CN113545844B - Simulation method and equipment for laser interstitial thermotherapy - Google Patents
Simulation method and equipment for laser interstitial thermotherapy Download PDFInfo
- Publication number
- CN113545844B CN113545844B CN202110825568.7A CN202110825568A CN113545844B CN 113545844 B CN113545844 B CN 113545844B CN 202110825568 A CN202110825568 A CN 202110825568A CN 113545844 B CN113545844 B CN 113545844B
- Authority
- CN
- China
- Prior art keywords
- ablation
- laser
- model
- tissue
- magnetic resonance
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 45
- 238000000015 thermotherapy Methods 0.000 title claims abstract description 44
- 238000004088 simulation Methods 0.000 title claims abstract description 18
- 238000002679 ablation Methods 0.000 claims abstract description 77
- 230000008569 process Effects 0.000 claims abstract description 24
- 206010020843 Hyperthermia Diseases 0.000 claims abstract description 20
- 230000036031 hyperthermia Effects 0.000 claims abstract description 20
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 13
- 238000013135 deep learning Methods 0.000 claims abstract description 9
- 238000003745 diagnosis Methods 0.000 claims abstract description 8
- 238000004514 thermodynamic simulation Methods 0.000 claims description 12
- 230000008859 change Effects 0.000 claims description 11
- 230000004087 circulation Effects 0.000 claims description 9
- 238000001816 cooling Methods 0.000 claims description 7
- 238000010438 heat treatment Methods 0.000 claims description 6
- 230000017531 blood circulation Effects 0.000 claims description 5
- 230000000694 effects Effects 0.000 claims description 5
- 230000003902 lesion Effects 0.000 claims description 5
- 230000010412 perfusion Effects 0.000 claims description 5
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 5
- 239000000110 cooling liquid Substances 0.000 claims description 4
- 239000013307 optical fiber Substances 0.000 claims description 4
- 238000003384 imaging method Methods 0.000 claims description 3
- 238000012544 monitoring process Methods 0.000 claims description 3
- 230000002596 correlated effect Effects 0.000 claims description 2
- 238000007781 pre-processing Methods 0.000 claims description 2
- 238000012549 training Methods 0.000 claims description 2
- 210000001519 tissue Anatomy 0.000 description 50
- 238000002604 ultrasonography Methods 0.000 description 3
- 206010011732 Cyst Diseases 0.000 description 2
- 206010028980 Neoplasm Diseases 0.000 description 2
- 210000001175 cerebrospinal fluid Anatomy 0.000 description 2
- 208000031513 cyst Diseases 0.000 description 2
- 230000001037 epileptic effect Effects 0.000 description 2
- 238000003780 insertion Methods 0.000 description 2
- 230000037431 insertion Effects 0.000 description 2
- 230000001575 pathological effect Effects 0.000 description 2
- 238000006467 substitution reaction Methods 0.000 description 2
- 238000010521 absorption reaction Methods 0.000 description 1
- 238000009825 accumulation Methods 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 230000008081 blood perfusion Effects 0.000 description 1
- 210000004204 blood vessel Anatomy 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000003763 carbonization Methods 0.000 description 1
- 230000007423 decrease Effects 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 239000000835 fiber Substances 0.000 description 1
- 239000012530 fluid Substances 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 208000014674 injury Diseases 0.000 description 1
- 239000003550 marker Substances 0.000 description 1
- 238000002324 minimally invasive surgery Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000010606 normalization Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000035515 penetration Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 230000008733 trauma Effects 0.000 description 1
Classifications
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- 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
- A61B18/20—Surgical instruments, devices or methods for transferring non-mechanical forms of energy to or from the body by applying electromagnetic radiation, e.g. microwaves using laser
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B18/00—Surgical instruments, devices or methods for transferring non-mechanical forms of energy to or from the body
- A61B2018/00571—Surgical instruments, devices or methods for transferring non-mechanical forms of energy to or from the body for achieving a particular surgical effect
- A61B2018/00577—Ablation
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/30—Assessment of water resources
Abstract
The application provides a simulation method and equipment 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 actual data of the completed laser interstitial hyperthermia; obtaining medical image data of a subject to be treated, establishing a three-dimensional model, drawing a region to be ablated in the three-dimensional model, inputting tissue parameters of the region to be ablated into an ablation model, and simulating an ablation process by the ablation model to provide parameters of a laser interstitial thermotherapy scheme meeting requirements.
