阅读说明：本技术 基于生物传热模型的磁共振温度成像方法及系统 (Magnetic resonance temperature imaging method and system based on biological heat transfer model ) 是由 应葵 唐文丁 于 2020-11-03 设计创作，主要内容包括：本发明公开了一种基于生物传热模型的磁共振温度成像方法及系统,该方法包括以下步骤：S101,在hybrid算法中引入生物传热模型,修改正则约束项；S102,在温度分布计算过程中利用磁共振温度成像模型对生物传热模型的参数进行优化处理与实时更新；S103,利用Kalman滤波器将生物传热模型与磁共振温度成像模型进行结合,迭代更新图像。根据本发明的基于生物传热模型的磁共振温度成像方法,一方面能够保留hybrid算法对于运动以及温漂的抑制能力；另一方面还能够大大提升温度变化较小区域的重建误差,增加hybrid算法的适用性,提升预测的准确度。(The invention discloses a magnetic resonance temperature imaging method and a system based on a biological heat transfer model, wherein the method comprises the following steps: s101, introducing a biological heat transfer model into a hybrid algorithm, and modifying a regular constraint term; s102, optimizing and updating parameters of the biological heat transfer model in real time by using a magnetic resonance temperature imaging model in the temperature distribution calculation process; and S103, combining the biological heat transfer model with the magnetic resonance temperature imaging model by using a Kalman filter, and iteratively updating the image. According to the magnetic resonance temperature imaging method based on the biological heat transfer model, on one hand, the inhibition capability of the hybrid algorithm on motion and temperature drift can be reserved; on the other hand, the reconstruction error of a region with small temperature change can be greatly improved, the applicability of the hybrid algorithm is improved, and the prediction accuracy is improved.)

1. A magnetic resonance temperature imaging method based on a biological heat transfer model is characterized by comprising the following steps:

s101, introducing a biological heat transfer model into a hybrid algorithm, and modifying a regular constraint term;

s102, optimizing and updating parameters of the biological heat transfer model in real time by using a magnetic resonance temperature imaging model in the temperature distribution calculation process;

and S103, combining the biological heat transfer model with the magnetic resonance temperature imaging model by using a Kalman filter, and iteratively updating the image.

2. The biological heat transfer model-based magnetic resonance temperature imaging method according to claim 1, wherein in step S101, the process of modifying the regular constraint term comprises:

s201, performing biological heat transfer simulation on a heating process of the focus tissue to obtain a simulated temperature field at the temperature measuring time;

s202, predicting corresponding phase variation through a strong linear relation between the temperature and the PRF in magnetic resonance;

s203, correcting the regular constraint item in the hybrid algorithm by using the phase variation, so that the norm constraint of the phase field variation is modified into the norm constraint of the difference between the measured value and the predicted value of the phase field.

3. The method of claim 1, wherein the parameters of the magnetic resonance temperature imaging model are optimized by solving Jacobbi matrix by using quasi-newton method.

4. The biological heat transfer model-based magnetic resonance temperature imaging method according to claim 3, wherein the step S102 comprises:

s301, determining criterion conditions and threshold values according to initial parameters of the biological heat transfer model;

s302, calculating whether a termination condition value is matched with a temperature distribution value measured by magnetic resonance according to a criterion condition, if so, terminating the operation, otherwise, entering S303;

s303, calculating a temperature distribution value at the moment according to the parameter value of the current biological heat transfer model, making a difference between the temperature distribution value at the moment and the temperature distribution value measured by magnetic resonance, and calculating a Jacobbi matrix by a difference method;

and S304, updating the parameter vector by using a quasi-Newton method according to the Jacobbi matrix, jumping to S302, and converging to obtain an accurate parameter value through a plurality of iterations.

5. The biological heat transfer model-based magnetic resonance temperature imaging method according to claim 1, wherein the step S103 comprises:

s401, detecting temperature change of lesion tissues by using a heating probe, and screening out a part with the temperature being greater than a threshold value as a monitoring area;

s402, filtering the monitoring area by using a Kalman filter;

and S403, placing the filtering process of the Kalman filter on the monitoring area on a GPU for operation processing.

