Patent Application
20220157195
Not Applicable.
This invention relates to a surgical simulation and training model apparatus and methods for making a surgical simulation and training model.
Almost 60,000 invasive brain tumor removal surgeries take place every year in the United States and Canada. These neurosurgical procedures are advanced and require extensive training, typically done during a 7-year residency program, which is the longest medical residency. There are several thousand surgical residents in the United States who primarily learn to perform these difficult procedures through observation of current neurosurgeons and participation in cadaver labs. Cadavers are fairly accessible in the United States but can cost up to USD $2,500.00 and imply some ethical and religious concerns. Additionally, there are many practical issues with this approach.
The current gold standard in neuroncolytic tumor resection surgery training involves anatomical and practical labs conducted on cadaver brains. Cadaver brains, however, are fixed and provide several obstacles to accurate modeling, including inaccuracies in material properties and the inability to respond to electrical stimulation. In modern neurosurgical practice, intraoperative brain mapping, through direct electrical stimulation of cortical and subcortical structures, constitutes a critical component of awake tumor resection surgery that helps neurosurgeons avoid cutting or damaging essential cortical elements and subcortical pathways. Stimulation is traditionally applied via a bipolar electrode in a technique that neurosurgery residents learn through observation in the operating room.
While training for neurosurgical procedures on a cadaver can help a trainee go through the motions of a procedure, as noted there are many important features of a live brain surgery that do not translate to a cadaver. One of these features is the texture of the brain tissue, and another is that a user cannot train tumor resection. Cadaver tissue is fixed, meaning that it was filled with chemicals to preserve it. This introduces a vast difference in texture and material density between the fixed and live brain tissue, which makes training on the cadaver less accurate to true surgical operations. Additionally, the cadaver tissue does not bleed or pulse with a heartbeat like live tissue does. Finally, and perhaps most importantly, the cadaver tissue is unable to respond to electrical stimulation like a live brain, which is a crucial part of brain tumor resection surgery.
Functional brain mapping was introduced over a century ago and is based upon the principle of nerve conduction. Currently, this technique is widely used in neurosurgery with applications including neuro-oncology, epilepsy surgery, and surgery of selective vascular lesions. Functional brain mapping represents the most reliable tool for identifying and protecting eloquent brain tissue, including speech and motor tracts, during surgery. Mastering the technique of brain mapping requires rigorous training including live observation and operation, which is complicated by changing expectations of attending physicians and reduced resident exposure and autonomy due to the recent COVID-19 pandemic. Surgical education through simulation may reduce the learning curve and error rates, increase resident autonomy, and increase patient safety and improve patient outcomes.
In order to combat some of these challenges, a new type of device is being considered to train medical professionals to perform tumor resection surgeries: functional anatomical models. However, the current offering of products in this field is primitive and, despite the goals, non-functional. There are several technological challenges in creating a functional model. One of these challenges involves the selection of material for the model. Live brain tissue is quite soft and flexible, unlike many synthetic materials used for modeling. The challenge lies in finding a material that can more effectively mimic the texture of live brain tissue. While a true model would incorporate all the aspects of the live brain, much of the brain is still misunderstood and therefore difficult to model. Another current challenge is to scale back the number of critical components or anatomical considerations in order to create the first iteration of a functional model.
The current training methods for neurosurgery procedures lack important aspects of live tumor resection surgery, including simulation of real-time electrical feedback, which is critical for procedure success. What is needed, therefore, is an improved surgical training model apparatus and methods for creating a surgical training model apparatus.
Systems and methods are provided for a training apparatus that includes a functional brain model that responds to electrical stimulation and enables users to simulate cortical brain mapping outside the operating room. Methods for creating a surgical training model include consideration of engineering design inputs, such as metrics in electrical stimulation performance, elasticity, brain tract inclusions, anatomical accuracy, low-grade glioma pathophysiology, product shelf life, and the like. The model may be used by practicing neurosurgeons, neurosurgery residents, and the like.