Description
The application relates to a simulation method and equipment for laser interstitial thermotherapy, which are filed on 12 months and 31 days in 2019, and are divided application of China patent application with the application number of 201911424950.6.
Technical Field
The application relates to a medical scheme simulation method based on deep learning, in particular to a simulation method and equipment for laser interstitial thermotherapy.
Background
The laser interstitial thermotherapy system is used for treating deep focus, which is a minimally invasive surgery scheme, and the advantages of quick effect, small trauma and the like are more and more applied clinically, but ablated tissues cannot be directly observed, so that how to ensure accurate and efficient ablation of focus tissues is always plagued by wide users. How to realize the damage to the pathological tissues in the shortest time possible and protect the normal tissues from being damaged is also one of the keys of successful operation.
The shape, optical performance and thermodynamic parameters of different pathological tissues are greatly different, doctors need to realize accurate and efficient ablation, and longer learning and familiarity are needed, so that popularization and application of the system and the method are hindered, how to improve the learning speed of a user, reduce the use difficulty, provide a personalized operation scheme for a subject to be tested, and reduce risks are the problems which need to be solved urgently.
Disclosure of Invention
In view of this, the present application provides a method and apparatus for simulating laser interstitial hyperthermia.
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 actual data of the completed laser interstitial hyperthermia;
obtaining medical image data of a subject to be treated, establishing a three-dimensional model, outlining an area to be ablated in the three-dimensional model, inputting tissue parameters of the area to be ablated into the corrected ablation model,
the ablation model simulates an ablation process and provides laser interstitial thermotherapy scheme parameters meeting requirements.
In this context, tissue diagnosis data includes experimental data, which means data obtained by the inventors during an experiment, and literature data, which means data provided from published literature, which 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 hyperthermia comprises at least the following aspects: the method comprises the steps of 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 of various tissues by cooling circulation, and thermodynamic simulation of influence of blood flow perfusion of various tissues on temperature distribution. The propagation simulation of the laser in various tissues adopts an HG phase equation; thermodynamic simulations of the effect of various tissue perfusion on temperature distribution employ Pennes equations.
The actual data of the completed laser interstitial hyperthermia include: CT, ultrasound, magnetic Resonance (MR) image data, and real-time recording of ablation parameters such as laser, water circulation and the like in an ablation process. The MR image data includes: MR images of lesion tissues at the mark before ablation, magnetic resonance temperature image data in the ablation process, and MR images of the ablation range after ablation.
The step of delineating the area to be ablated in the three-dimensional model is performed by a professional, and the area to be ablated may be a lesion, such as a tumor, a nodule, a cyst, etc., or may be a range of experience judgment of the professional, such as an epileptic lesion, etc.
The parameters of the laser interstitial thermotherapy scheme include: laser power, laser irradiation time, laser irradiation interval time, and cooling liquid circulation speed.
In a second aspect, the application also provides a laser interstitial thermotherapy device comprising a memory, a processor and a program stored in the memory and running on the processor, characterized in that the processor implements the steps of the aforementioned method when executing the program.