6. A biological heat transfer model-based magnetic resonance temperature imaging system, which is characterized in that the system adopts the biological heat transfer model-based magnetic resonance temperature imaging method according to any one of claims 1-5, and comprises the following steps:

the system comprises a first module, a second module and a third module, wherein the first module is used for introducing a biological heat transfer model into a hybrid algorithm and modifying a regular constraint term;

the second module is used for performing optimization processing and real-time updating on parameters of the biological heat transfer model by using the magnetic resonance temperature imaging model in the temperature distribution calculation process;

and the third module is used for combining the biological heat transfer model with the magnetic resonance temperature imaging model by using a Kalman filter and iteratively updating the image.

7. The biological heat transfer model-based magnetic resonance temperature imaging system of claim 6, wherein the first module comprises:

the first module A part is used for carrying out biological heat transfer simulation on the heating process of the focus tissue to obtain a simulated temperature field at the temperature measuring time;

the first module B part is used for predicting the corresponding phase variation through the strong linear relation between the temperature and the PRF in the magnetic resonance;

and the first module C part is used for correcting the regular constraint item in the hybrid algorithm by using the phase variation, so that the norm constraint of the phase field variation is modified into the norm constraint of the difference between the measured value and the predicted value of the phase field.

8. The biological heat transfer model-based magnetic resonance temperature imaging system of claim 6, wherein the second module comprises:

the second module A part is used for determining criterion conditions and threshold values according to the initial parameters of the biological heat transfer model;

the second module B part is used for calculating whether the termination condition value is consistent with the temperature distribution value measured by the magnetic resonance according to the criterion condition;

the second module C part is used for calculating the temperature distribution value at the moment according to the parameter value of the current biological heat transfer model;

and the second module D part is used for updating the parameter vector by using a quasi-Newton method according to the Jacobbi matrix.

9. A non-transitory readable storage medium having stored thereon a computer program, wherein the computer program, when being executed by a processor, implements the method for biological heat transfer model based magnetic resonance temperature imaging according to any one of claims 1-5.

Technical Field

The invention relates to the technical field of biomedical engineering, in particular to a magnetic resonance temperature imaging method and system based on a biological heat transfer model.

Background

The thermotherapy technology is a novel tumor therapy means with minimal or even no wound, has the advantages of low mortality, low recurrence rate, rapid postoperative recovery and the like, and has the core purpose of ensuring the safety of healthy tissue cells while inactivating tumor cells. In order to achieve the purpose, the temperature change conditions of the heated area and the surrounding normal tissues need to be closely concerned in the tumor thermotherapy process, so that the operation progress is evaluated according to the heat tolerance of different tissues, and reasonable operation planning is performed.

Magnetic Resonance Temperature Imaging (MRTI) has a wide application prospect in tumor hyperthermia because MRTI can perform near real-time, global, lossless temperature imaging on water-containing tissues and the like, and magnetic resonance imaging has high soft tissue contrast and imaging resolution.

In the magnetic resonance temperature monitoring process of clinical treatment, the traditional magnetic resonance imaging sequence needs longer data acquisition time, the temperature is changed in the acquisition process, and the reconstructed temperature distribution is only a fitting value of the actual temperature in the acquisition process, namely a time average effect, so that the application prospect of the technology in clinic is greatly reduced. In order to improve the data acquisition speed and the time resolution, a magnetic resonance fast imaging sequence is a choice, but the higher time resolution and the higher imaging speed can cause the reduction of the spatial resolution, the quality of images obtained by scanning is poorer, the signal-to-noise ratio is lower, and in order to solve the problem, a biological heat transfer model is introduced for assistance.