In one aspect, a surgical training model apparatus is provided. The surgical training model apparatus includes a body that emulates brain tissue and a wire that emulates a corticospinal tract, the wire being at least partially surrounded by the body.
In some aspects, the surgical training model includes a component that emulates a tumor that is at least partially surrounded by the body. In some aspects, the body has a first color, the component has a second color, which is different from the first color. In some aspects, the body has a transition region adjacent to the component that has a color gradient between the first color and the second color.
In some aspects, the body comprises a polymeric material. The polymeric material may have an elasticity of 7000 kPa±440 kPa. The polymeric material may be selected from polyvinyl alcohol, polylactic acid, silicones, thermoplastic elastomers, and mixtures and blends thereof.
In some aspects, the wire used in the surgical training model has a diameter in a range of 5 millimeters to 7 millimeters. The apparatus may provide live electrical feedback in response to a bipolar current from an electrode. The bipolar current may be in a range of 2 mA to 8 mA. The apparatus may include a device for creating an electric field around the wire.
In some aspects, the apparatus includes a probe such that the electric field from the wire induces a current in the probe. The current in the probe may be analyzed to determine an area of the body in which the probe is located. An indicator may be used for signaling when the probe is within a predetermined distance from the wire. In some aspects, the apparatus includes an inner conductor a first insulating layer surrounding the inner conductor, a conductive layer surrounding the first insulating layer, and a second insulating layer surrounding the conductive layer. In some aspects, the wire emulates a right arm corticospinal tract and the apparatus includes a second wire that emulates a right leg corticospinal tract. The second wire may be at least partially surrounded by the body. The apparatus may include a third wire that emulates a left leg corticospinal tract, that is at least partially surrounded by the body. The apparatus may include a fourth wire that emulates a left arm corticospinal tract, that is at least partially surrounded by the body.
In one aspect, a method is provided for creating a surgical training model. The method includes providing a wire that emulates a corticospinal tract. The method also includes providing a component that emulates a tumor and surrounding the wire and the component with a first section of a body that emulates brain tissue and a complementary second section of the body.
The some aspects, the first section of the body and the second section of the body and the component are 3D printed. The first section of the body and the second section of the body may comprise a polymeric material. The polymeric material may have an elasticity of 7000 kPa±440 kPa. The polymeric material may be selected from the group including polyvinyl alcohol, polylactic acid, silicones, thermoplastic elastomers, and mixtures and blends thereof. The first section of the body and the second section of the body may have a first color, the component may have a second color, and the first color and the second color may be different.
In one aspect, another method is provided for creating a surgical training model. The method includes creating a body that emulates brain tissue. The body includes a first interior space dimensioned to receive a wire that emulates a corticospinal tract and a second interior space dimensioned to receive a component that emulates a tumor. The first interior space and the second interior space are filled with a dissolvable material. The method also includes contacting the dissolvable material with a solvent to remove the dissolvable material from the first interior space and the second interior space. The method also includes positioning the wire in the first interior space; and positioning the component in the second interior space.
In some aspects, the body is 3D printed. The body may be formed of a polymeric material. The polymeric material may have an elasticity of 7000 kPa±440 kPa. The polymeric material may be selected from the group including polyvinyl alcohol, polylactic acid, silicones, thermoplastic elastomers, and mixtures and blends thereof. The dissolvable material may include a water soluble polymer including polyvinyl alcohols, polyvinyl pyrrolidone, polyalkylene oxides, acrylamide, acrylic acid, cellulose, cellulose ethers, cellulose esters, cellulose amides, polyvinyl acetates, polycarboxylic acids and salts, polyaminoacids, polyamides, polyacrylamide, copolymers of maleic/acrylic acids, polysaccharides, natural gums, and blends and mixtures thereof. The dissolvable material may be polyvinyl alcohol, and the solvent may be water.
