CN113545844A - Simulation method and device for laser interstitial thermotherapy - Google Patents
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- 238000000015 thermotherapy Methods 0.000 title claims abstract description 54
- 238000004088 simulation Methods 0.000 title claims abstract description 19
- 238000002679 ablation Methods 0.000 claims abstract description 69
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- 238000004422 calculation algorithm Methods 0.000 claims abstract description 11
- 238000013135 deep learning Methods 0.000 claims abstract description 9
- 238000003745 diagnosis Methods 0.000 claims abstract description 6
- 206010020843 Hyperthermia Diseases 0.000 claims description 12
- 230000036031 hyperthermia Effects 0.000 claims description 12
- 238000004514 thermodynamic simulation Methods 0.000 claims description 12
- 230000004087 circulation Effects 0.000 claims description 10
- 238000001816 cooling Methods 0.000 claims description 7
- 230000008081 blood perfusion Effects 0.000 claims description 5
- 239000013307 optical fiber Substances 0.000 claims description 4
- 238000002604 ultrasonography Methods 0.000 claims description 4
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- A61B18/00—Surgical instruments, devices or methods for transferring non-mechanical forms of energy to or from the body
- A61B18/18—Surgical instruments, devices or methods for transferring non-mechanical forms of energy to or from the body by applying electromagnetic radiation, e.g. microwaves
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Abstract
The invention provides a simulation method and a simulation device for laser interstitial thermotherapy, wherein the method comprises the following steps: establishing an ablation model of laser interstitial thermotherapy according to the tissue diagnosis data; correcting an ablation model based on a deep learning algorithm and the completed actual data of the laser interstitial thermotherapy; obtaining medical image data of a to-be-detected object, establishing a three-dimensional model, delineating a region to be ablated in the three-dimensional model, inputting tissue parameters of the region to be ablated into the ablation model, and simulating an ablation process by the ablation model and providing laser interstitial thermotherapy scheme parameters meeting requirements.
Description
The application is a divisional application of Chinese patent application with application number of 201911424950.6, entitled simulation method and equipment for laser interstitial thermotherapy, which is filed in 2019, 12 and 31.
Technical Field
The invention relates to a medical scheme simulation method based on deep learning, in particular to a simulation method and device for laser interstitial thermotherapy.
Background
The laser interstitial thermotherapy system is a minimally invasive surgical scheme for treating deep focus, and the advantages of quick response, small wound and the like are more and more applied clinically, but ablated tissues cannot be directly observed, so that the wide range of users are always puzzled by how to ensure accurate and efficient ablation of focus tissues. How to damage the pathological tissue and protect the normal tissue from being damaged in the shortest possible time is also one of the keys of the success of the operation.
The shapes, optical properties and thermodynamic parameters of different pathological tissues have large differences, and doctors need to learn and become familiar with the system and the method for realizing accurate and efficient ablation for a long time, so that the popularization and application of the system and the method are hindered, the learning speed of users is improved, the use difficulty is reduced, a personalized surgical scheme is provided for subjects to be tested, and the reduction of risks is a problem which needs to be solved urgently.
Disclosure of Invention
In view of the above, the present invention provides a simulation method and apparatus for laser interstitial thermotherapy.
Accordingly, in one aspect, there is provided a method of simulating laser interstitial hyperthermia, comprising the steps of:
establishing an ablation model of laser interstitial thermotherapy according to the tissue diagnosis data;
correcting an ablation model based on a deep learning algorithm and the completed actual data of the laser interstitial thermotherapy;
obtaining medical image data of a to-be-detected object, establishing a three-dimensional model, delineating a region to be ablated in the three-dimensional model, inputting tissue parameters of the region to be ablated into the modified ablation model,
the ablation model simulates the ablation process and provides laser interstitial thermotherapy scheme parameters meeting the requirements.
Herein, tissue diagnostic data includes experimental data, which is data obtained by the inventors during the course of experiments, and literature data, which is data provided from published literature, and includes parameters such as thermodynamic properties of various tissues, and simulation methods commonly used in the art, and the like.
The ablation model for laser interstitial thermotherapy comprises at least the following aspects: propagation simulation of laser in various tissues, tissue thermodynamic simulation after various tissues absorb laser energy and convert the laser energy into heat energy, thermodynamic simulation of cooling circulation and cooling of various tissues and thermodynamic simulation of influence of blood perfusion of various tissues on temperature distribution. The method comprises the following steps of (1) simulating propagation of laser in various tissues by using an HG phase equation; the Pennes equation is used for thermodynamic simulation of the effect of blood perfusion of various tissues on temperature distribution.
