Morton, in 1881, showed that human being's muscle stimulation terminates whenever the applied signal frequency is higher than 100 kHz. Based on that, electrosurgery was first invented in 1926 by William Bovie. Modern electrosurgery passes alternating signals with a fundamental frequency above 200 kHz but lower than 5 MHz through the human body to conduct clinical treatment, such as cutting, coagulation, and fulguration, etc. Therefore, inverters with high frequency (“HF”) outputs are required for electrosurgery.
Resonant inverters (e.g., class E, F, EF2, Class Φ2) and their variants feature HF output with reasonable efficiency. However, none of those topologies has been investigated in the area of electrosurgery due to certain limitations, such as load sensitivity, high device voltage stress, and sophisticated tuning processes. Wide-bandgap (WBG) (e.g., SiC, GaN) device-based pulse-width modulation (“PWM”) inverters can operate at HF. However, to generate a fundamental (inverter-sinusoidal-output) frequency of at least 200 kHz, as needed by electrosurgery, an extremely high switching frequency (multi-MHz) is needed that is impractical from efficiency, thermal, and electromagnetic interference standpoints. Selective harmonic elimination (“SHE”) was pursued in line-frequency-commutated converters decades back owing to very-slow thyristor and triacs.
However, introduction of notches is still required for extracting the fundamental-frequency output at an acceptable total harmonic distortion (“THD”) and hence application of SHE will still require an electrosurgery HF inverter to operate at frequencies that are multiples of 200 kHz yielding similar challenges mentioned above.
The existing inverters for electrosurgery include inverters that generate square-wave output with a much higher switching frequency that yields higher switching loss and potentially tissue-damaging super-harmonics. Alternative known inverters also include soft-switched inverters that reduce the switching loss but can only support very narrow load range (to achieve soft switching) and, the load range bounds are also well short of the ones needed to support electrosurgery.
In a first aspect, an example electrosurgical system is disclosed. The electrosurgical system includes (a) a high-frequency inverter (“HFI”) having a full bridge, and (b) a control system electrically coupled to the HFI that controls output parameters including one or more of an output power Pout(t) and an output voltage or current by varying power reference Pref(t) or switch states of the HFI. The control system causes a power adaptation ΔP(t) to a preset power Pset based on receiving at least one of impedance feedback and thermal feedback according to the following relationship: Pref(t)=Pset+ΔP(t).
In a second aspect, an example method for using the electrosurgical system according to the first aspect of the disclosure is provided. The method includes (a) receiving, via the control system, at least one signal with an indication of thermal feedback and/or impedance feedback, (b) determining, via the control system, a power adaptation ΔP(t) based on the thermal feedback and/or impedance feedback, and (c) combining, via the control system, a preset power Pset for the HFI with the determined power adaptation ΔP(t) to obtain the output power reference Pref(t) for the HFI.
In a third aspect, an example non-transitory computer-readable medium having stored thereon program instructions that upon execution by a processor, cause performance of a set of steps according to the second aspect of the disclosure is provided. The non-transitory computer-readable medium includes (a) the control system receiving at least one signal with an indication of thermal feedback and/or impedance feedback, (b) the control system determining a power adaptation ΔP(t) based on the thermal feedback and/or impedance feedback, (c) the control system combining a preset power Pset for the HFI with the determined power adaptation ΔP(t) to obtain the output power reference Pref(t) for the HFI.
Exemplary high-frequency inverters adapted for use in electrosurgery and associated exemplary power systems are set forth. Systems herein facilitate control of output parameters, such as output power, voltage, current etc., by varying power switch states. The power switch states can be based on one or more of a proper combination of impedance, spatial, and/or temporal feedback, estimations, and/or predictions, in order to sufficiently regulate collateral tissue damage during electrosurgery.
Systems and methods constructed in accordance with the principles of the disclosure can be configured to monitor load properties, such as load temperature, load impedance, etc. in an ultrafast real-time manner. High-frequency inverters constructed in accordance with the principles of the disclosure can be safely packaged into a case with output terminals and a normal AC plug as input. These high-frequency inverters can also be operated by professional surgeons during electrosurgery according to their clinical needs.
Exemplary high-frequency inverters set forth in the disclosure eliminate the need for an extremely high switching frequency (multi-MHz) to generate a fundamental (inverter-sinusoidal-output) frequency of at least 200 kHz, as needed by electrosurgery. Furthermore, the new control system set forth herein significantly and autonomously reduces collateral damage of electrosurgery. Systems and inverter devices herein eliminate potential tissue-damaging super-harmonics from the known square-waveform by incorporating sinewave generating technology. Systems herein further reduce electro-magnetic interference (“EMI”) during surgery. Power control systems for inverters herein can be based on thermal feedback, impedance feedback, model prediction, and/or additional parameters to reduce collateral tissue damage during electrosurgery. Systems herein can incorporate technology and filter architecture that supports a nominal 390 kHz output frequency (with added frequency bandwidth for supporting other modes) and variable output voltages depending on the modes of electrosurgery.
Exemplary systems herein eliminate the need of selective harmonic elimination (“SHE”) and avoid solving transcendental equations without requiring complex, or very-high-frequency PWM or expensive solutions.
Systems herein can incorporate one or more bandpass filters that have very high attenuation for both higher order harmonics and low frequency, and thus the muscle stimulation and waveform distortion due to low frequency and high frequency are avoided at the same time.
Systems herein can include one or more filters that produce a small gain at both high-order harmonics (e.g., 3rd, 5th, and 7th, etc.) and frequencies lower than 100 kHz, which distinguishes an exemplary multi-resonant frequency (“MRF”) filter from conventional low pass filters, such as LCL, and LLCL, etc.
The features, functions, and advantages that have been discussed can be achieved independently in various examples or may be combined in yet other examples further details of which can be seen with reference to the following description and drawings.
The drawings are for the purpose of illustrating examples, but it is understood that the disclosure is not limited to the arrangements and instrumentalities shown in the drawings.
In a first aspect of the disclosure, shown in
In one optional implementation, the electrosurgical system 100 further includes a multi-resonant-frequency (“MRF”) filter 125 electrically coupled to the HFI 110. The MRF filter 125 includes a first resonant tank 126 and a second resonant tank 127. The first resonant tank 126 resonates at a switching frequency and the second resonant tank 127 resonates at least at third-, fifth-, and seventh-order harmonic. A fundamental output frequency of the HFI 110 is the same as a switching frequency of the HFI 110. In one optional implementation, the switching frequency is 390 kHz. The MRF filter 125 is discussed in more detail in Example 1 and 2 below.
In one optional implementation, the HFI 110 generates a bipolar square waveform, and the MRF filter 125 shapes the bipolar square waveform into a sinusoidal waveform output. Further, a transformer primary side voltage of the HFI 110 is determined based on the following:
In one optional implementation, the electrosurgical system 100 also includes an electric scalpel 130 electrically coupled to a transformer secondary side 111 of the HFI 110 and a return pad 135 electrically coupled to the transformer secondary side 111 of the HFI 110. The return pad 135 is configured to receive a load 105 in the form of biomedical tissue that permits current flow therethrough from the electric scalpel 130 to the return pad 135 thereby closing a path for the current flow.
In one optional implementation, the electrosurgical system 100 additionally includes a thermal sensor 140 electrically coupled to the control system 120. The thermal sensor 140 is configured to detect a surface temperature of the load 105. The thermal sensor 140 may include an infrared sensor. The thermal sensor 140 is discussed in more detail in Example 3 below.
In one optional implementation, the control system 120 further includes a modulator 145 configured to output pulse-width modulation signals to the HFI 110, and a power controller 150 that tracks the output power reference Pref(t). The power controller is discussed in more detail in Examples 3 and 4 below.