In some aspects, The first section of the body and the second section of the body may have a first color, the component may have a second color, and the first color and the second color may be different.
In one aspect, another method is provided for creating a surgical training model. The method includes positioning a wire that emulates a corticospinal tract in a mold. The method also includes positioning a component that emulates a tumor in the mold. The method also includes placing a moldable material in the mold and allowing the moldable material to set to a body that emulates brain tissue, the body surrounding the wire and the component.
In some aspects, the mold may be formed of a dissolvable material, and the method may include contacting the mold with a solvent to remove the mold from the body. he body comprises a polymeric material. The polymeric material may have an elasticity of 7000 kPa±440 kPa. The polymeric material may be selected from polyvinyl alcohol, polylactic acid, silicones, thermoplastic elastomers, and mixtures and blends thereof. The dissolvable material may be formed from a water soluble polymer selected from polyvinyl alcohols, polyvinyl pyrrolidone, polyalkylene oxides, acrylamide, acrylic acid, cellulose, cellulose ethers, cellulose esters, cellulose amides, polyvinyl acetates, polycarboxylic acids and salts, polyaminoacids, polyamides, polyacrylamide, copolymers of maleic/acrylic acids, polysaccharides, natural gums, and blends and mixtures thereof. The dissolvable material may include polyvinyl alcohol, and the solvent comprises water.
In some aspects, the first section of the body and the second section of the body have a first color, the component has a second color, and the first color and the second color are different.
These and other features, aspects, and advantages of the present invention will become better understood upon consideration of the following detailed description, drawings and appended claims.
Like reference numerals will be used to refer to like parts from Figure to Figure in the following description of the drawings.
Systems and methods are provided for a functional model of the brain to be used for training medical professionals, such as to resect brain tumors outside of the operating room. In some configurations, the model incorporates electrical feedback. Electrical feedback, or cortical mapping, is a component of all awake brain tumor resection surgeries and conventionally is only taught by observation. Many neurosurgical residents say that they learn this skill on the job, meaning that the most useful experience and knowledge that they gain about the surgical procedure happens live in the operating room with a patient relying on them to resect a tumor without leaving permanent deficiencies. This is not ideal, and having models that incorporate more aspects of a live surgery can aid in the prevention of this problem. These models may be used to change the way tumor resection techniques are taught to residents and allow them to train more safely and successfully.
Simulators have proven validity and demonstrable transfer of skills to the clinical setting. There is currently no commercialized surgical simulator of brain mapping; trainees are limited to learn by the classic teaching method of “see one, do one, and teach one,” instead of a more appropriate “see one, simulate one (or many), do one, and teach everyone.” The anatomy and nuances of brain mapping surgery can be simulated using current 3-dimensional (3D) printing techniques to replicate functional white matter tracts and cortex. In some configurations, an anatomically accurate and electrically responsive high-definition biomimetic 3D-printed tool may be used for training and simulating brain mapping.
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A model of the brain for this type of procedure may be configured to interact with the other pieces of surgical equipment used. In a non-limiting example, the model may interface with the neurostimulator, which typically features a bipolar electrode that delivers stimulation to the surface of the model. In a non-limiting example, the model may interface with a wire probe that represents the neurostimulator but that does not deliver stimulation. In a non-limiting example, the model may interface with other handheld surgical devices such as the scalpel and suction device, which are used during surgeries to cut and clear tissue and liquids. The users may include neurosurgeons and neurosurgical residents. As far as design implications, these users are all highly educated and trained, but work in fast-paced, high pressure environments, and so the model may be configured to be accurate to how procedures occur in the operating room yet simple enough to learn easily.
A list of non-limiting example design inputs (e.g., electrical stimulation, elasticity, and tract representation) for a functional brain model sufficient for training purposes are shown in Table 1. These inputs were identified as significant needs for neurosurgeons and neurosurgery residents. A conventional model for training is only an anatomical representation of the brain, which provides no functionality or ability to practice neurosurgical techniques. The non-limiting design inputs in Table 1 may provide for a training device to make the training device brain feel and respond like real brain tissue during a procedure, such as tumor resection surgery, and the like.