The laser interstitial thermotherapy device includes:
one or more processors and a memory coupled to the one or more processors, the memory storing a program that, when executed by the one or more processors,
causing 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 actual data of the completed laser interstitial hyperthermia;
obtaining medical image data of a subject to be treated, establishing a three-dimensional model, outlining an area to be ablated in the three-dimensional model, inputting tissue parameters of the area to be ablated into the corrected ablation model,
the ablation model simulates an ablation process and provides laser interstitial thermotherapy scheme parameters meeting requirements.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the present application, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is an example of a flow chart of a simulation method of laser interstitial thermotherapy according to the present application;
fig. 2 is a schematic view of an example of a laser interstitial thermotherapy device capable of implementing the method of the present application.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of the present application will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
Example 1:
referring to fig. 1, the simulation method of laser interstitial thermotherapy includes 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 actual data of the completed laser interstitial hyperthermia;
obtaining medical image data of a subject to be treated, establishing a three-dimensional model, outlining an area to be ablated in the three-dimensional model, inputting tissue parameters of the area to be ablated into the corrected ablation model,
the ablation model simulates an ablation process and provides laser interstitial thermotherapy scheme parameters meeting requirements.
The characteristics of the tissue, in particular the light penetration property and the thermodynamic (heat conduction) property, are two factors which have a great influence on the laser interstitial hyperthermia, so that based on experimental data and literature data, ablation performance parameters of various tissues can be obtained, and thus, local structures containing various tissues can be modeled, and an ablation model of the laser interstitial hyperthermia can be obtained.
The ablation model for laser interstitial hyperthermia includes the following aspects: the method comprises the steps of 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 of various tissues by cooling circulation, and thermodynamic simulation of influence of blood flow perfusion of various tissues on temperature distribution. In the laser interstitial thermotherapy process, energy is mainly transmitted in two forms, one is that light is directly irradiated to tissues to be absorbed, but the depth range is limited, the other is that the tissues are heated after light energy is absorbed, and the temperature difference exists between the tissues and the tissues which do not absorb the light energy, so that heat energy can be transmitted, and therefore, the laser absorption and the heat energy transmission of surrounding tissues in the laser interstitial thermotherapy process need to be calculated based on the insertion position of an optical fiber in the laser interstitial thermotherapy process. The accumulation of heat is also affected by tissue fluid, particularly blood flow and cerebrospinal fluid, which all contribute to the position of the blood vessels in the tissue to be ablated, blood flow, distance from the cerebrospinal fluid. In order to avoid carbonization of the tissue and influence the laser interstitial thermotherapy, the tissue near the optical fiber insertion position needs to be cooled down. Based on the characteristics of the tissues and the complex cross influence of the four influencing factors, an ablation model is initially constructed. The propagation simulation of the laser in various tissues adopts an HG phase equation; thermodynamic simulation of the influence of various tissue blood perfusion on temperature distribution adopts Pennes equation; those skilled in the art have a general understanding of these equations and simulations and will not be described in detail.
The primarily 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 the training is, the more the simulation on the expected situation is compounded with the actual situation.
The laser interstitial hyperthermia of the present application is short for magnetic resonance guided laser interstitial hyperthermia, so the actual data may include all relevant conventional medical image data during actual use, such as: CT, ultrasound, magnetic Resonance (MR) image data, and real-time recording of ablation parameters such as laser, water circulation and the like in an ablation process. The MR image data includes: MR images of lesion tissues at the mark before ablation, magnetic resonance temperature image data in the ablation process, and MR images of the ablation range after ablation.
MR images of focal tissue at the marker prior to ablation are used to construct a three-dimensional model of the subject to be examined, classify and mark the tissue, and assign different property parameters to various tissues in the three-dimensional model. The three-dimensional model may also be combined with CT and/or ultrasound data to obtain a model with more information.
The magnetic resonance temperature image data in the ablation process is used for real-time monitoring of the ablation process, and PRF phase subtraction is used for calculating the temperature change value in the application. As the temperature increases, the water proton resonance frequency decreases, and the change of the proton resonance frequency can be obtained by calculating the change of the phase of the heating area by using the basic gradient echo sequence (gradient recalled echo, GRE), and the magnitude of the phase change is positively correlated with the echo time TE. The relationship of temperature change to phase difference can be expressed as:
wherein phi (T) and phi 0 are the phases of the current image (after heating) and the reference image (before heating), alpha is the temperature coefficient of the shielding constant, gamma represents the nuclear gyromagnetic 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).