The actual data of the completed laser interstitial thermotherapy include: real-time recording of CT, ultrasound, Magnetic Resonance (MR) image data, laser, water circulation and other ablation parameters during the ablation process. The MR image data includes: the method comprises the steps of marking an MR image of focus tissue before ablation, marking magnetic resonance temperature image data in an ablation process, and marking an MR image of an ablation range after ablation.
The step of delineating the region to be ablated in the three-dimensional model is performed by a professional, and the region to be ablated may be a lesion, such as a tumor, a nodule, a cyst, or the like, or may be a range determined by the professional according to experience, such as an epileptic lesion, or the like.
The laser interstitial thermotherapy protocol parameters include: laser power, laser irradiation time, laser irradiation interval time, and cooling liquid circulation speed.
In a second aspect, the invention also provides a laser interstitial hyperthermia apparatus comprising a memory, a processor and a program stored in the memory and run on the processor, characterized in that the processor implements the steps of the aforementioned method when executing the program.
Laser interstitial thermotherapy apparatus comprising:
one or more processors and memory coupled to the one or more processors, the memory storing a program that, when executed by the one or more processors,
cause the one or more processors to perform operations comprising:
establishing an ablation model of laser interstitial thermotherapy according to the tissue diagnosis data;
correcting an ablation model based on a deep learning algorithm and the completed actual data of the laser interstitial thermotherapy;
obtaining medical image data of a to-be-detected object, establishing a three-dimensional model, delineating a region to be ablated in the three-dimensional model, inputting tissue parameters of the region to be ablated into the modified ablation model,
the ablation model simulates the ablation process and provides laser interstitial thermotherapy scheme parameters meeting the requirements.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a flow chart showing an example of a simulation method of laser interstitial thermotherapy according to the present invention;
figure 2 is a schematic view of an example of a laser interstitial hyperthermia apparatus capable of implementing the method of the present invention.
Detailed Description
To make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example 1:
referring to fig. 1, the simulation method of laser interstitial thermotherapy includes the following steps:
establishing an ablation model of laser interstitial thermotherapy according to the tissue diagnosis data;
correcting an ablation model based on a deep learning algorithm and the completed actual data of the laser interstitial thermotherapy;
obtaining medical image data of a to-be-detected object, establishing a three-dimensional model, delineating a region to be ablated in the three-dimensional model, inputting tissue parameters of the region to be ablated into the modified ablation model,
the ablation model simulates the ablation process and provides laser interstitial thermotherapy scheme parameters meeting the requirements.
The characteristics of the tissue, particularly the light penetration property and the thermodynamic (heat conduction) property are two factors which have a large influence on the laser interstitial thermotherapy, so that based on experimental data and literature data, ablation performance parameters of various tissues can be obtained, and therefore, local structures containing various tissues can be modeled to obtain an ablation model of the laser interstitial thermotherapy.
The ablation model of laser interstitial thermotherapy comprises the following aspects: propagation simulation of laser in various tissues, tissue thermodynamic simulation after various tissues absorb laser energy and convert the laser energy into heat energy, thermodynamic simulation of cooling circulation and cooling of various tissues and thermodynamic simulation of influence of blood perfusion of various tissues on temperature distribution. In the laser interstitial thermotherapy process, energy transmission mainly has two forms, one is that light directly irradiates to tissues to be absorbed, but the depth range is limited, and the other is that the tissues are heated after light energy is absorbed, and the temperature difference is generated between the tissue and the tissues which do not absorb the light energy, so that heat energy conduction can be carried out, and therefore, the laser absorption and heat energy transmission of surrounding tissues in the laser interstitial thermotherapy process need to be calculated based on the insertion positions of optical fibers in the laser interstitial thermotherapy process. The accumulation of heat is also influenced by tissue fluids, in particular blood flow and cerebrospinal fluid, which all constitute factors for the position of the blood vessels in the tissue to be ablated, the blood flow, the distance from the cerebrospinal fluid. In order to avoid carbonization of the tissue and influence the laser interstitial thermotherapy, the tissue near the insertion position of the optical fiber needs to be cooled. Based on the characteristics of the tissue and the complex cross influence of the four influencing factors, the preliminarily constructed ablation model is obtained. The method comprises the following steps of (1) simulating propagation of laser in various tissues by using an HG phase equation; the Pennes equation is adopted for thermodynamic simulation of the influence of blood perfusion of various tissues on temperature distribution; these equations and simulations are generally known to those skilled in the art and will not be described in detail.