In some configurations, design inputs include user experiences in current training methods, gaps of understanding, surprises upon entering the operating room, current neurosurgical procedures, the challenges of tumor resection, and the like. Design inputs may also be categorized into critical, expected, and wanted needs. Categorization may be further validated in order to exclude bias.
The design inputs in Table 1 may be summarized into four main user needs: anatomical accuracy, touch, stimulation feedback, and representation of low-grade glioma pathophysiology. Stimulation feedback includes where the model may respond to electrical stimulation and be able to convey this information back to the trainee. Design inputs may also take into account touch, such that the model may feel more similar to live brain tissue in bounciness and density than a cadaver in order to be useful.
In some configurations, human factor considerations may be included in the design inputs. Human factors include haptic feedback from cutting into the brain, replication of a surgical environment, collaboration with other professionals for signal interpretation, how the model will be held while in use, and the like. A successful model may allow for more accurate training than operation on a cadaver, such that the model may have better texture and interactive capabilities, which may be delineated in the design inputs.
Any appropriate material may be considered as part of the design inputs, and as the model may not be implanted in the body it may not need to be biocompatible. The materials for a device may be selected to not be made out of harmful materials, as the surgical trainees need to interact with it regularly. The model may include dissolvable components that may be dissolved with a solvent (e.g., water) from the mold to accommodate electrical tracts. The dissolvable material can be a water soluble polymer selected from polyvinyl alcohols, polyvinyl pyrrolidone, polyalkylene oxides, acrylamide, acrylic acid, cellulose, cellulose ethers, cellulose esters, cellulose amides, polyvinyl acetates, polycarboxylic acids and salts, polyaminoacids, polyamides, polyacrylamide, copolymers of maleic/acrylic acids, polysaccharides, natural gums, and blends and mixtures thereof. The non-dissolvable component of the model may include polylactic acid, silicone, thermoplastic elastomers such as styrenic block copolymers, thermoplastic polyolefin elastomers, thermoplastic polyurethanes, thermoplastic copolyesters, thermoplastic polyamides, and mixtures thereof. The device may also not need to be sterilized to food or medical grade levels. The device may be designed to run on electricity provided by a standard American outlet or standard battery to increase accessibility and lower cost.
Design inputs may be categorized as: electrical stimulation, elasticity, and tract representation. The lack of live electrical feedback present in current surgical training methods presents a challenge for users. To address this need, the model may be able to respond to a bipolar current ranging between 2-8 mA, in a non-limiting example. The timing of the model response may be used to simulate the real-life surgical scenario. In a non-limiting example, the model may provide feedback within 10 seconds, as in a surgical procedure any stimulation longer than 10 seconds may result in seizure of the brain tissue. The model may also provide for the ability to train for longer than the duration of a normal procedure, such as longer than 30 minutes, in a non-limiting example. In another non-limiting example, the model may be able to operate for at least one hour. There is also a linear correlation between the distance between the electrode and the tract and the amount of voltage applied through the stimulator, which is quantified as 1 millimeter of tissue distance is roughly equal to 1 mV of applied voltage.
The texture of the model may be equal to or better than cadaver tissue. In order to quantify the feel of brain tissue, a measurement of elasticity may be performed, which is the ability of a material to resume its original shape after a stretch, compression, or other deformation is applied. The elasticity of brain cadaver tissue is 70,000±44,000 kPa, while the elasticity of live bovine tissue is 1.389 kPa±0.289 kPa. In a non-limiting example, in order to have the model feel more like live brain tissue, the model elasticity may be selected to be an order of magnitude less than the cadaver elasticity, with a two order of magnitude decrease in tolerance. In the non-limiting example model, the elasticity was 7000 kPa±440 kPa.