The phase value of the body pixel can be read from the DICOM image received by the magnetic resonance equipment, and the phase value is preprocessed by the deconvolution algorithm, so that the temperature measuring range and the temperature accuracy of the temperature imaging algorithm can be improved as follows. The unwind and fold algorithm is as follows:
since the phase angle is known as the above formula, the range of the phase angle is known as-pi to pi, and the calculation of the phase angle difference is performed by the following algorithm to avoid the overlapping of the phase angles.
Because of the need of rapid scanning, the thickness of MRI imaging is larger, so that the interval point is larger and the temperature data is lost, and the application adjusts by GRE sequence parameters: TR/TE, sense and FOV, combined with phase data preprocessing normalization, interpolation processing, unreeling and the like, the temperature of the data missing part is fitted through an algorithm, the resolution is improved, the error is reduced, the spatial resolution is about 1mm, the temperature accuracy is within 1 ℃, and the temperature monitoring of the temperature refreshing time is 4 s.
The MR image of the ablation range is marked after the ablation, which means the process of distinguishing and calculating the ablated volume by confirming the operation effect through the MR image after the operation is finished. After laser interstitial thermotherapy, the tissue is necrotized, has different characteristics from normal tissue on the MR image, is easy to distinguish, and can be automatically identified by setting a distinguishing standard.
In the use process of the laser interstitial thermotherapy system, real-time records of adjustment and control parameters actually used, such as laser power, laser irradiation time, laser irradiation interval time, cooling liquid circulation speed and the like, can be used as a description of the process.
The actual data of a plurality of completed laser interstitial thermotherapy is used as input, the preliminarily constructed ablation model is trained, a corrected ablation model is obtained, and based on a deep learning mode, the accuracy of the ablation model for simulating an ablation process is higher as the input actual data is more. In general, the modified ablation model of the present application is trained from at least 10 or more actual data of completed laser interstitial hyperthermia.
The medical image data of the person to be treated is obtained and a three-dimensional model is established, and then the area to be ablated can be sketched in the three-dimensional model by a professional, wherein the area to be ablated can be a focus, such as a tumor, a nodule, a cyst and the like, or can be a range which the professional judges according to experience, such as an epileptic focus and the like.
And inputting the tissue parameters of the area to be ablated into the corrected ablation model for simulation, and obtaining the recommended parameters of the laser interstitial thermotherapy scheme. The parameters of the laser interstitial thermotherapy scheme at least comprise: laser power (unit: W), laser irradiation time (unit: S), laser irradiation interval time (unit: S), and cooling liquid circulation speed (mL/min). Further, the laser interstitial hyperthermia protocol parameters may also include spatial position information of the inserted optical fiber in the three-dimensional model.
The meeting requirements mentioned in the present application may be determined by a professional and may vary individually from case to case.
Example 2:
the laser interstitial thermotherapy device of the present application comprises a memory, a processor and a program stored in the memory and running on the processor, which processor implements the steps of the aforementioned method when executing the program. It may further comprise a display, an input device, a housing, a cooling jacket, an ablation fiber, etc., see fig. 2, and reference may also be made to the patent application "magnetic resonance guidance based laser hyperthermia device and system", filed by the present inventors, application number: 201810459539.1.
in one example, the laser interstitial thermotherapy device of the present application includes:
one or more processors and a memory coupled to the one or more processors, the memory storing a program that, when executed by the one or more processors,
causing 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 actual data of the completed laser interstitial hyperthermia;
obtaining medical image data of a subject to be treated, establishing a three-dimensional model, drawing 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 an ablation process and provides laser interstitial thermotherapy scheme parameters meeting requirements.