The control system further includes one or more processors that are detailed in Examples 3 and 4 below and as shown in
The data storage may include or take the form of one or more computer-readable storage media that can be read or accessed by the processor(s). The computer-readable storage media can include volatile and/or non-volatile storage components, such as optical, magnetic, organic or other memory or disc storage, which can be integrated in whole or in part with the processor(s). The data storage is considered non-transitory computer readable media. In some examples, the data storage can be implemented using a single physical device (e.g., one optical, magnetic, organic or other memory or disc storage unit), while in other examples, the data storage can be implemented using two or more physical devices.
The data storage thus is a non-transitory computer readable storage medium, and executable instructions are stored thereon. The instructions include computer executable code. When the instructions are executed by the processor(s), the processor(s) are caused to perform functions.
The processor(s) may be a general-purpose processor or a special purpose processor (e.g., digital signal processors, application specific integrated circuits, etc.). The processor(s) may receive inputs from the communication interface and process the inputs to generate outputs that are stored in the data storage and output to the display. The processor(s) can be configured to execute the executable instructions (e.g., computer-readable program instructions) that are stored in the data storage and are executable to provide the functionality of the computing device described herein.
The following method 200 may include one or more operations, functions, or actions as illustrated by one or more of blocks 205-215. Although the blocks are illustrated in a sequential order, these blocks may also be performed in parallel, and/or in a different order than those described herein. Also, the various blocks may be combined into fewer blocks, divided into additional blocks, and/or removed based upon the desired implementation. Alternative implementations are included within the scope of the examples of the present disclosure in which functions may be executed out of order from that shown or discussed, including substantially concurrent or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art.
Referring now to
In one optional implementation, the control system 120 receiving the at least one signal with the indication of the thermal feedback and/or the impedance feedback includes the control system 120 receiving each switching cycle at least one signal indicating values for a plurality of pairs of output voltage and output current that are measured simultaneously during a given switching cycle. This process is discussed in more detail in Example 4 below with respect to multi-sampling-based power calculations.
In one optional implementation, the control system 120 monitors the output power Pout(t) and thereby tracks the output power reference Pref(t).
In one optional implementation, method 200 further includes the control system 120 determining an ideal average output power Pidl based on a cutting time duration Δt, a mass m of the load, a temperature rise ΔT of the load, a specific heat capacity ceq of the load, a density ρ of the load, an electrode insertion depth h, and/or a cutting width r, as set forth below:
P
idl
·Δt=m·c
eq
·ΔT=½·r·h·v·Δt·ρ·ceq·ΔT.
In one optional implementation, the control system 120 determining the power adaptation ΔP(t) based on the thermal feedback and/or impedance feedback includes the control system 120 determining a load impedance based on a largest value of sampled output voltage and output current for the given switching cycle. Then the control system 120 determines the power adaptation ΔP(t) based on the load impedance and the ideal average output power Pidl. In a further optional implementation, determining the power adaptation ΔP(t) is further based on a load impedance value determined from a moving average of the determined load impedance values over at least 10 switching cycles.
In one optional implementation, method 200 further includes the control system 120 updating the output power reference Pref(t) for the HFI for each switching cycle in 3 μs or less.
In one optional implementation, the control system 120 receiving the at least one signal with the indication of the thermal feedback and/or the impedance feedback includes the control system 120 receiving, per each switching cycle, at least one signal indicating an output voltage Vo(t) corresponding to an output voltage positive peak at Ts/4 and a first and a second sample of output current. The first sample of output current io(k) is measured between 0 and Ts/4 and the second sample of output current io(k+1) is measured after the first sample output current such that the first and the second output current samples do not overlap in time. This process is discussed in more detail in Example 4 with respect to sparse-sampling-based power calculations.
In one optional implementation, method 200 further includes the HFI 110 generating a bipolar square waveform. The MRF filter 125 electrically coupled to the HFI 110 then shapes the bipolar square waveform into a sinusoidal waveform output. The MRF filter 125 includes a first resonant tank 126 and a second resonant tank 127. The first resonant tank 126 resonates at a switching frequency and the second resonant tank 127 resonates at least at third-, fifth-, and seventh-order harmonics. A fundamental output frequency of the HFI 110 is the same as a switching frequency of the HFI 110. In a further optional implementation, the method 200 further includes the control system 120 determining a transformer primary side voltage of the HFI 110 electrically coupled to the MRF filter 125 based on the following:
This process is discussed in more detail below in Examples 1 and 2.
In one optional implementation, method 200 further includes the control system 120 adjusting a phase shift angle α0 between gate signals of diagonal switch pairs Q1-Q4 of the HFI 110 based on the following relationship:
In one optional implementation, method 200 further includes the control system 120 continuously monitoring a surface temperature of the load 105. The control system 120 then determines that the surface temperature of the load 105 differs from a predetermined nominal tissue temperature. Next, the control system adjusts the power reference Pref(t) based on the relationships:
such that the surface temperature of the load is controlled towards the predetermined nominal tissue temperature. This process is discussed in more detail in Example 3 below.
In a third aspect of the disclosure, a non-transitory computer-readable medium having stored thereon program instructions that upon execution by an electrosurgical system 100 according to the first aspect of the disclosure and that has one or more processors may be utilized to cause performance of any functions of the foregoing methods according to the second aspect of the disclosure.
As one example, a non-transitory computer-readable medium having stored thereon program instructions that upon execution by a processor, cause performance of a set of steps includes a control system 120 receiving at least one signal with an indication of thermal feedback and/or impedance feedback. The control system 120 then determines a power adaptation ΔP(t) based on the thermal feedback and/or impedance feedback. Next, the control system 120 combines a preset power Pset for the HFI 110 with the determined power adaptation ΔP(t) to obtain the output power reference Pref(t) for the HFI 110.
The following four example sections discuss experimental results and further inform aspects of the various components of the electrosurgical system 100.
To power electrosurgery, a very-high-frequency AC inverter (“VHFI”) is required. In this example, a full-bridge based VHFI is proposed to enable electrosurgery. Its high-frequency output generating mechanism and the high order filter design are explained. To check the feasibility of the proposed VHFI, a 300 W Gallium Nitride High Electron Mobility Transistors (“GaN HEMT”) based experimental setup with 390 kHz output frequency, has been designed and implemented. Experimental efficiency and total harmonic distortion (“THD”) results are graphed for pure cutting mode. It turns out that maximum THD is less than 2.5% for the proposed VHFI. Further, recurring and burst experiment results are provided for blend cutting mode and coagulation mode, respectively. The experiment results show that the proposed VHFI has extreme fast-responding time for both blend cutting and coagulation mode, and crest factor is about 21 for coagulation mode. All experiment results together validate the feasibility of the proposed VHFI and also verify its capability of supporting different load values under different clinical modes.
GaN HEMT can dramatically improve switching speed, reduce switching loss and thus, GaN HEMT has found wide use in high-frequency applications since emerging in about 2004. Electrosurgery employs high-frequency current, ranging from hundreds kHz to several MHz, passed through human tissues to generate desired clinical effect, such as pure cutting, blend cutting and coagulation. It is obvious that power devices, supporting high frequency with low switching loss, are essential to electrosurgery. Consequently, electrosurgery generator (ESG) which powers electrosurgery, becomes one of target applications for GaN HEMT.
The illustration of electrosurgery using ESG has been highlighted in
There are a large number of inverter topologies and a portion of them are grid-interfaced. The output frequency of these topologies is 50/60 Hz which is significantly slower than the device switching frequency and thus, many modulation schemes, such as sinusoidal pulse width modulation (SPWM), space vector pulse width modulation (SVPWM) can be applied. However, when it comes to high output frequency inverter, the fundamental output frequency is hundreds of kHz or several MHz, then SPWM and SVPWM modulation schemes will not be applicable anymore due to constraints from switching loss and thermal dissipation stress, etc. Therefore, soft-switching technique or different modulation schemes are highly necessary for high output frequency inverter. Class E inverter is well-known soft-switching based topology in high output frequency inverter family, and its output frequency can be more than 10 MHz while maintaining high efficiency. But it is worth noting that Class E inverter is load sensitive and voltage stress on power devices is quite high. A load-independent Class E inverter has been experimentally verified; however, the low and fixed output voltage makes it unsuitable for applications requiring variable high voltage. A Φ2 inverter with lower device voltage stress has been described by others whereas the voltage stress across the device drain and source is still about 2.4 times of the input voltage. Yet, output THD is not mentioned by others, and also, output load is fixed.