Design inputs for the model may include the incorporation of white matter tracts, which interconnect and convey information from the cerebral cortex to subcortical structures. These tracts begin in the motor cortex, stemming from the surface of the brain and travel within the brainstem to end in the spinal cord and interconnect with muscles and sensory terminals. In a non-limiting example, the motor tracts of the arm and leg may be included in the model.
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The design inputs may include anatomical accuracy, tumor representation, and operational considerations. The model may be as anatomically accurate as possible, which is an advantage because the brain is very complex and a model that looks and feels similar to a human brain during an operation may provide superior training than the systems that are conventionally available. The average adult brain weighs 1198 grams for females and 1336 grams for males. These weights can fluctuate based off of various demographic factors, such as age, weight, and height. In a non-limiting example, the model weighs within the averages noted above, and the volume of the model may be from 1130-1260 cm3, also within the range of the female to male average. The average brain dimensions include a width of 140 millimeters, a length of 167 millimeters, and a height of 93 millimeters. In a non-limiting example, the device may have these dimensions within a tolerance of 5%. The thickness of the cerebral cortex is in a range of 1.5-4.5 millimeters. The brain model may be selected to be anatomically proportional. The brain is composed of four lobes, and these lobes may be configured to all be anatomically accurate within their size in relation to one another. These proportions are traditionally measured as a percentage of the total cerebral cortex volume; on average, the frontal lobe accounts for 41% of the cerebral cortex volume, the temporal lobe is 22%, the parietal lobe is 19%, and the occipital lobe is 18%. The color of healthy brain tissue is a Pantone code of 663 C. In some configurations, the model may be configured to reflect this color.
In some configurations, the model may contain a tumor for users to practice performing a resection. Safely resecting a tumor near or at an eloquent brain region is one of the most difficult parts of performing an intracranial surgery. In a non-limiting example, the model may contain a low-grade glioma, which is similar in structure and color to healthy brain tissue; just like healthy tissue, gliomas are soft, gray, and have a density and texture comparable to gelatin sold under the trademark Jell-O®. These similarities make it difficult to determine the boundaries of the tumor. To approximate this, in the non-limiting example, the model may have a 1-3 centimeter diameter spherical tumor that is a slightly darker gray (Pantone 664 C) than the surrounding tissue (Pantone 663 C), as well as 3-5 millimeter sphere of transition space around the tumor. This is the average size of tumors that are removed via resection surgery. The transition space is healthy, noncancerous tissue that has been impacted by tumor growth via cell crowding and angiogenesis, which is usually a finding with higher-grade tumors.
The design inputs may include the model's shelf life, human factors, and regulatory considerations. The current gold standard for training is cadavers, which, when embalmed, last for up to six years. In some configurations, the model is configured to have a greater shelf life than the current gold standard. In a non-limiting example, the model may last for over six years and remain stable at room temperature for storage purposes. Since the models may be used by neurosurgeons and neurosurgery residency programs; the models may be stored in labs and classrooms, and therefore will be kept at 25° C., for an unknown period of time. The model may be used in a classroom or clinical environment; with the users including neurosurgery residents and neurosurgeons training for tumor resection surgery. The model may also be used in clean lab spaces. The model may be used during lab or class sessions, so the user may be surrounded by other students working on their own models.
In some configurations, the model may be used with a stimulator as in current surgical procedure. The model may receive the stimulation from the neurostimulator, integrate it, and output it to the users for feedback. In a non-limiting example, this output may include turning on an LED (Light Emitting Diode) if a sufficient amount of voltage is received, such as an output current no more than 20 mA. The model may be engineered in a way where significant harm to the user is avoided.
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The following Example has been presented in order to further illustrate the invention and is not intended to limit the invention in any way.
