe signal difference of the spectrum is used as the CEST signal contributed by the metabolite;
s4: based on the optimal multi-pool parameter combination, sequentially taking each parameter in a parameter set of the CEST signal contribution degree to be evaluated as a variable parameter, and performing MTR (maximum Transmission Rate) of a second stageasymSpectrum Bloch-McConnell simulation; when each variable parameter is simulated, the rest parameters in the optimal multi-pool parameter combination are kept unchanged, and sixth MTRs corresponding to different values of the variable parameter in a value change range are obtained through simulation calculationasymSpectrum at sixth MTRasymSpectra and the third MTRasymThe signal difference of the spectrum is used as a second CEST contrast; aiming at each variable parameter, calculating the contrast between the first CEST and the second CEST corresponding to different values in the value variation rangeDetermining the minimum root mean square error according to the root mean square error of the degree;
s5: evaluating the contribution of the parameter to the normal tissue CEST signal and the lesion-abnormality CEST signal separately in two aspects with respect to the results obtained in the first stage and the second stage, wherein:
for the result obtained in the first stage, the absolute value of the CEST signal contributed by each metabolite is used as the contribution degree characterization of the corresponding metabolite to the CEST signal of the normal tissue, and the contribution degrees of different metabolites to the CEST signal of the normal tissue are obtained;
and regarding the result obtained in the second stage, regarding each parameter in the parameter set, taking the minimum root mean square error as a positive correlation contribution characterization of the parameter to the abnormal change of the CEST signal of the lesion tissue, and obtaining the contribution of different parameters in the parameter set to the abnormal change of the CEST signal of the lesion tissue of the target individual.
4. The method for evaluating tissue metabolite information based on Bloch-McConnell equation simulation of any one of claims 1 to 3, wherein the CEST image signals are respectively converted into MTRs (maximum Transmission Rs)asymThe specific method of the spectrum is as follows:
firstly, CEST signals corresponding to different saturation frequencies Δ w are calculated according to the following formula:
wherein: s (- Δ w) is the signal intensity corresponding to the saturation frequency- Δ w, S (Δ w) is the signal intensity corresponding to the saturation frequency Δ w, S0Is the signal strength in the absence of saturation;
then, the MTR at the saturation frequency Δ w in the region of interest is calculatedasymMTR of mean, different saturation frequencies Δ wasymMean value formation MTRasymSpectra.
5. The tissue metabolite information evaluation method based on Bloch-McConnell equation simulation as claimed in any one of claims 1 to 3, wherein the determination method of the optimal multi-pool parameter combination is as follows:
aiming at each group of multi-pool parameter combination to be selected, through MTRasymSpectrum Bloch-McConnell simulation to obtain a group of MTRsasymSpectra, then calculating the MTR obtained by simulationasymSpectra and the first MTRasymPearson's correlation coefficient between spectra; selecting MTR with highest Pearson correlation coefficient and obtained through simulationasymSpectra and the first MTRasymAnd a group of multi-pool parameter combinations with the nearest spectrum are used as the optimal multi-pool parameter combination.
6. The method for assessing tissue metabolite information based on Bloch-McConnell equation simulation of claim 2 or 3, wherein the parameter set comprises the concentration of each metabolite pool, the longitudinal relaxation time T of the free water pool1And transverse relaxation time T2And (4) parameters.
7. The method for evaluating tissue metabolite information based on Bloch-McConnell equation simulation of claim 2 or 3, wherein the parameter with the largest contribution is determined as the most probable parameter causing the abnormal change of the CEST signal of the lesion tissue of the target individual according to the contribution of different parameters in the parameter set to the abnormal change of the CEST signal of the lesion tissue of the target individual.
8. The method for evaluating tissue metabolite information based on Bloch-McConnell equation simulation of claim 1 or 3, wherein the contribution degree of the different metabolites to the normal tissue CEST signal is converted into percentage form, and the contribution degree percentages distinguish between positive and negative.
9. A tissue metabolite information evaluation device based on Bloch-McConnell equation simulation is characterized by comprising a memory and a processor;
the memory for storing a computer program;
the processor, when executing the computer program, is configured to implement the tissue metabolite information assessment method based on Bloch-McConnell equation simulation according to any one of claims 1 to 8.
10. A computer-readable storage medium, wherein the storage medium has stored thereon a computer program which, when executed by a processor, implements the Bloch-McConnell equation simulation-based tissue metabolite information assessment method according to any one of claims 1 to 8.
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