ESG involves high frequency AC output with variable output voltage and varied load. Different output voltage magnitudes are required by different clinical purposes while the load variability during normal electrosurgery operation originates from two aspects. The first one is human tissues variation and the second one is plasma arcing dynamic transients. Different types of human tissues, such as skin, muscle and fat, etc. will characterize different electrical properties and tissue impedances will also change a lot even for the same piece of tissue as moving speed or moving direction of electric scalpel differs. The plasma arcing is in series with human tissues and will present when output voltage is sufficiently high such that the electric field around the electric scalpel exceeds that of the air. Further, plasma arcing is polarity dependent, therefore, Class E family, Φ2 inverters and their variants are not attractive for electrosurgery case. Others have proposed a multiphase buck-based inverter with the capability of handling plasma arcing and modifying the output frequency. Yet, the output possesses square shape that contains significant harmonic components, and heavy electro-magnetic interference will be problematic. With the above-mentioned requirements in mind, this example depicts a GaN-HEMT based 390 kHz VHFI prototype capable of delivering energy to variable loads with adjustable output magnitude. At the same time, the output is of sinusoidal waveform, leading to low EMI emission, which makes the proposed prototype a good fit for electrosurgery application.
The rest part of this example is divided into four parts and organized as follows: In Section II, high-frequency AC output generation mechanism and the high order filter design are explained in detail. In Section III, GaN-based experiment validation for pure cutting mode, blend cutting mode, and coagulation mode are exhibited. Finally, this example is concluded in Section IV.
A. High-Frequency AC Generation Mechanism
The topology capture of proposed VHFI is shown in
Assuming that DC power supply, noted as Vin, can support enough ripple power and the DC-link voltage is constant. Phase shift control strategy is employed here to switch the full bridge network at 390 kHz which is exactly the same as fundamental output frequency. For the initial understanding, mathematical analysis included here neglects all nonideal parasitics, such as DC-link voltage ripple, device conduction resistance, gate signal rising and falling time. The ideal gate signals, triggering GaN devices, are shown in
Where n is the order of harmonics and only odd order frequency components exist for square wave. Vin and ƒs are input voltage and switching frequency, respectively. In order to extract fundamental frequency of
all higher order harmonics should be eliminated from Vs(t) and thus, the full bridge block is followed by a high order filter which should be able to pass 390 kHz whereas other harmonics are suppressed.
B. High Order Filter Design
To extract the fundamental frequency from square-wave input, this paper proposes a high order filter structure, and its topology diagram is shown in
As is known, filter bandwidth will enlarge and frequency selectivity will worsen as its quality factor reduces. As a result, more harmonic components will pass, and waveform distortion will deteriorate. To achieve better THD, high quality factor is preferred, however, high quality will also lead to high capacitor voltage stress, increase cost, and augment tuning difficulty. Therefore, the filter quality factor value of tank 1 and tank 2 should be determined and optimized by taking all aforementioned factors into consideration. This example targets loads ranging from 60Ω to 310Ω with maximum output power as 300 W and the quality factor is chosen as 0.82 for 300Ω, which should be further optimized between THD and inductor loss in the future. Given such high frequency, ferrite core inductors, featuring very high self-resonant frequency (“SRF”), are employed. Meanwhile, C0G dielectric capacitors with high voltage rating, extremely low equivalent resistance (“ESR”) and inductance (“ESL”) are of necessity here.
The transfer function of the proposed high order filter is derived and written in equation (2).
Where Ztank1(s) and Ztank2(S) is the impedance of tank 1 and tank 2, respectively. The load connected to the filter is represented as R and only pure resistive load is considered in this paper. Human tissue is not purely resistive and, therefore, more accurate load modeling is needed in future research.
To investigate the efficacy of the proposed filter structure, the Bode plot of transfer function with different loads is plotted in
The feasibility of the proposed VHFI is certified by simulation built in Saber and the simulation result is promising. To further examine its practical performance, a 300 W GaN-HEMT based experimental setup, operating at 390 kHz, has been designed and implemented. The PWM signals controlling GaN devices are generated from TMS320F28335 processor and GaN devices are GS66508B from GAN System with 650 V voltage rating. The setup used to conduct experiments is given in
A. Continuous Output for Pure Cutting Mode
Electrosurgery requires several clinical operation modes, such as pure cutting mode, blend cutting mode and coagulation mode, and each operation mode requires different output waveforms. When ESG is operating under pure cutting mode, continuous output voltage and current are required, and different output power setting is decided by surgeons in actual electrosurgery.
Next, an FFT analysis for output voltage is executed and the frequency spectrum is shown in
Where Mag(n) is the magnitude of nth order harmonics read from FFT analysis and (dBc)n represents the magnitude difference between nth order harmonics and fundamental frequency. It turns out that the approximated output THD is about 1.01% when load is 100Ω and the output voltage is almost pure sinusoidal with 390 kHz.
B. Recurring Output for Blend Cutting Mode
When ESG is operating under blend cutting mode, the output is no longer continuous and blank period appears where output voltage is zero during this time slot. After the blank period, ESG will output sinusoidal waveform again and thus, power is transferred to load in order to elicit clinical blend cutting effect.
C. Burst Output for Coagulation Mode
When ESG is operating under coagulation mode, sinusoidal burst is required to coagulate human tissues. To get burst output, gate signals triggering GaN devices should be configured correspondingly, so that full bridge can output single discrete square-waveform. And then, the single burst, Vs(t), will be reshaped by high order filter into sinusoidal burst which is needed to feed energy into load.
The burst output in
D. Capability of Supporting Variable Loads
To check the performance of proposed VHFI in term of supporting variable loads, different experiments are conducted under the continuous operation condition where the output power is always set around 300 W and the load is manually altered from 60Ω to 310Ω. During the experiments, it is found that the output voltage will gradually rise as power fed to load increases. The experimental recordings show that peak-to-peak output voltage reaches 940 V when 300 W is delivered to 310Ω load, which is relatively high compared to other high output frequency inverters.
The experimental efficiency and THD versus different load values are gathered and lineated in
To deliver high-frequency power to electrosurgery, a high output frequency AC inverter is required. However, class E family, Φ2 inverter and its variants are not able to tackle variable loads and high output voltage at the same time. And thus, a full-bridge based VHFI prototype is described in this example. Its high output frequency producing mechanism and high order filter design are explained in detail. To check practical feasibility of proposed VHFI, a 300 W GaN-HEMT based experimental platform, switching at 390 kHz, has been fabricated and evaluated. The experiment results demonstrate that VHFI is qualified to simultaneously support variable loads and high voltage for a pure cutting mode. The proposed VHFI possesses very low THD, when it is under continuous cutting mode, and the highest THD is less than 2.5%. In addition, the proposed VHFI can also support recurring output and burst output required by blend cutting and coagulation mode, respectively. Further, the proposed VHFI has extremely fast-responding time which takes only 2 cycles for blend cutting mode reaching steady state, and 1 cycle for output magnitude falling to zero. For coagulation mode, the estimated crest factor of proposed VHFI is quite high compared to that in the art and reaches around 21. To validate the accommodation for variable loads, the VHFI is tested with different loads and the experimental efficiency and THD versus different loads are presented. However, the resulting efficiency curve reveals that one drawback of proposed VHFI is its low efficiency, and the highest efficiency is lower than 87.5%. Therefore, efficiency improvement is one of the key research work for the proposed VHFI in the future. Two loss sources are observed as dominating the low efficiency. One is the switching loss affiliated to GaN devices and the other is the loss originating from inductor winding resistance and core loss. As a result, soft switching technique is one of the promising solutions to tackle switching loss. And optimization for inductor design is another effective method to handle the low efficiency issue and achieve the best tradeoff among resonant capacitor voltage stress, total cost, tuning difficulty, THD, core loss, etc. Thereby, high efficiency and low THD can be gathered together inside the proposed VHFI.