A surgical training model apparatus according to an example embodiment of the invention can comprise a polyvinyl alcohol (PVA) mold. The mold may be configured using a 3D printer, such as in a Makerbot Method, a slush mold, or any appropriate method. The model itself may be made of a silicone. In a non-limiting example, the silicone is EcoFlex 00-20 material with wires inside of it and a silicone tumor. Silicone dyes may be used to color both the model and/or the tumor. In some configurations, a combination of a pink and grey color may be used with the tumor being slightly darker than the overall model.
The PVA mold may be an inverse of patient brain imaging created in a program, such as 3D Slicer, that allows for MRI imaging to become an 3D printer compatible file. The model may be patient specific and may be made from patient imaging. The mold may be printed in two pieces to be placed together. The mold may be placed together and filled with the uncured dyed silicone, the wires (to serve as corticospinal tracts in the model), and a dyed solidly cured silicone tumor. The wires and the tumor may be held in place using any appropriate mechanism to stay in the correct position while the silicone cures and sets. In some configurations, the PVA mold may be placed in water in order to form the final silicone model with the proper surface including well-formed sulci. When placed in water, the PVA may dissolve in the water solvent and leave the final silicone model behind. The inserted wires may then be connected to the electrical component. A neurosurgeon can then use the model to practice electrical stimulation and resecting a tumor. The model may also be used for pre-operative planning due to patient-specific imaging.
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A tumor may be represented as a sphere, such as a sphere made of the same molded silicone as the brain. The tumor may be colored slightly darker than the model. In a non-limiting example, the tumor may be a sphere 3-5 millimeters in diameter set in a simulated brain model.
In some configurations, the systems and methods of the current disclosure provide an improved surgical training model apparatus and methods for creating a surgical training model apparatus.
Non-Limiting Example Brain Model
Thirteen neurosurgeons and neurosurgery residents were interviewed in an open-ended format to determine critical design aspects and educational value of a 3D-printed training model. Anonymized brain magnetic resonance imaging (MRI) data were converted into a 2-piece inverse 3D rendered shell, which was printed from polyvinyl acetate (PVA) using MakerBot method 3D printers (MakerBot, New York City, USA, 2019). After printing, the two pieces of the shell were attached, forming an anatomically accurate mold of the brain. Then, a preset silicone molded tumor dyed with the FUSEFX BC-03 dye was inserted into the full brain mold in the superolateral surface in the posterior end of the middle frontal gyrus anterior to the motor cortex. The tumor was further dyed with a layer of blue silicone pigment for a more life-like grayish appearance. The shell was filled with Ecoflex-20 silicone by Smooth-On and dyed with equal parts of the FUSEFX silicone dyes S-301-D and BC-03 to create the opaque pink color. Once the silicone cured for 12 hours, the silicone-filled shell was completely submerged in water to dissolve the PVA mold.
Functional MRI and diffusion tensor imaging (DTI) data were analyzed in the open-source software 3D Slicer15 (Slicer 4.10.2, www.slicer.org, 2019) to generate tractography. This was used to guide wire placement in the created mold, to approximate the projection fibers from the arm and leg areas in the motor homunculus that should be mapped and preserved during surgery. The wires generated electric fields that were detected by a handheld probe through electrical coupling. The handheld probe included a shielded wire connected to a data acquisition (DAQ) system (myDAQ 19.0.0, National Instruments, Austin, USA, 2019). The electric fields were generated digitally through MATLAB (MATLAB R2020b, The MathWorks, Inc., Natick, USA, 2020) with a 2Vpp square wave running through each tract, at frequencies of 10 and 20 kHz, respectively. Electrical parameters remained constant in both cortical and subcortical regions of the tract. Collected raw data pass from the DAQ device through processing software written in LabVIEW (LabVIEW 2020, National Instruments, Austin, USA, 2020). Processing included a bandpass filter between 5 and 20 kHz, amplification with a gain of 30, and spectral power analysis to discriminate between the 2 tracts and to predict relative location.
The relationship between the electrical signal acquired and the distance between the probe and the simulated tract was quantified (n=3 trials) by measuring the response recorded by the simulator between 0 and 3 cm at increments of 0.5 cm using a standard ruler. A baseline reading was taken at a distance greater than 30 cm from the model for calibration purposes.