The Biological Heat Transfer (BHT) model can simulate the temperature field variation of biological tissues under the condition of known external Heat source input by using the initial conditions, boundary conditions and thermophysical parameters of the tissues, so as to predict the temperature distribution of the tissues at a certain time in the future. However, when the hardware facilities and the selection sequence are fixed, the time resolution, the spatial resolution and the image quality can not be obtained in the imaging process. Therefore, there is room for improvement in the above-described technology.

Disclosure of Invention

The present invention is directed to solving at least one of the problems of the prior art. Therefore, an object of the present invention is to provide a magnetic resonance temperature imaging method based on a biological heat transfer model, which can retain the inhibition capability of the hybrid algorithm on motion and temperature drift on one hand; on the other hand, the reconstruction error of a region with small temperature change can be greatly improved, the applicability of the hybrid algorithm is improved, and the prediction accuracy is improved.

The second object of the invention is to provide a system using the magnetic resonance temperature imaging method based on the biological heat transfer model.

A third object of the invention proposes a non-transitory readable storage medium.

The magnetic resonance temperature imaging method based on the biological heat transfer model comprises the following steps:

s101, introducing a biological heat transfer model into a hybrid algorithm, and modifying a regular constraint term;

s102, optimizing and updating parameters of the biological heat transfer model in real time by using a magnetic resonance temperature imaging model in the temperature distribution calculation process;

and S103, combining the biological heat transfer model with the magnetic resonance temperature imaging model by using a Kalman filter, and iteratively updating the image.

According to the magnetic resonance temperature imaging method based on the biological heat transfer model, on one hand, the inhibition capability of the hybrid algorithm on motion and temperature drift can be reserved; on the other hand, the reconstruction error of a region with small temperature change can be greatly improved, the applicability of the hybrid algorithm is improved, and the prediction accuracy is improved.

According to the magnetic resonance temperature imaging method based on the biological heat transfer model, in step S101, the process of modifying the regular constraint term comprises the following steps:

s201, performing biological heat transfer simulation on a heating process of the focus tissue to obtain a simulated temperature field at the temperature measuring time;

s202, predicting corresponding phase variation through a strong linear relation between the temperature and the PRF in magnetic resonance;

s203, correcting the regular constraint item in the hybrid algorithm by using the phase variation, so that the norm constraint of the phase field variation is modified into the norm constraint of the difference between the measured value and the predicted value of the phase field.

According to the magnetic resonance temperature imaging method based on the biological heat transfer model, a quasi-Newton method is adopted for parameters of the magnetic resonance temperature imaging model, and optimization processing of the parameters in the biological heat transfer model is achieved by solving a Jacobbi matrix.

According to an embodiment of the invention, the magnetic resonance temperature imaging method based on the biological heat transfer model includes the following steps of S102:

s301, determining criterion conditions and threshold values according to initial parameters of the biological heat transfer model;

s302, calculating whether a termination condition value is matched with a temperature distribution value measured by magnetic resonance according to a criterion condition, if so, terminating the operation, otherwise, entering S303;

s303, calculating a temperature distribution value at the moment according to the parameter value of the current biological heat transfer model, making a difference between the temperature distribution value at the moment and the temperature distribution value measured by magnetic resonance, and calculating a Jacobbi matrix by a difference method;

and S304, updating the parameter vector by using a quasi-Newton method according to the Jacobbi matrix, jumping to S302, and converging to obtain an accurate parameter value through a plurality of iterations.

According to an embodiment of the invention, the magnetic resonance temperature imaging method based on the biological heat transfer model includes the following steps S103:

s401, detecting temperature change of lesion tissues by using a heating probe, and screening out a part with the temperature being greater than a threshold value as a monitoring area;

s402, filtering the monitoring area by using a Kalman filter;

and S403, placing the filtering process of the Kalman filter on the monitoring area on a GPU for operation processing.