This example presents a multi-resonant-frequency (“MRF”) filter for a high-frequency inverter (“HFI”) used in electrosurgery. The fundamental (sinusoidal) output frequency of the HFI is 390 kHz and is the same as the switching frequency of the HFI. The MRF filter is designed to extract the fundamental frequency of the tri-state bipolar waveform, generated by the HFI operating with phase-shift control. The structure and operation of the MRF filer are outlined. An experimental 300 W GaN-FET-based HFI prototype is developed to validate the feasibility of the proposed MRF filter under closed-loop control.
Morton, in 1881, showed that human being's muscle stimulation terminates whenever the applied signal frequency is higher than 100 kHz. Based on that, electrosurgery was first invented in 1926 by William Bovie and the illustration of the monopolar surgery using the electrosurgery generator (“ESG”) is demonstrated in
Resonant inverters (e.g., class E, F, EF2, Class Φ2) and their variants feature HF output with reasonable efficiency. However, none of those topologies has been investigated in the area of electrosurgery due to certain limitations, such as load sensitivity, high device voltage stress, and sophisticated tuning processes. Wide-bandgap (WBG) (e.g., SiC, GaN) device-based PWM inverters can operate at HF. However, to generate a fundamental (inverter-sinusoidal-output) frequency of at least 200 kHz, as needed by electrosurgery, an extremely high switching frequency (multi-MHz) is needed that is impractical from efficiency, thermal, and electromagnetic interference standpoints. Selective harmonic elimination (“SHE”) was pursued in line-frequency-commutated converters decades back owing to very-slow thyristor and triacs. However, introduction of notches is still required for extracting the fundamental-frequency output at an acceptable THD and hence application of SHE will still require an electrosurgery HF inverter to operate at frequencies that are multiples of 200 kHz yielding similar challenges mentioned above.
The existing literature on inverters for electrosurgery either outline inverters that generate square-wave output with a much higher switching frequency that yields higher switching loss and more importantly potentially tissue-damaging super-harmonics or soft-switched inverters that reduce the switching loss but can only support very narrow load range (to achieve soft switching) and further, the load range bounds are also well short of the ones needed for supporting electrosurgery.
For this reason, the MRF filter is proposed to tackle the HF inverter challenges faced in the electrosurgery area. This example section introduces the MRF filter structure, HF generation mechanism, and its actual inductance-based transfer function. Based on that, experimental results showing closed-loop performances are provided to justify the feasibility of the proposed MRF filter.
A. HF Output Generation Mechanism
The proposed HFI topology together with its closed-loop control diagram is shown in
where n and α are the order of the harmonics and the phase shift angle, respectively. The MRF filter practically suppresses the odd harmonics in Vs(t) and only the fundamental frequency component of Vs(t), appears in the transformer primary side and delivers energy to the load via the high-frequency step-up transformer. Consequently, the output frequency of the HFI is the same as the switching frequency of the GaN-based HFI.
B. MRF Filter
The proposed MRF filter, as shown in
For the MRF, tank-1 L1 is based on CoilCraft AGP4233 series inductor. For tank-2, SER2211 and SER1390 series inductors from CoilCraft are used for L2-L4. Finally, all resonant capacitors are based on C0G dielectric. The MRF transfer function for a given load (R) is determined to be the following:
and is plotted in
Overall, and as shown in
A 300 W GaN-based HFI hardware prototype, as shown in
A. Output Regulation and THD
As indicated by (3), the phase shift angle α regulates the transformer's primary side voltage, and thus, secondary-side voltage, Vo(t). The theoretical output voltage and experimental measurements are compared and plotted in
B. Closed-loop Transient Results
To showcase the controllability of the proposed MRF, the closed-loop control diagram for output voltage regulation is given in
The feedforward block provides an initial phase shift angle α0 and the PI controller output Δα compensates for the remaining regulation errors by adjusting the phase shift angle α between gate signals of diagonal switch pairs (Q1 and Q4 or Q2 and Q3). Based on the control diagram, output start-up transient from zero-state to steady-state is plotted in
This example outlines a MRF filter of a full-bridge-based HFI for electrosurgery. It enables the fundamental output and switching frequency of the HFI to be the same at 390 kHz without requiring complex, or very-high-frequency PWM or expensive solutions. The HF output generation mechanism is explained and the MRF structure and its transfer function are provided as well. A 300 W experimental GaN-based HFI is developed and tested. The experiment results show that the HFI has the capability of regulating output voltage via phase-shift angle. Meanwhile, the HFI also supports a wide range of loads with low output voltage THD under various output powers and load conditions. Furthermore, output THD and regulation errors remain small as the tank-1 capacitor deviates from its nominal value. However, the output THD and current quality slightly deteriorate at light load and high load resistance, which can be improved by properly optimizing the transformer turn ratio. Finally, the feedforward and PI-based control ensures that the transient performance of the HFI is found to be satisfactory despite high order of the HFI, as validated by the experimental results when using transient and load step changes.
Well-selected power with accurate delivery is of importance in electrosurgery to generate proper temperature at the cutting site, and thus, reduce undesired collateral tissue damages. Conventional electrosurgery generator (“ESG”) targets tracking a preset power, manually set by surgeons per their experience before the surgery, with high accurate delivery. It is possible that this fixed power setting is not at the optimal point and thus, increases the possibility of added-collateral biomedical tissue damage. To eliminate the potential negative impact of the fixed and ill-suited power setting, a real-time feedback control scheme is outlined in this paper to adjust the preset power of the ESG to create an adaptive power reference, which is then tracked using an experimental high-frequency inverter (“HFI”) that enables electrosurgery with a fundamental (sinusoidal) output frequency of 390 kHz. Subsequently, experiments using the GaN-based HFI are carried out to demonstrate the efficacy of the new variable-power approach over the conventional fixed power approach in terms of collateral tissue damage reduction.
Electrosurgery has been used for around a century. It applies HF voltage across conductive biomedical tissue along with the current dictated by the tissue impedance to elicit a clinical effect, such as cutting, coagulation, etc. The mechanism of bio-tissue incision or removal roots in the Joule energy converted from the applied electrical active power. The tissue liquid is rapidly heated up by the energy to the point of vaporization and then the tissue disperses in the form of smoke and stream. The cutting effect on a certain tissue is tightly related to the total energy delivered to it. As is known, energy is the integration of power and time, therefore, both cutting speed and cutting power impose an impact on the final cutting effect. Mismatched cutting speeding or poorly-regulated cutting power either is not able to generate desired clinical effect or may cause undesired added tissue damage, such as charring, thermal spread, dragging and so on.
ESG cutting speed is exclusively controlled by surgeons according to their clinical experience and expertise. Therefore, traditional ESG aims at delivering the power, manually set by surgeons before the surgery, as accurately as possible regardless of the tissue variation. Conventionally, this power is maintained the same during the entire electrosurgery until it is manually updated by the surgeon. As a result, the interruption of the time-sensitive surgery inevitably occurs. Furthermore, others have shown that different ESG power settings have an impact on cutting effects and thermal response, but these experiments did not incorporate thermal-feedback-based real-time power adaptation. Therefore, there is a chance that the power is not optimally orderly set and leads to increased tissue damage or undesired cutting effects. The literature either focuses on improving the power tracking accuracy or pursues a prompt response to the power setting. In one example, infrared sensing is employed to record liver surface-temperature distribution under a single power setting, and it turns out that a small radial region surrounding the ES has the highest temperature during cutting. Moreover, it is reported that thermography-based sensing can be utilized to identify solar cell aging, hotspots, and partial shading faults. However, thermal sensors are employed in existing work simply for temperature measurement. None of the known art links thermal feedback with real-time power adaptation for electrosurgery. As such, the ill-suited power setting issue persists and its resolution via power adaptation is of interest for an ESG.
To tackle the ill-suited power setting challenge and reduce collateral tissue damage, a novel real-time thermal-feedback-based closed-loop control scheme is pursued, as illustrated in
More than 70% weight of soft tissue comprises water and the tissue is removed when the applied energy vaporizes the water at 100° C. Tissue vaporization is accompanied by another phenomenon, namely, tissue denaturation when tissue cell temperature is between 60° C. and 100° C. Physical properties of tissues among individuals exhibit differences associated with gender, age, size, etc., and those differences escalate the extent of tissue denaturation when the constant power is indiscriminately applied. Tissue denaturation occurs due to undesired thermal spread, and it should be minimized by adjustment in power reference Pref(t), by modulating it with ΔP(t) around the Ppre-set, when cutting speeding is fixed.
This point is illustrated in
The full-bridge and bandpass filter-based HFI was initially introduced in Example 1 above and detailed in Example 2 above and it is redrawn in
The fundamental (sinusoidal) output frequency and switching frequency of the HFI are set to be 390 kHz. Referring to
where Vin and ƒs are the input voltage and the full-bridge switching frequency, respectively. nt is the transformer turn ratio, α is the phase shift angle between the diagonal switches in the full-bridge, and vscaling represents the voltage-sensor scaling. The corresponding output power Po(t) for a linear load, with a load angle of θ, is given by:
where Vo_pk is the peak of Vo(t) and Io_pk is the peak of io(t), the scaled output current of the HFI. ρscaling is the coefficient that maps sensed Vo(t) and io(t) back to actual output power. As seen, the first item of the Po(t) is a constant component, which is the average power (
A. Constant Power Control
The control block diagram in
The constant power reference is then compared with feedback
B. Thermal-Feedback-Based Power-Adaptation Control
To mitigate the collateral tissue damage due to nonoptimality in Ppre-set, the proposed thermal-feedback-based power adaptation control monitors tissue surface temperature using an infrared sensor and feeds it back at a frequency of 8 Hz. Based on the sensed temperature data, the power-adaptation controller feeds in real-time a power adaption term (ΔP(t)):
to the Pref(t) yielding the following, as captured in
P
ref(t)=Ppre-set+ΔP(t). (4)
In (3). Ttissue(t) is the tissue surface temperature, max(Ttissue(t)) is the maximum of Ttissue(t), and Tnom is the nominal tissue temperature that ensures safe electrosurgery with minimal/no collateral damage. By following (4), any time the max(Ttissue(t)) exceeds Tnom, Pref(t) is so adjusted such that the incision-site tissue temperature is brought back close to Tnom thereby mitigating collateral tissue damage.
A GaN-FET-based hardware prototype, as shown in
Using this setup, first, the efficacy of HFI operating, under constant power control with Pref(t)=Ppre-set=50 W (i.e., ΔP(t)=0) is shown in
Next, in
The purpose of the test scenarios 1 and 2 is to provide an illustrative approach to the choice of Tnom for determining ΔP(t) using (3) (to obtain Pref(t) using (4)) in the last 3 scenarios. As evident in
Using that range, the test scenarios 3-5, following Table II, were pursued and the results are captured in Table III. The latter shows that test scenario 3 together with its repeated trial 3′ yields overall the best results and this is evident in
This example outlines a thermal-feedback-based power adaptation control to reduce the collateral tissue damage for electrosurgery. The impact of the ill-suited power setting is illustrated, and the power adaptation scheme is elaborated. A GaN-based HFI setup is used to examine the cutting effects of constant power and thermal-feedback-based power adaptation control in terms of cutting gap and thermal spread. The experiment results show that the cutting test with a higher constant power features a larger cutting gap and wider thermal spread. Moreover, with a constant power setting, the tissue is not evenly cut with visible cutting gap and thermal spread difference at two-incision locations. With the proposed power adaptation control, the cutting gap and thermal spread are tangibly reduced with the proper choice of nominal temperature. Moreover, it is also found that thermal sensor location and its resolution have an impact on the accuracy of sensing the surface temperature of the tissue and needs careful attention. Further, the gap and thermal difference at different incision sites are found to be reduced compared to results obtained using a fixed power setting. In other words, cutting uniformity with power adaptation is improved in terms of both cutting gap and thermal spread. It is evident that power adaptation in the vicinity of accurate nominal temperature is the key to reduced collateral damage. In practical electrosurgery, this estimate can be obtained more accurately given the repeatability of reliable cutting performance and the availability of a much larger electrosurgery database.
This example investigates two ways of output-power computation, namely, sparse- and multi-sampling-based methods, to overcome sampling speed limitation and arcing nonlinearity for electrosurgery. Moreover, an impedance-based power adaptation strategy is explored for reduced collateral tissue damage.
Methods: The efficacy of the proposed power computation and adaptation strategy are experimentally investigated on a gallium-nitride (GaN)-based high-frequency inverter prototype that allows electrosurgery with a 390 kHz output frequency.
Results: The sparse-sampling-based method samples output voltage once and current twice per cycle. The achieved power computing errors over 1000 cycles are 1.43 W, 2.54 W, 4.53 W, and 4.89 W when output power varies between 15 W and 45 W. The multi-sampling-based method requires 28 samples of both outputs, and the corresponding errors are 0.02 W, 0.86 W, 1.86 W, and 3.09 W. The collateral tissue damage gauged by average thermal spread is 0.86 mm, 0.43 mm, 1.11 mm, and 0.36 mm for the impedance-based power adaptation against 1.49 mm for conventional electrosurgery.
Conclusion: Both power-computation approaches break sampling speed limitations and calculate output power with small errors. However, with arcing nonlinearity presence, the multi-sampling-based method yields better accuracy. The impedance-based power adaptation reduces thermal spreads and diminishes sensor count and cost. Significance: This example exemplifies two novel power-computation options using low-end industrial-scale processors for biomedical research involving high-frequency and nonlinearly distorted outputs. Additionally, this work is the first to present the original impedance-based power adaptation strategy for reduced collateral damage and it may motivate further interdisciplinary research towards collateral-damage-less electrosurgery.
Biomedical tissue is made of numerous cells and cellular membranes introduce capacitance to tissue, therefore, the current path inside the tissue is frequency-dependent. Capacitance blocks DC current and presents a high impedance to low-frequency AC current. Hence, the resultant current path mainly surrounds cells through extracellular liquids, as shown in
The tissue damage during electrosurgery is tightly related to electrical energy applied to the target tissue. Properly delivered energy not only minimizes additional tissue damage but also shortens the time required for post-surgery recovery. In contrast, inappropriately transmitted energy enhances undesired tissue damage, and increases safety concerns as well. As is well known, electrical energy is the integration of instantaneous power over the period that power from the electrosurgery generator (“ESG”) is activated. The cutting speed solely determines the time interval spent over a certain length when the electrode moves along the tissue surface trace during electrosurgery. Consequently, both applied power and cutting speed (or time interval of power activated) are theoretically supposed to play an important role in tissue damage during actual electrosurgery.
Others have explored thermal damage induced by fixed power with different cutting speeds. The experimental results validate the significant impact of cutting speed on electrocuting damage and emphasize the importance of cutting speed control. Moreover, others have experimentally demonstrated that various activated periods of power lead to different surgical damage even with the same power setting. Instead of varying cutting speed or power activation period, others vary applied power and show the important impact of discrete power settings on the overall cutting performance. Beyond that, others have elaborated on the generation mechanism of collateral damage due to an ill-suited power setting. Based on the foregoing, it can be concluded that cutting speed (or power activated interval) and power setting are experimentally proven to be critical for electrosurgery and should be properly controlled to reduce unwanted collateral tissue damage.