The elasticity, in kilopascals (kPa), was determined for human tissue, as well as animal tissues from Rhesus monkeys, rodents, cows, and pigs. These values were then compared to the elasticity of Ecoflex-20 silicone to demonstrate material accuracy. A follow-up survey was conducted in Likert format among 32 neurosurgical professionals in order to assess the model's validity and applicability to surgical and educational scenarios.
Electrical data were assessed with a linear regression and estimation of fit. A 95% CI based on observed variance was calculated to ensure that no outliers were present. Material results were reported as percent improvement over baseline, as results were compared to values from literature.
Neurosurgeon interviews and design ideation via multiple interviews (n=13) with neurosurgeons who routinely perform awake and asleep craniotomies for brain tumor resection, primary needs were identified to guide the design of the simulator: anatomic accuracy (n=8/13, 61.5%), tactile feedback (n=7/13, 54%), and stimulation feedback (n=7/13, 54%). Emphasis was on creating a simulator that would replicate not only the brain anatomy and elasticity, but also the brain's electrical conductivity (n=13/13, 100%).
The final brain model used 540 g of PVA, contained 810 g of silicone, and measured 16×12×10 cm. This simulator allowed for replication of various surgical scenarios and training performance of microsurgical techniques including brain mapping and tumor resection. The simulator can be used for mapping functional cortex and subcortical regions adjacent to a glioma to perform safe resection.
The linear relationship between distance and amplitude for the electrical response was determined to be an acceptable representation of the properties of live brain tissue. A linear regression was performed on the data (n=3 trials), showing an r2=0.86. Additionally, almost all data (n=25/27, 93%) fell within the 95% CI that was calculated from observed data, indicating that there were few outliers, and that the device produced consistent results.
Brain tissue hardens postmortem. The elastic modulus of a fixed Rhesus monkey brain measures up to 140 kPa, 17 compared to the approximate elasticity of live brain tissue, which is reported between ˜1.9 and 60 kPa. 17-21 The silicone brain simulation model had an elasticity of only 55 kPa, which is within the range of reported biological values and over 60% closer to the reported average of 14.8 kPa when compared to cadaveric brain tissue.
The model may be used in educational and surgical practice environments. Other aspects of the device may include anatomic accuracy, material accuracy, and mimicry of real surgical scenarios.
Cortical mapping for safe intraoperative monitoring of brain function is the gold standard management for many neurological conditions, including brain tumors, epilepsy, selective vascular lesions (cavernomas, aneurysms, and arteriovenous malformations), and intraparenchymal hematomas. Mastering brain mapping requires extensive practice as a neurosurgeon and as a multidisciplinary team including neurosurgeons, neurologists, neurophysiologists, anesthesiologists, and the like. An anatomically accurate, electrically responsive, 3D-printed model to simulate the essential steps of performing a brain mapping operation may provide for enhanced training of the entire surgical team by replicating a multitude of surgical scenarios and improving interdisciplinary communication.
Previous conventional models have failed to simulate direct stimulation and electrical conduction, allowing for delineation of functional cortex and subcortical tracts. The consistent linear relationship that was quantified allows for the device to give an accurate prediction of nearby corticospinal tracts. The model may be used for simulating a live neurosurgery, without the use of actual human tissue. The simulator simulates the brain's normal anatomy and reproduces a similar elasticity. A patient-specific 3D-printed physical model can be conveniently created at a low cost for preoperative planning, procedure rehearsal, patient education, and resident training and evaluation, and the like.
The model may be incorporated into the resident surgical skills curriculum and used to evaluate trainee performance with quantifiable milestones to certify their competency prior to a real case. This may translate to more resident autonomy in the operating room (OR), improved surgical precision, and increased patient safety.