According to a second aspect of the present invention, the biological heat transfer model-based magnetic resonance temperature imaging system employs the biological heat transfer model-based magnetic resonance temperature imaging method according to any one of the first aspect, including:

the system comprises a first module, a second module and a third module, wherein the first module is used for introducing a biological heat transfer model into a hybrid algorithm and modifying a regular constraint term;

the second module is used for performing optimization processing and real-time updating on parameters of the biological heat transfer model by using the magnetic resonance temperature imaging model in the temperature distribution calculation process;

and the third module is used for combining the biological heat transfer model with the magnetic resonance temperature imaging model by using a Kalman filter and iteratively updating the image.

Further, the first module includes:

the first module A part is used for carrying out biological heat transfer simulation on the heating process of the focus tissue to obtain a simulated temperature field at the temperature measuring time;

the first module B part is used for predicting the corresponding phase variation through the strong linear relation between the temperature and the PRF in the magnetic resonance;

and the first module C part is used for correcting the regular constraint item in the hybrid algorithm by using the phase variation, so that the norm constraint of the phase field variation is modified into the norm constraint of the difference between the measured value and the predicted value of the phase field.

Further, the second module includes:

the second module A part is used for determining criterion conditions and threshold values according to the initial parameters of the biological heat transfer model;

the second module B part is used for calculating whether the termination condition value is consistent with the temperature distribution value measured by the magnetic resonance according to the criterion condition;

the second module C part is used for calculating the temperature distribution value at the moment according to the parameter value of the current biological heat transfer model;

and the second module D part is used for updating the parameter vector by using a quasi-Newton method according to the Jacobbi matrix.

In conclusion, according to the magnetic resonance temperature imaging system based on the biological heat transfer model in the second aspect of the invention, on one hand, the inhibition capability of the hybrid algorithm on motion and temperature drift can be reserved; on the other hand, the reconstruction error of a region with small temperature change can be greatly improved, the applicability of the hybrid algorithm is improved, and the prediction accuracy is improved.

According to a third aspect of the invention, a non-transitory readable storage medium is stored with a computer program which, when executed by a processor, implements the method for bio heat transfer model based magnetic resonance temperature imaging according to any one of the first aspects. The non-transitory readable storage medium has the same advantages as the magnetic resonance temperature imaging method based on the biological heat transfer model compared with the prior art, and the detailed description is omitted here.

Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.

Drawings

The above and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:

FIG. 1 is a schematic diagram of a biological heat transfer model based magnetic resonance temperature imaging method according to an embodiment of the invention;

FIG. 2 is a process diagram of modifying a regular constraint term according to an embodiment of the invention;

FIG. 3 is a schematic diagram of a magnetic resonance temperature imaging model according to an embodiment of the invention optimizing parameters of a bio-thermal model;

FIG. 4 is a schematic diagram of combining a biological heat transfer model with a magnetic resonance temperature imaging model using a Kalman filter and iteratively updating the images in accordance with an embodiment of the present invention;

FIG. 5 is a schematic structural diagram of a magnetic resonance temperature imaging system based on a biological heat transfer model according to an embodiment of the invention;

FIG. 6 is a schematic block diagram of a first module according to an embodiment of the invention;

FIG. 7 is a schematic structural diagram of a second module according to an embodiment of the invention;

FIG. 8 is a diagram illustrating the result of updating a parameter vector after multiple iterations according to an embodiment of the present invention.

Reference numerals:

10-magnetic resonance temperature imaging system based on biological heat transfer model, 1-first module, 11-first module part a, 12-first module part B, 13-first module part C, 2-second module, 21-second module part a, 22-second module part B, 23-second module part C, 24-second module part D, 3-third module.

Detailed Description

Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.

In the description of the present invention, it is to be understood that the terms "central," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," "counterclockwise," "axial," "radial," "circumferential," and the like are used in the orientations and positional relationships indicated in the drawings for convenience in describing the invention and to simplify the description, and are not intended to indicate or imply that the referenced device or element must have a particular orientation, be constructed and operated in a particular orientation, and are not to be considered limiting of the invention.

A magnetic resonance temperature imaging method based on a biological heat transfer model according to an embodiment of the present invention is described below with reference to fig. 1 to 8. As shown in fig. 1, a magnetic resonance temperature imaging method based on a biological heat transfer model according to an embodiment of the present invention includes the following steps:

s101, introducing a biological heat transfer model into a hybrid algorithm, and modifying a regular constraint term; furthermore, by adopting hybrid algorithm, the problem of motion artifact in the imaging process can be effectively inhibited, and the problem of magnetic field drift accumulation caused by the change of the main magnetic field along with time can be solved. In the prior art, the hybrid algorithm is greatly affected by temperature, that is, in the reconstructed temperature distribution of the hybrid algorithm, if the temperature rise value of a certain area is small, the temperature rise value of the area is suppressed, so that misjudgment of the value is caused.

S102, optimizing and updating parameters of the biological heat transfer model in real time by using a magnetic resonance temperature imaging model in the temperature distribution calculation process;

and S103, combining the biological heat transfer model with the magnetic resonance temperature imaging model by using a Kalman filter, and iteratively updating the image. Furthermore, higher time resolution and spatial resolution are often needed in clinical temperature monitoring, a Kalman filter is introduced, a temperature map obtained by magnetic resonance measurement is filtered by using a biological heat transfer model, and finally, a temperature map with higher signal-to-noise ratio and temperature measurement accuracy is reconstructed.

According to the magnetic resonance temperature imaging method based on the biological heat transfer model, on one hand, the inhibition capability of the hybrid algorithm on motion and temperature drift can be reserved; on the other hand, the reconstruction error of a region with small temperature change can be greatly improved, the applicability of the hybrid algorithm is improved, and the prediction accuracy is improved.

According to the magnetic resonance temperature imaging method based on the biological heat transfer model in one embodiment of the invention, as shown in fig. 2, in step S101, the process of modifying the regular constraint term includes:

s201, performing biological heat transfer simulation on a heating process of the focus tissue to obtain a simulated temperature field at the temperature measuring time;

s202, predicting corresponding phase variation through a strong linear relation between temperature and Pulse Repetition Frequency (PRF) in magnetic resonance;

s203, correcting the regular constraint item in the hybrid algorithm by using the phase variation, so that the norm constraint of the phase field variation is modified into the norm constraint of the difference between the measured value and the predicted value of the phase field.

Further, in the prior art, in a non-heating area, the temperature of the biological heat transfer model simulation is 0, and at this time, the hybrid target equation remains unchanged, and the hybrid (mixing) algorithm judgment is accurate. However, in a region where the heating temperature rises less, when the hybrid algorithm misjudges that the temperature of the region is 0, this may cause a deviation in the actually measured temperature value. In the invention, through the correction of the biological heat transfer model, the hybrid algorithm can make correct temperature judgment on the area with less heating temperature rise, so that the actual measured temperature value is more accurate, and the error rate is favorably reduced.

According to the magnetic resonance temperature imaging method based on the biological heat transfer model, a quasi-Newton method is adopted for parameters of the magnetic resonance temperature imaging model, and optimization processing of the parameters in the biological heat transfer model is achieved by solving a Jacobbi matrix. This is advantageous for improving the accuracy of the prediction.

According to the magnetic resonance temperature imaging method based on the biological heat transfer model in one embodiment of the invention, as shown in fig. 3, step S102 includes:

s301, determining criterion conditions and threshold values according to initial parameters of the biological heat transfer model;

s302, calculating whether a termination condition value is matched with a temperature distribution value measured by magnetic resonance according to a criterion condition, if so, terminating the operation, otherwise, entering S303;

s303, calculating a temperature distribution value at the moment according to the parameter value of the current biological heat transfer model, making a difference between the temperature distribution value at the moment and the temperature distribution value measured by magnetic resonance, and calculating a Jacobbi matrix by a difference method;

and S304, updating the parameter vector by using a quasi-Newton method according to the Jacobbi matrix, jumping to S302, and converging to obtain an accurate parameter value through a plurality of iterations. For example, in one particular embodiment, as shown in FIG. 8, the exact parameter values may be converged upon through three to four iterations. PVC is Polyvinyl chloride (PVC).