In actual electrosurgery, the cutting speed or time interval of power activation is at the surgeon's sole discretion, except that the electrosurgery is autonomously implemented by a robotic arm. In such a case, the cutting speed or power-activated time interval should be controlled by embedded servo motors inside the robotic arm with very high precision and sensitivity. Nonetheless, in either case, both cutting speed and power activation time interval are externally controlled and none of them is regulated by the ESG itself. The focus of this example centers on the power adaptation of a high-frequency inverter that enables electrosurgical trials, rather than autonomous robotic electrosurgery. Therefore, autonomous control on cutting speed or regulation of power activation time interval is not covered herein.
For conventional electrosurgery, the applied power is exclusively determined and manually entered into ESGs by a surgeon before surgery is initiated. The value of selected power is primarily decided based on surgeons' cumulative clinical experiences. There is a lack of professional procedures indicating how to quantitatively update the power setting when target tissue changes either due to tissue type or physical property variation, etc. The practical physical and electrical properties of tissue differ when it comes from distinct individuals or even from the same individual and the same tissue type but separate locations, etc. Under such circumstances, the optimal power choice, ideally speaking, should be so adjusted such that the induced collateral damage is minimized as much as possible.
Unfortunately, the power setting is usually maintained the same during conventional electrosurgery until further updates are reimported by surgeons when appreciable undesired electrosurgical effects or collateral damages have been irreversibly generated and observed. Those undesirable effects or additional damage prolong the post-surgery rehabilitation duration and should be avoided as far as possible through timely power adaptation. In practice, it is challenging for surgeons to entirely avoid power setting nonoptimality and thus, added collateral damage because they can hardly precisely and promptly identify tissue property discrepancies or variations.
Even if surgeons are hypothetically able to distinguish tissue's physical and electrical property fluctuations, it also takes them some time to halt the electrosurgery, manually reload the power setting into ESG and then reinitiate the surgical procedures again. The total time duration for time-sensitive electrosurgery is elongated as the alteration frequency of such processes climbs. The stretched clinical duration increases the risk of clinical failure or might lead to other serious consequences. Given that, there should be a viable tradeoff between the increasing power modification frequency for reduced collateral tissue damage and the decreasing power modification frequency for shortened electrosurgical time consumption.
The majority of existing literature tracks the manually entered power setting with high accuracy and rapid response. As a result, no real-time power adaptation is adopted in traditional ESG, and surgeons solely take control of the power setting. In contrast to that, a thermal-feedback-based power adaptation that can autonomously modify this power setting has been detailed. Electrocuting traces, conducted on fresh pork using such power adaptation strategy, show superiority over those cut by conventional fixed power in terms of thermal spread and cutting gap uniformity.
However, notwithstanding the apparent merits mentioned above, drawbacks exist for the former thermal-based power adaptation method. A considerable amount of smoke occurs during electrosurgery, and they suffuse between the thermal sensor and tissue, imposing a negative influence on temperature measurement accuracy. Either an advanced and complex thermal sensing compensation algorithm or a costly smoke evacuation pencil is needed to remove the adverse impact of the smoke on tissue surface temperature measurement.
Besides, the thermal sensor mounting location also plays an important role in thermal measurement accuracy, as shown in
Furthermore, the thermal sensor resolution also matters and imposes an impact on measurement granularity and precision, etc. Higher resolution yields more accurate thermal sensing while, on the other hand, higher resolution also indicates larger thermal data size, heavier data processing burden, and probably higher sensor cost. Finally, it is worth mentioning that the refresh rate of thermal sensors can hardly go beyond 100 Hz. This significantly limits the application of thermal-feedback-based power adaptation in the instance that is highly sensitive to power settings and requires ultrafast power adjustment.
Considering the limitations mentioned above for thermal-based power adaptation, the impedance-based ultrafast power adaptation is proposed in Section II-D to serve as one promising substitution. Compared to the conventional constant power scenario, the proposed power adaptation method reduces collateral tissue damage. Meanwhile, it eliminates the limitations linked to the thermal-based tactic with additional benefits of reduced sensor count, shrunken budget and communication requirement, etc. The efficacy of this novel method is examined on the full-bridge-based high-frequency inverter (HFI) that is introduced in Example 2 above with a fundamental (sinusoidal) output frequency of 390 kHz.
A. Sparse-Sampling-Based Power Calculation
It is a practical challenge to sample and precisely reconstruct signals of hundreds of kilohertz without a multi-MHz analog-to-digital converter (ADC) sampling rate. Others have sampled this kind of high-frequency signal at 50 MHz using Xilinx field-programmable gate array (“FPGA”), rather than a low-cost industrial-scale digital signal processor (“DSP”). A high ADC sampling rate is avoided by others for average output power computation. However, the output power is indirectly calculated from the input side of the high-frequency inverter with the assumption of a lossless switch network, rather than directly computed from the output (load) side. The practical switches are lossy, especially switched at high frequency. Consequently, the feasibility of this method only works to some extent and needs more examination for diverse situations. Given that the majority of commercial low-end DSPs possess only a few MHz sampling rates, therefore, a power computing algorithm (“PCA”) is provided to compute the mean value of output power over each cycle. The proposed PCA requires only one output voltage sample and two output current samples per cycle, respectively. These three data can also be utilized to estimate the load impedance in a cycle-by-cycle ultrafast manner.
When the ideal sinusoidal voltage is applied to a linear load, the resultant current is also sinusoidal and oscillates at the same frequency. The magnitude and phase of the load current depend on load impedance magnitude and type, such as resistive, capacitive, or inductive. Although the Cole-model indicates the capacitive property of the biotissue, all three linear load characteristics are delineated in
Z=|Z|∠(0) (4)
where Vopk equals to Vo(k+1) and is the output voltage peak while Iopk is the peak of output current and ƒs is the output frequency. With the assumption of sinusoidal output voltage and current, the proposed PCA can determine average output power and load impedance in a cycle-by-cycle ultrafast manner with only one voltage sample and two current samples, respectively. Hence, compared with existing literature, the ADC sampling rate requirement is significantly reduced. Furthermore, the PCA only involves simple mathematical operations with low complexity, which enables its real-time implementation in low-end industrial level DSP.
B. Impact of Arcing Presence on Output Current and Power Calculation
Both instantaneous and average power computations using (5) and (6) assume a linear load. This means that the sinusoidal voltage induces sinusoidal current with the same frequency. This assumption is adopted by most of the existing literature, and it is true if no arcing is generated by the applied voltage during electrocuting, which is generally not the case in reality. On the opposite, actual electrosurgery is often accompanied by nonlinear arcing so that the current is no longer sinusoidal. Arcing occurs when the air around the electrode is broken down (ionization of dielectric), as graphically represented in
With appreciable current distortion and potential voltage asymmetry or distortion, neither output current nor voltage should be assumed as sinusoidal anymore. Because of that, the average output power cannot be calculated by the way described in (6), otherwise, tangible power calculation errors are inevitably induced. On the other hand, it is quite important for an ESG to accurately compute the output power and then precisely track the given reference. For these reasons, a real-time multi-sampling-based power calculating method is set forth herein with the expectation of enhanced power calculating precision. The detailed illustrative explanation for it is presented in the following subsection.
C. Multi-Sampling-Based Power Calculation
The presence of large arcing heavily distorts the output current, thereby, more sampling points are necessary to reflect current characteristics. A graphical representation of output voltage and distorted current is plotted in
P
ins(k)=Vo(k)·Io(k) (7)
avg(j)T
where Vo(k) and Io(k) symbolize kth instant value of output voltage and current in each cycle, respectively. k ranges from 1 to N. Pins(k) stands for the kth instant powe.
In
|Z|(k)=max(Vo(kTs))/max(Io(kTs)) (9)
where |Z|(k) is the load impedance while max (Vo(kTs) and max (Io(kTs) represent the largest value of sampled output voltage and current within region A, respectively. If needed, then the average value of
avg
M·T
=(Σj=1MΣk=(j−1)·N+1(j−1)·N+NVo(k)·Io(k))/(M·N) (10)
|Z|M·T
where
Viewing the maximum ADC sampling rate of DSP used for this example, the value of N in (8) is selected as 28 herein. With 28 samples each cycle, the instant output powers are derived as an example for original continuous signals and discretely sampled data as exhibited in
D. Principle of Impedance-Based Power Adaptation
As stated before, the thermal-feedback-based power adaptive method can reduce collateral tissue damage, but its performance is affected by lots of factors. Therefore, impedance-based ultrafast power adaptation is proposed to conquer the influence of those factors. The principle of this novel methodology is thoroughly explained in this section.
It is reported that as the electrode designed for electrocuting gets in touch with the biotissue, the high-density current flows through the advancing edge of the tissue, followed by gradually decreased current density inside the tissue. The temperature profile of the tissue during electrocuting also quickly drops down as the radial distance measured from the electrode increases. Consequently, only tissue within a few millimeters (mm) radial distance from the electrode is vaporized and removed in the hypothetical shape of an inverted cone.
P
idl
·Δt=m·c
eq
·ΔT=½·r·h·v·Δt·ρ·ceqΔT (11a)
where Pidl(W) is the ideal average output power over one cycle, Δt (s) is the cutting time duration and m (kg) is the mass of the targeted tissue. ΔT (K) is the temperature rise while ceq (J·kg−1·K−1) and ρ (kg/m3) are the equivalent specific heat capacity and density of the tissue, respectively. They can be approximately processed as a constant for tissues of similar constituents without causing too many errors.
To simplify analyses, 3 conditions are assumed as follows:
where λ is the coefficient that relates the |Z| with the h·v (m2/s). α and β is the coefficient bridging the load impedance magnitude |Z| with electrode insertion depth h (m) and moving speed v (m/s), respectively. Instead of function type, different tissue types differ from each other only by separate coefficient values in (12). Condition 3) is experimentally certified for muscle tissue and documented in Section III-E.
Applying (12a) in (11), (13) is elicited as follows:
P
idl
·|Z|=γ (13)
where γ is a constant and equates to ½·r·λ·ρ·ceq·ΔT.
From (13), the multiplication of ideal power and load impedance magnitude is a constant. To ensure Pref(t) is equal to Pidl for reduced damage as load impedance varies, the real-time power adaptation can be generated as (14) and added to preset power reference Pset from surgeons, yielding (15):
where |Z0| is equal to γ/Pset. Based on (14) and (15), the impedance-based power adaptation strategy with reduced collateral tissue damage from
A. Sparse-Sampling-Based Power Calculation
The accuracy of power yielded from the sparse-sampling-based algorithm is experimentally examined by cutting fresh pork using the HFI introduced in Example 2 above. The output power of HFI is under closed-loop control and the power reference in
B. Impact of Arcing Presence on Output Current
The impact of arcing on output current distortion can be qualitatively probed through revising the output voltage or insertion depth, etc.
C. Multi-Sampling-Based Power Calculation
The computing accuracy performance of the new power quantification method is experimentally checked under the same experimental settings for the sparse-sampling-based approach. The acquired average power from the DSP and those obtained from DSO data are revealed in
D. Steady-State Power Tracking Performance
Utilizing the new power computing method, the steady-state power tracking performance of the HFI in Example 2 above is investigated with a proportional-integral controller and showcased in
E. Relationships of Load Impedance Against the Electrode Insertion Depth and Cutting Speed
To authenticate condition 3) mentioned in Section II-D, electrocuting trials with various insertion depths and cutting speeds are conducted with the help of a programmable Emile3 3-axes robotic gantry. Experimental results are documented in
F. Impedance-Based Power Adaptation
By following (13), the power reference is so adjusted such that the product of Pref(t) and |Z| is brought back to γ as |Z| varies. In this way, the actual electrocuting is near to ideal cutting with minimal collateral tissue damage. It is noteworthy that the exact value of γ is hard to theoretically calculate, therefore, its approximated optimal value is figured out by extensive experiments. Based upon massive trials, the value of γ used in this paper is 30000, and all load impedance in DSP is smoothed out by the approach of moving average. The process of moving averaging enhances system power tracking stability by eliminating power reference violent fluctuation that originates from load impedance sudden jump. The larger the moving average window length, the better tracking stability. However, it also compromises prompt power adaptation dynamics by slowing it down. To keep a balance, the load impedance in this example is monitored each cycle, but all impedance values used in (14) for the power reference regulation are their moving average scrolling over 10 cycles. Based on that, the efficacy of the load impedance-based power adaptation method is scrutinized with multi-sampling-based output power computation in
In test scenario 1, the cutting speed is 5 mm/s and set power is kept the same at 35 W during the entire electrocuting. The electrode is fixed on the programmable Emile3 3-axes robotic gantry and its moving speed is under control for the sake of experimental repeatability. Instead of constant power configuration, test scenarios 2-5 are equipped with power adaptation. And their cutting speeds along with electrode insertion depth are intentionally configured differently to examine the general applicability of the proposed impedance-based power adaptation philosophy. The purpose of test scenario 1 is to emulate conventional electrosurgery and it also provides a reference criterion of collateral tissue damage for test scenarios 2-5 as explained later in Table III.
Following the experimental setting listed in Table II, fresh pork is cut from the top to bottom, and 5 electrocuting traces are outcomes, as present in
The gauged thermal spread is summarized in Table III. As noticed in Table III, test scenario 1 conducted with constant power has maximum thermal spread. With the impedance-based power adaptation, the thermal spread of test scenario 2 is significantly reduced from test scenario 1. When the electrode insertion depth exceeds more than half of the entire electrode blade, as in scenario 4, the power adaptation can still reduce thermal spread, but the reduction is not as much as that in a shallow case much like test scenario 2. But in actual electrosurgery, the electrode insertion depth is controlled by surgeons with a routinely shallow insertion depth (around 6 mm) since deep cutting can be achieved by several shallow slices. Looking through test scenarios 2 and 3 or test scenarios 4 and 5, it is verified that the proposed impedance-based power adaptation works well even if cutting speed varies. For test scenarios 2 and 4 (or test scenarios 3 and 5), the cutting speed is maintained at 5 mm/s (or 10 mm/s) while the electrode insertion depth differs. It is seen that the thermal spread is always smaller for traces cut at 10 mm/s than that at 5 mm/s. It underlines the important role of cutting speed in reducing thermal spread once more. Aside from that, it might also forecast the necessity to massively collect habitual cutting speeds preferred by surgeons such that the value of γ is accordingly optimized for reductive thermal spread.
As seen in (14) and (15), the proposed impedance-based power adaptation method supervises the load impedance in real time and adjusts the power reference cycle by cycle. It implies that power reference updates in a few microseconds in consideration of 390 kHz output frequency. It is much faster than the thermal-based power adaptive manner. Fragment of power references during electrocuting together with measured load impedance for test scenarios 2-5 are plotted in
This example briefly illustrates the current paths inside the biotissue and elucidates the electrocuting mechanism to pave the way for a basic understanding of electrosurgery. Conventional electrosurgery delivers constant power to the target tissue with high accuracy. It increases the possibility of added collateral tissue damage. Surgeons can frequently and manually modify power settings to better cutting quality with less tissue damage. At the cost of doing that, the termination of time-sensitive electrosurgical processes is unavoidable and lengthens the clinical duration which might lead to serious consequences or cause potential exposure to a higher risk of clinical surgical failure. Therefore, autonomous real-time power adaptation is of paramount importance.
With the information on tissue surface temperature, the thermal-feedback-based power adaptation can reduce tissue collateral damage during electrosurgery. But it requires one extra thermal sensor and resultant cost, heavy thermal data processing, fast communication, etc. Besides that, it is also experimentally revealed in this example that the accuracy of temperature measurement is notably affected by thermal sensor mounting locations. Moreover, thermal sensor resolution and the existence of smoke during electrosurgery impose an impact on the thermal measurement precision as well. Because of that, either a costly thermal sensor with good resolution or a smoke evacuation pencil is needed to get rid of those impacts. Additionally, an advanced thermal sensing compensating algorithm might be another alternative to tackle the smoke issue for thermal sensing. Nevertheless, with the constraint of thermal sensor refresh rate, the thermal-based power adaptation can hardly refresh the power reference beyond 100 Hz, which sets a barrier to thermal sensor application in circumstances requiring ultrafast power adjustments. To crack all limitations or downsides related to the thermal-based power adaptive approach, the load impedance-based power adaptation method is brought forward in this example. Before the detailed explanation of such method, two schemes to calculate average output power with limited output measurements are formulated.
The sparse-sampling-based method requires the assumption of sinusoidal output voltage and current, which is not always true for actual electrosurgery due to the presence of arcing. But it dramatically shrinks the sampling requirements and calculates output power with only one voltage sample and two current samples each cycle. The power calculation results are analyzed through comparison with those from the digital storage oscilloscope which are thought of as the most accurate. It turns out that the sparse-sampling-based method has satisfied power computing accuracy and only a small proportion of points are of relatively large errors. Therefore, the sparse-sampling-based tactic is suited for the case with limited processor computing power or with low accuracy concerns. It might need improvement for the application that is sensitive to power precision. This example experimentally evidences the presence of arcing during electrocuting and demonstrates the current distortion. The distortion largely depends on the amount of arcing and the output current is no longer sinusoidal when heavily distorted. This observation accounts for relatively large power calculation errors seen in some of the power points quantified by the spare-sampling-based method.
Instead of sparse samples, the multi-sampling-based approach divides one output cycle into 4 linear regions for load impedance definition and calculates the output power with 28 samples per cycle. Experimental results show that the multi-sampling-based power computation values cluster very close to the values from the digital storage oscilloscope and have better power counting fidelity than that of the sparse-sampling-based formula. However, Table I reveals that power calculation errors still remain for the multi-sampling-based method with 28 samples of both outputs.
As is well-known, the greater the sample number N per cycle, the more accurate the power calculation. Therefore, a sampling number N larger than 28 is needed to further reduce power computing errors. But larger N requires a higher sampling rate that is limited by the maximum achievable sampling speed on the ADC. Moreover, the computation burden also rapidly escalates as the sampling rate increases. The output frequency herein is 390 kHz with a rough period of 2.5 μs, therefore, the digital computation speed is another key factor. In consequence, a tradeoff among power calculating accuracy, digital computing burden, and speed restriction should be maintained for satisfactory performance.
Furthermore, the cycle number M in (10) poses an impact on power tracking performance in which a smaller M features prompt power tracking, but it also compromises system control stability. On the other side, an overlarge M ensures system stability whereas it also jeopardizes system tracking dynamics. And thus, the value of M should be properly selected to achieve a balance between prompt power response and tracking stability. In viewing of typical availability of maximum ADC sampling rate and ADC channels from low-end industrial-scale DSPs, the output current is sampled 14 times each cycle by two ADC channels in this paper. The sampling initiation point of each channel is so arranged such that all 28 sampling points are evenly distributed in time without overlapping. The less distorted output voltage is sampled 14 times each cycle and another 14 sampling points are interpolated between two actual samples. In other words, N in (8) equates to 28 in this example.
If more sampling points are crucial for ultrahigh power computation precision, then a dedicated data acquisition system or high-end FPGA with a very high ADC sampling rate is an alternative solution. With the rapid advancement of technology, normal digital processors might become sufficiently powerful to handle this task in the near future. Nevertheless, the multi-sampling-based approach is more suitable for circumstances that have sufficient digital computing capability and need high fidelity as well.
Given the characteristics of the two methods, power counting is dependent on the actual circumstances presented in a given operating environment. In this example, the way of multi-sampling is embraced by viewing the importance of power fidelity in electrosurgery.
Based on the definition of impedance, the relationships of load impedance against electrode insertion depth and cutting speed are established, followed by experimental proofs. Both correlations fit in the form of inverse proportional function. But it should be noted that load impedance against the electrode cutting speed seems to saturate and diverge from the inverse proportional form when the speed is too small. To that end, data collection may prove useful to gather individual surgeons' preferred cutting speed together with electrode insertion depth. Doing so would provide more accurate links among impedance, insertion depth, and cutting speed to refine the power control method.
In this example, muscle tissue is used and its parametric properties, such as mass density, etc. are assumed to be uniform and there is no local variation. Even in this framework, it is still quite challenging to theoretically quantify the precise value of γ in (13) since the biotissue specific heat capacity is reported as a function temperature in the literature. On that account, it is also challenging to determine the value of equivalent specific heat capacity ceq. Before ceq can be precisely determined, there is a need of extensive clinical trials to experimentally determine the approximated optimal value of γ for different tissue types. On the foundation of relationships just built, the impedance-based power adaptation is developed. This novel approach takes the thermal sensor away and updates the power reference in a cycle-by-cycle ultrafast manner, namely, in less than 3 μs in this example. It is orders of magnitude faster than the thermal-feedback-based power adaptation. Therefore, the proposed method is extremely suitable for cases that are very sensitive to power mismatch and need ultrafast power modulations, such as electrosurgery on the brain, cerebral vessels, heart, etc. If rapid power adaptation is not required, then it is also easy to slow down the power reference update rate by reducing load impedance monitoring frequency.
This example details and evaluates two novel ways of computing output power using limited output measurements for electrosurgery. The sparse-sampling-based method only samples output voltage once and current twice whereas it yields output power computation with small deviation errors. With slightly increased sampling points and computation burden, the multi-sampling-based method improves accuracy even when nonlinearity from electrosurgical arcing is present on the outputs. These two methods are implemented in low-end industrial-scale processors with limited sampling speed. Consequently, they may reduce the need for high-end processors with fast sampling speed and save corresponding costs for applications involving high-frequency and highly distorted outputs.
In addition, a relationship among defined load impedance, electrode insertion depth, and cutting speed is developed from the multi-sampling-based methodology. Evolving from the relationship, an original impedance-based power adaptation strategy is further formulated with the capability of autonomous power reference updating in a cycle-by-cycle ultrafast manner. Experimental results of different test scenarios show that electrocuting traces delivered by the impedance-based power adaptation strategy yield diminished thermal spreads from conventional electrosurgery, which validates the feasibility and efficacy of the impedance-based power adaptation strategy.
Moreover, this power adaptation strategy eliminates the thermal sensor and eradicates the drawbacks associated with thermal-based power adaptation. In other words, collateral tissue damage in terms of thermal spread is reduced with the extra benefits of less sensor count and decreased cost. This demonstrates the superiority of impedance-based power adaptation over existing thermal-based power adaptation strategy.
The description of different advantageous arrangements has been presented for purposes of illustration and description and is not intended to be exhaustive or limited to the examples in the form disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art. Further, different advantageous examples may describe different advantages as compared to other advantageous examples. The example or examples selected are chosen and described in order to best explain the principles of the examples, the practical application, and to enable others of ordinary skill in the art to understand the disclosure for various examples with various modifications as are suited to the particular use contemplated.
This application is a U.S. Non-Provisional application that claims priority to U.S. Provisional Patent Application No. 63/352,046, filed Jun. 14, 2022, and to U.S. Provisional Patent Application No. 63/443,277, filed Feb. 3, 2023, which are hereby incorporated by reference in their entirety.
This disclosure was made with government support from the National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health (NIH) under award number R01EB029766. The government has certain rights in the disclosure.
Number | Date | Country | |
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63443277 | Feb 2023 | US | |
63352046 | Jun 2022 | US |