A functional patient-specific 3D-printed model may be superior to cadaveric dissection for simulating the resection of intraparenchymal brain tumors. Cadaveric tissue is unable to retain the material and electrical properties of the live brain. The ethical, religious, and pricing concerns of cadavers make them an inaccessible resource for many. As has been seen during the COVID-19 pandemic, external forces may reduce access to cadavers and limit the ability of trainees to travel for cadaver courses. A 3D-printed simulator can be transported easily and safely, and used in a variety of environments, while a cadaver's usage is restricted to laboratory space and storage. The lack of biological tissue in the simulator also may eliminate the need for sterilization of tools, personal protective equipment, and cleaning. The cost for the nonreusable components of the model may be minor.
3D printing technologies are increasingly used for neurosurgical simulation, ranging from simulators of superficial extra-axial and intra-axial tumor resection to deep intraventricular neuro-endoscopic tumor resection. These previous methods have failed to provide a functional anatomic model that allows stimulation of brain tracts not available in even the best cadaveric specimens. Virtual reality (VR) has also been used previously in surgical simulation, such as in laparoscopic and general surgery, but current VR technologies cannot accurately replicate the experience of complex microsurgical maneuvers unique to neurosurgery or the neural tissue's haptic feedback or response to surgical manipulation and tension forces.
In some configurations, other relevant white matter tracts may be used to increase the validity and applications of the model, such as white matter fiber tracts and connectomic networks that are relevant to mastering the cortical mapping technique. In some configurations, the model may include enhanced anatomic validity, tactile feedback, and approximation of live brain tissue during surgery. Patient-specific tractography may be merged with the silicone model at the time of creation to account for the effect of cerebral edema on nearby tracts. A conductive and malleable material, rather than a single wire, could be placed into the 3D-printed shell before the silicone molding process in order to integrate biomimetic tracts and improve their material properties.
In some configurations, real-time simulated electroencephalography or electrocorticography may be used to monitor electrical discharges and epileptiform activity while a surgical trainee is stimulating the model. Blood and cerebrospinal fluid circulation may be incorporated using mechanical pumps, which may replicate intraoperative complications such as vessel injury and seizures.
Feedback mechanisms may also help to better simulate the conditions of the operating room (OR). In some configurations, the use of audio feedback (in addition to visual) during stimulation may more accurately replicate the verbal feedback between neurosurgeon and neuropsychologist intraoperatively. Patient-specific models with different lesion types and locations can be manufactured and used for preoperative planning and to rehearse patient positioning and intraoperative team dynamics and OR setup, which may be beneficial especially for crowded ORs during awake craniotomies. An additional feedback metric could include amplitude and distance cutoffs for stimulation, in order to avoid seizures and tissue damage. The simulator could be configured to include fiducial markers to function with existing neuro-navigation systems.
In some configurations, a biomimetic 3D-printed model for simulating craniotomies and brain mapping may be used for resident learning, and for improving neurosurgical training methods. The realistic neural properties of the simulator may improve representation of a live surgical environment. Complicated tractography, blood and cerebrospinal fluid circulation, and feedback mechanisms may be conveyed in the simulator model. The model may enhance training of not only the neurosurgical trainee but also the entire surgical team by replicating a multitude of surgical scenarios and improving interdisciplinary communication and preoperative planning.
Although the invention has been described in considerable detail with reference to certain embodiments, one skilled in the art will appreciate that the present invention can be practiced by other than the described embodiments, which have been presented for purposes of illustration and not of limitation. Therefore, the scope of the appended claims should not be limited to the description of the embodiments contained herein.
The citation of any document is not to be construed as an admission that it is prior art with respect to the present invention.
This application claims the benefit of U.S. Provisional Patent Application Ser. No. 63/115,738 filed on Nov. 19, 2020 and entitled “Simulator for Brain Mapping,” which is incorporated herein by reference as if set forth in its entirety for all purposes.
| Number | Date | Country | |
|---|---|---|---|
| 63115738 | Nov 2020 | US |