Finally, it should be noted that: the above examples are only specific embodiments of the present application, and are not intended to limit the scope of the present application, but it should be understood by those skilled in the art that the present application is not limited thereto, and that the present application is described in detail with reference to the foregoing examples: any person skilled in the art may modify or easily conceive of the technical solution described in the foregoing embodiments, or perform equivalent substitution of some of the technical features, while remaining within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application, and are intended to be included in the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (1)
1. A magnetic resonance guided laser interstitial hyperthermia device comprising a memory, a processor and a program stored in the memory and running on the processor, characterized in that the processor, when executing the program, realizes the steps of a method of simulating magnetic resonance guided laser interstitial hyperthermia, the method of simulating magnetic resonance guided laser interstitial hyperthermia comprising the steps of:
establishing an ablation model of laser interstitial thermotherapy according to the tissue diagnosis data;
correcting the ablation model based on a deep learning algorithm and actual data of the completed laser interstitial thermotherapy;
obtaining medical image data of a subject to be treated, establishing a three-dimensional model, drawing 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 an ablation process and provides parameters of a laser interstitial thermotherapy scheme meeting requirements;
the actual data of the completed laser interstitial thermotherapy comprises: CT, ultrasonic and magnetic resonance image data, and real-time recording of laser and water circulation ablation parameters in an ablation process;
the laser interstitial thermotherapy scheme parameters include: laser power, laser irradiation time, laser irradiation interval time and cooling liquid circulation speed;
the magnetic resonance image data includes: marking the magnetic resonance image of the lesion tissue at the position of the mark before ablation, marking the magnetic resonance image of the ablation range after ablation according to the magnetic resonance temperature image data in the ablation process;
the magnetic resonance image of focus tissue at the mark before ablation is used for constructing a three-dimensional model of a subject, classifying and marking the tissue, and endowing different property parameters to various tissues in the three-dimensional model;
the magnetic resonance temperature image data in the ablation process is used for monitoring the ablation process in real time, and the PRF phase subtraction is used for calculating a temperature change value; as the temperature rises, the water proton resonance frequency is reduced, the change of the proton resonance frequency is obtained by calculating the change of the phase of the heating area by using a basic gradient echo sequence, and the magnitude of the phase change is positively correlated with the echo time TE; the relationship between temperature change and phase difference is expressed as:
；
wherein DeltaT (T) is the temperature change; phi (T) is the phase of the current image after heating, phi 0 For the phase of the reference image before heating, α is the temperature coefficient of the shielding constant, γ represents the gyromagnetic ratio, B 0 Is the main magnetic field intensity;
reading a phase value of a body pixel from a DICOM image received by magnetic resonance equipment, and preprocessing the phase value through an uncoiling and overlapping algorithm to improve the temperature measuring range and the temperature accuracy of a temperature imaging algorithm;
marking the magnetic resonance image of the ablation range after ablation refers to the process of distinguishing and calculating the ablated volume by confirming the surgical effect through the MR image after the operation is finished; after laser interstitial thermotherapy, the tissue is necrotized, the MR image has different characteristics from the normal tissue, and the computer automatic identification can be carried out by setting a distinguishing standard;
training the preliminarily constructed ablation model by using the actual data of the plurality of completed laser interstitial thermotherapy as input to obtain a corrected ablation model, wherein the corrected ablation model is trained by at least 10 actual data of the completed laser interstitial thermotherapy;
the laser interstitial thermotherapy protocol parameters further include: spatial position information of an optical fiber used for laser interstitial thermotherapy in the three-dimensional model;
the tissue diagnosis data comprises experimental data and literature data;
the ablation model of laser interstitial thermotherapy comprises the following aspects: the method comprises the following steps of performing propagation simulation of laser in various tissues, performing thermodynamic simulation of the tissues after various tissues absorb laser energy and convert the laser energy into heat energy, performing thermodynamic simulation of cooling by various tissue cooling cycles, and performing thermodynamic simulation of influence of blood flow perfusion of various tissues on temperature distribution;