The preliminarily constructed ablation model needs to be trained through data of actual results, so that continuous improvement based on deep learning can be obtained, and the more training, the more simulation of expected conditions is combined with actual conditions.
The laser interstitial thermotherapy of the present invention is a short name for magnetic resonance guided laser interstitial thermotherapy, so the actual data in the actual using process can include all relevant conventional medical image data, such as: real-time recording of CT, ultrasound, Magnetic Resonance (MR) image data, laser, water circulation and other ablation parameters during the ablation process. The MR image data includes: the method comprises the steps of marking an MR image of focus tissue before ablation, marking magnetic resonance temperature image data in an ablation process, and marking an MR image of an ablation range after ablation.
The MR image of lesion tissue marked before ablation is used for constructing a three-dimensional model of a subject to be examined, classifying and marking tissues, and endowing different property parameters to various tissues in the three-dimensional model. The three-dimensional model may also incorporate CT and/or ultrasound data to obtain a model with more information.
The magnetic resonance temperature image data during the ablation process is used to monitor the ablation process in real time, and the PRF phase subtraction is used in the present invention to calculate the temperature change value. With the temperature rise, the water proton resonance frequency is reduced, and the change of the proton resonance frequency can be obtained by calculating the change of the phase of the heating area by using a basic gradient echo (GRE), wherein the size of the phase change is in positive correlation with the echo time TE. The relationship between the temperature change and the phase difference can be expressed as the formula:
where Φ (T) and Φ 0 are the phases of the current image (after heating) and the reference image (before heating), respectively, α is the temperature coefficient of the shielding constant, γ represents the nuclear spin ratio, and B0 is the main magnetic field strength. If the reference temperature T0 is known, the current temperature T (T) can be calculated by the formula T (T) ═ T0 +. DELTA.T (T).
From the DICOM image received from the magnetic resonance device, the phase values of the volume pixels can be read and preprocessed by the deconvolution algorithm, which can improve the temperature measurement range and the temperature accuracy of the temperature imaging algorithm as follows. The deconvolution algorithm is as follows:
the phase angle is known to be obtained by the above formula, so that the range of the phase angle is known to be-pi, and the calculation of the phase angle difference is carried out by using the following algorithm in order to avoid the folding of the phase angle.
Because rapid scanning is required, the thickness of MRI imaging is large, so that the interval point is large, and temperature data is missing, and the invention adjusts the following parameters through the GRE sequence: TR/TE, sense and FOV are combined with phase data preprocessing normalization, interpolation processing, deconvolution and the like, the temperature of a data missing part is fitted through an algorithm, the resolution is improved, the error is reduced, the temperature monitoring of the spatial resolution of about 1mm, the temperature accuracy within 1 ℃ and the temperature refreshing time of 4s is realized.
The MR image for marking the ablation range after ablation refers to a process of confirming the operation effect through the MR image, and distinguishing and calculating the volume that has been ablated after the operation is finished. After laser interstitial thermotherapy, the tissue necrosis has different characteristics from normal tissue on an MR image, is easy to distinguish, and can be automatically identified by a computer by setting a distinguishing standard.
During the use of the laser interstitial thermotherapy system, the real-time recording of the actual used adjustment and control parameters, such as laser power, laser irradiation time, laser irradiation interval time, cooling fluid circulation speed, etc., can be used as the description of the process.
The method comprises the steps of training a preliminarily constructed ablation model by using a plurality of completed actual data of laser interstitial thermotherapy as input to obtain a corrected ablation model, wherein the accuracy of the ablation model for simulating an ablation process is higher as the input actual data is more based on a deep learning mode. Generally, the modified ablation model of the present invention is trained with at least 10 actual data sets of laser interstitial hyperthermia.
Obtaining medical image data of a subject to be tested and establishing a three-dimensional model, and then drawing a region to be ablated in the three-dimensional model by a professional, wherein the region to be ablated can be a focus, such as a tumor, a nodule, a cyst and the like, or can be a range judged by the professional according to experience, such as an epileptic focus and the like.
Inputting the tissue parameters of the region to be ablated into the corrected ablation model for simulation, and obtaining the recommended laser interstitial thermotherapy scheme parameters. The laser interstitial thermotherapy protocol parameters at least include: laser power (unit: W), laser irradiation time (unit: S), laser irradiation interval time (unit: S), and coolant circulation rate (mL/min). Further, the laser interstitial thermotherapy protocol parameters may also include spatial position information of the inserted optical fiber in the three-dimensional model.
The requirements to be met in the present invention can be determined by professional persons, and can have personalized differences for different situations.
Example 2:
the laser interstitial thermotherapy device of the present invention comprises a memory, a processor and a program stored in the memory and run on the processor, the processor implementing the steps of the aforementioned method when executing the program. It may also include a display, an input device, a housing, a cooling jacket, an ablation fiber, etc., see fig. 2, and refer also to the patent application "laser thermotherapy device and system based on magnetic resonance guidance" filed by the present inventor, application No.: 201810459539.1.
in one example, the laser interstitial thermotherapy device of the present invention comprises:
one or more processors and memory coupled to the one or more processors, the memory storing a program that, when executed by the one or more processors,
cause the one or more processors to perform operations comprising:
establishing an ablation model of laser interstitial thermotherapy according to the tissue diagnosis data;
correcting an ablation model based on a deep learning algorithm and the completed actual data of the laser interstitial thermotherapy;
acquiring medical image data of a to-be-detected object, establishing a three-dimensional model, delineating a region to be ablated in the three-dimensional model, and inputting tissue parameters of the region to be ablated into the corrected ablation model;
the ablation model simulates the ablation process and provides laser interstitial thermotherapy scheme parameters meeting the requirements.
Finally, it should be noted that: the above-mentioned embodiments are only specific embodiments of the present invention, which are used for illustrating the technical solutions of the present invention and not for limiting the same, and the protection scope of the present invention is not limited thereto, although the present invention is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: any person skilled in the art can modify or easily conceive the technical solutions described in the foregoing embodiments or equivalent substitutes for some technical features within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the embodiments of the present invention, and they should be construed as being included therein. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. A simulation method of laser interstitial thermotherapy is characterized by comprising the following steps:
establishing an ablation model of laser interstitial thermotherapy according to the tissue diagnosis data;
correcting an ablation model based on a deep learning algorithm and the completed actual data of the laser interstitial thermotherapy;
acquiring medical image data of a to-be-detected object, establishing a three-dimensional model, delineating a region to be ablated in the three-dimensional model, and inputting tissue parameters of the region to be ablated into the corrected ablation model;
the ablation model simulates the ablation process and provides laser interstitial thermotherapy scheme parameters meeting the requirements.
2. The method of claim 1, wherein the tissue diagnostic data comprises experimental data and literature data.
3. The method for simulating laser interstitial thermotherapy according to claim 1, wherein the ablation model of laser interstitial thermotherapy comprises the following aspects: propagation simulation of laser in various tissues, tissue thermodynamic simulation after various tissues absorb laser energy and convert the laser energy into heat energy, thermodynamic simulation of cooling circulation and cooling of various tissues and thermodynamic simulation of influence of blood perfusion of various tissues on temperature distribution.
4. The method of claim 3, wherein HG phase equation is used to simulate the propagation of laser light in various tissues.
5. The method for simulating interstitial thermotherapy with laser light according to claim 3, wherein the thermodynamic simulation of the effect of the perfusion of the blood in various tissues on the temperature distribution uses the Pennes equation.
6. The simulation method of laser interstitial hyperthermia according to claim 1, wherein the actual data of the completed laser interstitial hyperthermia comprises: real-time recording of CT, ultrasound, Magnetic Resonance (MR) image data, laser, water circulation and other ablation parameters during the ablation process.
7. The method for simulating laser interstitial hyperthermia according to claim 6, wherein the MR image data comprises: the method comprises the steps of marking an MR image of focus tissue before ablation, marking magnetic resonance temperature image data in an ablation process, and marking an MR image of an ablation range after ablation.
8. The method of simulating laser interstitial hyperthermia according to claim 1, wherein the laser interstitial hyperthermia protocol parameters comprise: laser power, laser irradiation time, laser irradiation interval time, and cooling liquid circulation speed.
9. The method of simulating laser interstitial hyperthermia according to claim 8, wherein the laser interstitial hyperthermia protocol parameters further comprise: spatial position information of optical fibers used for laser interstitial thermotherapy in the three-dimensional model.
10. A laser interstitial hyperthermia apparatus comprising a memory, a processor and a program stored in the memory and run on the processor, characterized in that the processor implements the steps of the method according to any one of claims 1 to 9 when executing the program.
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| CN112603536A (en) * | 2020-12-29 | 2021-04-06 | 北京华科恒生医疗科技有限公司 | Method and system for generating electrode thermal coagulation parameters in three-dimensional model |
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102081415A (en) * | 2010-12-29 | 2011-06-01 | 上海大学 | Real-time distributed temperature control system during laser-induced interstitial thermotherapy |
| CN109171998A (en) * | 2018-10-22 | 2019-01-11 | 西安交通大学 | Heating ablation region recognition monitoring imaging method and system based on ultrasonic deep learning |
| WO2019152935A1 (en) * | 2018-02-05 | 2019-08-08 | Broncus Medical Inc. | Image-guided lung tumor planning and ablation system |
| CN110325137A (en) * | 2017-02-23 | 2019-10-11 | 尹诺伯拉狄夫设计公司 | System and method for melting Stateful Inspection and custom ablated forming |
| CN110464454A (en) * | 2019-07-12 | 2019-11-19 | 华科精准（北京）医疗科技有限公司 | The laserthermia system of guided by magnetic resonance |
| WO2019232009A1 (en) * | 2018-05-30 | 2019-12-05 | The Johns Hopkins University | Real-time ultrasound monitoring for ablation therapy |
Family Cites Families (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109640830B (en) * | 2016-07-14 | 2021-10-19 | 医视特有限公司 | Precedent based ultrasound focusing |
| CN110151309B (en) * | 2018-02-14 | 2022-02-15 | 上海美杰医疗科技有限公司 | Preoperative planning method and equipment for multi-modal ablation therapy |
| CN110393589A (en) * | 2018-04-25 | 2019-11-01 | 刘珈 | The design method of tumour ablation treating plan, tumour ablation scheme generation system |
| CN108836477B (en) * | 2018-05-14 | 2021-05-11 | 华科精准（北京）医疗科技有限公司 | Laser thermotherapy device and system based on magnetic resonance guidance |
| CN109077801B (en) * | 2018-06-27 | 2020-06-12 | 清华大学 | Diagnosis and treatment method and system for multi-source information guided laser ablation |
| CN109567939A (en) * | 2018-12-10 | 2019-04-05 | 艾瑞迈迪科技石家庄有限公司 | A kind of percutaneous puncture optimum path planning method and device |
| CN109785325A (en) * | 2019-01-30 | 2019-05-21 | 陕西中医药大学 | A method of the Multimodal medical image based on deep learning |
| CN109893240A (en) * | 2019-03-18 | 2019-06-18 | 武汉大学 | A kind of hyperplasia of prostate bipolar electric resection operation method for early warning based on artificial intelligence |
| CN110164557B (en) * | 2019-07-08 | 2022-05-31 | 杭州爱卓科技有限公司 | Method for simulating and simulating soft tissue surgery path planning by using implicit surface algorithm |
| CN110432985B (en) * | 2019-08-01 | 2021-08-31 | 中山大学肿瘤防治中心 | Interventional ablation scheme simulation method and system, electronic device and storage medium |
-
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Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102081415A (en) * | 2010-12-29 | 2011-06-01 | 上海大学 | Real-time distributed temperature control system during laser-induced interstitial thermotherapy |
| CN110325137A (en) * | 2017-02-23 | 2019-10-11 | 尹诺伯拉狄夫设计公司 | System and method for melting Stateful Inspection and custom ablated forming |
| WO2019152935A1 (en) * | 2018-02-05 | 2019-08-08 | Broncus Medical Inc. | Image-guided lung tumor planning and ablation system |
| WO2019232009A1 (en) * | 2018-05-30 | 2019-12-05 | The Johns Hopkins University | Real-time ultrasound monitoring for ablation therapy |
| CN109171998A (en) * | 2018-10-22 | 2019-01-11 | 西安交通大学 | Heating ablation region recognition monitoring imaging method and system based on ultrasonic deep learning |
| CN110464454A (en) * | 2019-07-12 | 2019-11-19 | 华科精准（北京）医疗科技有限公司 | The laserthermia system of guided by magnetic resonance |
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| CN114451986A (en) * | 2022-01-19 | 2022-05-10 | 杭州堃博生物科技有限公司 | Steam ablation treatment method, device, system, equipment and medium |
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| CN111067618A (en) | 2020-04-28 |
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