According to the magnetic resonance temperature imaging method based on the biological heat transfer model in one embodiment of the invention, as shown in fig. 4, step S103 includes:

s401, detecting temperature change of lesion tissues by using a heating probe, and screening out a part with the temperature being greater than a threshold value as a monitoring area;

s402, filtering the monitoring area by using a Kalman filter;

s403, the filtering process of the monitoring area by the Kalman filter is placed on a GPU (Graphics Processing Unit) for operation Processing. For example, in one embodiment, the filtering process is performed by placing the GPU on a GPU, and the imaging duration can be reduced to five seconds per frame, thereby facilitating the clinical procedure requirements.

In conclusion, according to the magnetic resonance temperature imaging method based on the biological heat transfer model, on one hand, the inhibition capability of the hybrid algorithm on motion and temperature drift can be reserved; on the other hand, the reconstruction error of a region with small temperature change can be greatly improved, the applicability of the hybrid algorithm is improved, and the prediction accuracy is improved.

According to a second aspect of the present invention, a biological heat transfer model-based magnetic resonance temperature imaging system 10, which employs the biological heat transfer model-based magnetic resonance temperature imaging method according to any one of the first aspects, as shown in fig. 5, includes:

the first module 1 is used for introducing a biological heat transfer model into a hybrid algorithm and modifying a regular constraint term;

the second module 2 is used for performing optimization processing and real-time updating on parameters of the biological heat transfer model by using the magnetic resonance temperature imaging model in the temperature distribution calculation process;

and a third module 3 for combining the biological heat transfer model with the magnetic resonance temperature imaging model by using a Kalman filter and iteratively updating the image.

Further, as shown in fig. 6, the first module 1 includes:

the first module A part 11 is used for carrying out biological heat transfer simulation on the heating process of the focus tissue to obtain a simulated temperature field at the temperature measuring time;

a first module B part 12 for predicting a corresponding phase variation by a strong linear relationship between a temperature and a PRF in magnetic resonance;

the first module C part 13 is configured to modify the regularization constraint term in the hybrid algorithm by using the phase variation, so that the norm constraint of the phase field variation is modified to be the norm constraint of the difference between the measured value and the predicted value of the phase field.

Further, as shown in fig. 7, the second module 2 includes:

a second module A part 21 for determining criterion conditions and threshold values according to the initial parameters of the biological heat transfer model;

a second module B part 22, which is used for calculating whether the termination condition value is matched with the temperature distribution value measured by the magnetic resonance according to the criterion condition;

a second module C part 23, which is used for calculating the temperature distribution value according to the parameter value of the current biological heat transfer model;

and the second module D part 24 is used for updating the parameter vector by using a quasi-Newton method according to the Jacobbi matrix.

In summary, according to the magnetic resonance temperature imaging system 10 based on the biological heat transfer model of the second aspect of the present invention, according to the magnetic resonance temperature imaging method based on the biological heat transfer model of the present invention, on one hand, the suppression capability of the hybrid algorithm for motion and temperature drift can be preserved; on the other hand, the reconstruction error of a region with small temperature change can be greatly improved, the applicability of the hybrid algorithm is improved, and the prediction accuracy is improved.

The third aspect of the present invention also provides a non-transitory readable storage medium, on which a computer program is stored, which when executed by a processor implements the method for magnetic resonance temperature imaging based on a biological heat transfer model according to the first aspect of the present invention, thereby having the advantages of strong ability to suppress motion and temperature drift, lower error rate, and high accuracy of prediction.

In the description herein, references to the description of the term "one embodiment," "some embodiments," "an illustrative embodiment," "an example," "a specific example," or "some examples" or the like mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

While embodiments of the invention have been shown and described, it will be understood by those of ordinary skill in the art that: various changes, modifications, substitutions and alterations can be made to the embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.