the propagation simulation of the laser in various tissues adopts an HG phase equation;
thermodynamic simulations of the effects of various tissue perfusion on temperature distribution employ Pennes equations.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110825568.7A CN113545844B (en) | 2019-12-31 | 2019-12-31 | Simulation method and equipment for laser interstitial thermotherapy |
Applications Claiming Priority (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110825568.7A CN113545844B (en) | 2019-12-31 | 2019-12-31 | Simulation method and equipment for laser interstitial thermotherapy |
| CN201911424950.6A CN111067618A (en) | 2019-12-31 | 2019-12-31 | Simulation method and device for laser interstitial thermotherapy |
Related Parent Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911424950.6A Division CN111067618A (en) | 2019-12-31 | 2019-12-31 | Simulation method and device for laser interstitial thermotherapy |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113545844A CN113545844A (en) | 2021-10-26 |
| CN113545844B true CN113545844B (en) | 2023-11-28 |
Family
ID=70321491
Family Applications (2)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911424950.6A Pending CN111067618A (en) | 2019-12-31 | 2019-12-31 | Simulation method and device for laser interstitial thermotherapy |
| CN202110825568.7A Active CN113545844B (en) | 2019-12-31 | 2019-12-31 | Simulation method and equipment for laser interstitial thermotherapy |
Family Applications Before (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911424950.6A Pending CN111067618A (en) | 2019-12-31 | 2019-12-31 | Simulation method and device for laser interstitial thermotherapy |
Country Status (1)
| Country | Link |
|---|---|
| CN (2) | CN111067618A (en) |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112007289B (en) * | 2020-09-09 | 2022-11-18 | 上海沈德医疗器械科技有限公司 | Automatic planning method and device for tissue ablation |
| 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 |
| 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 |
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 |
|---|---|---|---|---|
| JP2019524221A (en) * | 2016-07-14 | 2019-09-05 | インサイテック リミテッド | Ultrasonic focusing based on precedents |
| 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 |
-
2019
- 2019-12-31 CN CN201911424950.6A patent/CN111067618A/en active Pending
- 2019-12-31 CN CN202110825568.7A patent/CN113545844B/en active Active
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 |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113545844A (en) | 2021-10-26 |
| CN111067618A (en) | 2020-04-28 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113545844B (en) | Simulation method and equipment for laser interstitial thermotherapy | |
| US20210100535A1 (en) | Method and apparatus for laser ablation under ultrasound guidance | |
| KR102014355B1 (en) | Method and apparatus for calculating location information of surgical device | |
| US20200085412A1 (en) | System and method for using medical image fusion | |
| Jolesz et al. | Integration of interventional MRI with computer‐assisted surgery | |
| JP6609330B2 (en) | Registration fiducial markers, systems, and methods | |
| JP6568478B2 (en) | Planning, guidance and simulation system and method for minimally invasive treatment | |
| US10548666B2 (en) | Systems and methods for ultrasound image-guided ablation antenna placement | |
| US10561462B2 (en) | System and method for temperature feedback for adaptive radio frequency ablation | |
| JP2019076693A (en) | Registration and motion compensation for patient-mounted needle guide | |
| EP2519324B1 (en) | Therapeutic apparatus | |
| WO2014031531A1 (en) | System and method for image guided medical procedures | |
| Azagury et al. | Image-guided surgery | |
| KR20120096265A (en) | Apparatus and method for tracking tumor for ultrasound therapy, ultrasound therapy system | |
| CA3138208A1 (en) | Method for planning tissue ablation based on deep learning | |
| Schumann et al. | State of the art in computer-assisted planning, intervention, and assessment of liver-tumor ablation | |
| JP2020039864A (en) | System and method for multi-probe guidance | |
| Wang | Real-time fusion imaging of liver ultrasound | |
| Shahin et al. | Ultrasound-based tumor movement compensation during navigated laparoscopic liver interventions | |
| Kokuryo et al. | Evaluation of a vessel‐tracking‐based technique for dynamic targeting in human liver | |
| Aleong | A Machine Learning Approach to Real-Time MRI Guidance for Robotic Needle-Based Procedures | |
| CN115775611B (en) | Puncture operation planning system | |
| Xu et al. | An MRI guided system for prostate laser ablation with treatment planning and multi-planar temperature monitoring | |
| US20240090866A1 (en) | System and method for displaying ablation zone progression | |
| Li et al. | Ultra-TransUNet: ultrasound segmentation framework with spatial-temporal context feature fusion |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |