1006 IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 53, NO. 6, JUNE 2006

Automatic Temperature Controller for MultielementArray Hyperthermia Systems

Jessi E. Johnson*, Member, IEEE, Paolo F. Maccarini, Member, IEEE, Daniel Neuman, andPaul R. Stauffer, Member, IEEE

Abstract—This paper concerns the optimization and perfor-mance analysis of an automatic control algorithm for managingpower output of large multielement array hyperthermia applica-tors. Simulation and corresponding measurement of controllerperformance in a solid tissue equivalent phantom model is utilizedfor analysis of controller response to dynamically varying thermalload conditions that simulate clinical treatments. The analysisleads to an optimum controller which demonstrates the abilityto achieve a uniform and stable temperature profile over a largesurface area regardless of surrounding thermal load. This paperpresents several advancements to the performance of a previouslypublished control routine, including: 1) simplified simulation tech-niques for thorough characterization of controller performance;2) an optimization procedure leading to an improved hybridcontrol algorithm for maintaining optimal performance duringperiods of both “rising” and “steady-state” temperature; 3) per-formance analysis of a control algorithm tailored for large areahyperthermia treatments with a mulitelement array applicator.The optimized hybrid controller is applied to the conformal mi-crowave array (CMA) hyperthermia system previously developedfor heating large area surface disease such as diffuse chestwallrecurrence of breast carcinoma, and shown to produce stable,uniform temperatures under the multielement array applicatorfor all thermal load conditions.

Index Terms—Control systems, hyperthermia, microwave an-tenna arrays, temperature control.

I. INTRODUCTION

THE significant enhancement of therapeutic efficacy ofcancer treatments combining hyperthermia (42 –45

for 60 min) with conventional radiation and/or chemotherapyhas led to the development of numerous hyperthermia tech-niques utilizing radio frequency (RF), microwaves and ul-trasound [1], [2]. For superficial disease such as chestwallrecurrence of breast cancer, which often spreads over a largesurface area, uniform heating can be particularly difficult interms of conforming to typical body contours and irregularly

Manuscript received January 20, 2005; revised September 25, 2005. Thiswork was supported by the National Institute of Health (NIH) under Grant 2RO1 CA70761-06. Asterisk indicates corresponding author.

*J. E. Johnson was with the Department of Radiation Oncology, University ofCalifornia, San Francisco, CA 94143 USA. He is currently with the Departmentof Electrical and Electronics Engineering, Nagasaki University, Nagasaki 852-8521 Japan (e-mail: johnsonj@ieee.org).

P. F. Maccarini and P. R. Stauffer were with the Department of RadiationOncology, University of California, San Francisco, CA 94143 USA. They arenow with the Department of Radiation Oncology at Duke University MedicalCenter, Durham, NC 27710 USA (e-mail: paul.stauffer@duke.edu).

D. Neuman is with the Department of Radiation Oncology, University of Cali-fornia at San Francisco Medical Center at Mount Zion, San Francisco, CA 94115USA.

Digital Object Identifier 10.1109/TBME.2006.873559

shaped disease. In recent years, a number of multiple-elementhyperthermia applicators have been developed to meet theclinical challenges of applying hyperthermia over increas-ingly larger surface areas [3]–[12]. The largest of these is theconformal microwave array (CMA) applicator which featuresa flexible structure and up to 32 individually powered dualconcentric conductor (DCC) antennas which allow the treat-ment of irregularly shaped disease over a wide variety of bodycontours. Although this system has been used successfully inthe patient clinic for several years, thermal monitoring andcontrol for uniform and stable temperature profiles across largetreatment areas are major shortcomings that have limited wide-spread application of the approach [13], [14]. Thus, in spiteof major improvements in system hardware that provide moreadjustable heating patterns and improved real-time temperaturemonitoring, control of power levels to these large applicatorsremains a major challenge. Manual power adjustments are te-dious and too slow for large multiple-element applicators. Thus,an automatic control routine is necessary based on temperaturefeedback from tissue under each independently powered heatsource, which is possible with recent high density temperaturemonitoring approaches [15]–[19]. In the current literature, thereare many groups who have developed automatic controllersfor various hyperthermia systems including: RF and ultra-sound [20]–[22]; scanned-focused and phased-array ultrasound[23]–[25]; microwave [26]; and high-temperature hyperthermia[27]. These systems utilize many control techniques rangingfrom basic proportional integral derivative type control [21] tocomplex, model predictive control [27].

Zhou et al. [26] present a control system based on a recur-sive algorithm developed from Pennes bioheat equation [28].The controller demonstrated good accuracy for control of large,multielement hyperthermia applicators, as well as a fast calcu-lation time. Although this system demonstrated success, severalaspects of the controller were not analyzed in detail, including:parametric variation in the control parameters; techniques fordetermining optimum parameter sets; and effects of system per-turbations on the controller performance.

In this paper, the recursive control algorithm proposed byZhou [26] is thoroughly analyzed and tested with simulationsand measurements of controller performance in a tissue equiv-alent phantom with variable thermal loading. As a result of thisextensive characterization, general trends in the controller re-sponse for variations in the control parameters are determined.Utilizing these trends, the controller performance is optimizedfor use with the CMA hyperthermia system which is represen-tative of other multielement array hyperthermia applicators.

0018-9294/$20.00 © 2006 IEEE

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II. METHODS

A. Goals for Control System

For automatic control in any clinical hyperthermia system,several performance characteristics must be considered. Thecontroller should be able to raise the temperature of a treatmentvolume from its baseline (typically 32 –37 to a settemperature within a therapeutic window—commonly definedas 42 –45 . For investigations of controller performancein phantom tests, the desired set temperature was fixed at 43with only a allowable variation. The more stringentlimits of this narrow temperature window were utilized toensure that the controller can easily maintain temperatureswithin the 42 –45 window when applied to a lesspredictable, heterogeneous and dynamically variable livingsystem. Common ‘rising region’ characteristics describing therise from the baseline to a stable, set temperature within thedesired temperature window consist of the rise time, overshoot(OS), and settling time.

1) Rise Time: In this paper, a nominal set temperature of 43was used and the rise time was considered as the time re-

quired for the treatment volume to reach that temperature. Witha nominal 37 baseline temperature, the controller shouldachieve a rise time of less than 12 min (a slope of 0.5 )to provide a thermal treatment of 60 cumulative equivalent min-utes at 43 (CEM43) within a reasonable time frame. Further-more, the controller must not raise the temperature faster thanaround 1 (or in less than 6 min), since more rapid slopesmay cause patient pain and discomfort.

2) Overshoot: Upon initial warm up the maximum tempera-ture overshoot must be below 1 , where the overshoot is de-fined as the difference between the peak temperature and thedesired set temperature .

3) Settling Time: The settling time is defined as the time ittakes for the temperature response to settle to within the desiredtemperature window (in this case ). The goal forthe settling time is a maximum of 12 min, corresponding to themaximum allowable rise time.

Once the temperature has reached steady-state, the goal forthe controller is to maintain a stable set temperature by adaptingto changes in the body such as blood perfusion rate. The positionof a CMA aperture may shift with respect to the correspondingtemperature sensor during treatment and the controller must beresponsive to this as well. Furthermore, the operator should beable to change the set temperature during treatments. If the tem-perature is raised, it should increase with a similar rising slopeas described previously (0.5 to 1 ). When lowered,the temperature decrease should be as rapid as possible, sincelowering may be required as a result of overheating and patientpain.

The controller should have the ability to control an entirearray, with independent control of the temperature underneatheach antenna. Finally, the controller should be able to utilizetemperature feedback information from a variety of sourcesincluding: 1) A single point reading in the center of the an-tenna at a depth of approximately 5 mm; 2) A single, centralpoint reading on the skin surface; 3) a single, radiometricsignal representing a volume-averaged temperature under the

Fig. 1. Experimental setup for characterization of microwave hyperthermia.

antenna center. In this paper, a fiber-optic thermometry systemis utilized to take single-point temperature measurements at5 mm depth. However, preliminary studies of the controllershow it can be utilized with fiber-optic probe readings at otherdepths [11] and should also be applicable using radiometricreadings taken with a receive antenna connected to a microwaveradiometer [13], [17].

B. Experimental Setup for Characterization of MicrowaveHyperthermia

The experimental setup utilized to emulate clinical mi-crowave hyperthermia treatments is shown in Fig. 1. A muscleequivalent tissue model is formed out of solid TX-150 phantom[29] encased in thin plastic. A phantom structure is constructedwith multiple layers of encased phantom, allowing fiber opticsensors to be placed on the top surface of the phantom and at5 or 10 mm depth. In order to emulate heat dissipation due toblood perfusion, the top and bottom water boluses are utilized.The boluses are connected via plastic tubing to a water circu-lation and temperature control system which rapidly circulatesthe water through both bolus layers.

A CMA applicator is placed on top of the upper bolus to applypower to the phantom structure. Two types of applicators wereutilized in this experiment. The first applicator (CMA1) is asingle-aperture, 3 cm square DCC antenna [30] and was usedthroughout the development of the single-channel controller, asdescribed in Section II-E. The second applicator (CMA2) is amultielement (5X4) array of 3 cm square DCC antennas [13],[31]–[33] utilized to test the controller in a multichannel con-figuration, as described in Section II-F.

The CMA applicators are powered by a multichannel, 915MHz RF generator (AMT1125, American Microwave Tech-nologies, Canoga Park, CA) which provides up to 100 W ofpower to each of eight channels. As discussed in previous work[13], multiple channels are driven incoherently to achieve auniform heating profile. The fiber-optic temperature probes areconnected to a fiber-optic thermometry system (Luxtron3000,Luxtron Corp., Santa Clara, CA) which has a precision ofapproximately . Both the AMT1125 and Luxtron3000are connected to a PC, with the AMT1125 linked through adata acquisition card (NI PCI-6221 M Series MultifunctionDAQ, National Instruments, Austin, TX) and the Luxtron3000connected to the serial port. Control commands were sent to thedevices utilizing National Instrument’s LabView 7.0 software,which allowed rapid development of instrument drivers as wellas easy implementation of the control algorithm.

1008 IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 53, NO. 6, JUNE 2006

C. Modeling the Solid Phantom Using the Bioheat Equation

Developing a model for the solid phantom structure describedin Section II-B allows for rapid and in-depth analysis of the con-trol system’s characteristics. For a single measurement point,hyperthermia effects can be modeled by a modified version ofthe single-point bioheat transfer equation presented in [27]

(1)

where is the tissue density; is the specificheat of both tissue and blood; and are the tissue and ar-terial (core) temperatures, respectively; and is an“effective” perfusion rate [34] describing the combined effectsof heat conduction, metabolic process, and blood convection.The [W] and terms are multiplied to estimate thevolumetric power deposited in the tissue, where is the avail-able power at the antenna input and is a correction factor.Defining the volumetric power in this way is useful for calcu-lating the response of the model in terms of the available power,which is an easily determined value in practice. The termis used to correct for the fact that some of the available poweris reflected at the input of the antenna and also adds a scalingfactor which would result from integrating the power densityover an infinitesimal volume at the temperature measurementpoint. Modeled temperature-time responses can be formulatedby discretizing and rearranging (1) into

(2)

where and are the previous and current temperatures,respectively.

To characterize the response of the solid phantom structure,some initial ‘open loop’ temperature-time measurements wereconducted by heating the solid phantom using CMA1 with aconstant applied power for 30 min, and then turning the poweroff and measuring 5 min of cool down. This measurement pro-cedure was conducted for several power levels between 5 and25 W. After conducting these measurements, (2) was utilized tomodel the equivalent temperature-time response. The responseof the model was matched to the measurements by estimatingthe values of , , and from previous results [21], [27] anddetermining through iteration. The results of this model de-velopment are presented in Section III-A.

D. Control Algorithm

The starting point for the controller development is the recur-sive algorithm of [26], given by (3)

(3)

where is the updated power level, is current powerlevel, is the set temperature, is the current temperature,

is the previous temperature, and and are the controlparameters. In this paper, two variations of this controller areutilized. The first is given by (4)

(4)

where is the next power update, is the recursivelycalculated power value from (3), and is the maximumpower limit if the algorithm calculates an updated power whichis too large. The term helps reduce overshoot and allowsthe maximum power level to be tailored for patient treatments.

For optimal application in the CMA system, the control algo-rithm of (3) was further modified according to (5)

(5)

where and represent a set of control parameters which areinput into (4) to give an optimum response when the tempera-ture is rising significantly; represents a “rising threshold”which determines if the temperature is in a rising state; and

represent a set of control parameters which are input into (4)to give an optimum response when the system is settled close toits set temperature; is the absolute maximum difference be-tween and over the previous seven temperature updates;and is the “steady-state threshold” which determines if thetemperature is in the steady-state region. The controller of (5)operates as follows. When the present temperature is well belowthe set temperature [dictated by the term in(5)], the control algorithm utilizes parameters and to giveoptimal “rising” characteristics. These parameters are active astreatment begins, as well as when the operator significantly in-creases the set temperature during a treatment. The termdetermines how far the present temperature must be belowthe set temperature to engage the ‘rising’ control parame-ters. Once the present temperature has settled in close to the settemperature [determined by the term in (5)], thecontrol algorithm utilizes the “steady-state” parameters and

to give a response which reacts optimally to perturbationsin the system (such as blood perfusion changes), as well as toreductions in the set temperature (giving quick temperature de-creases). The “steady-state” parameters are activated once thepresent temperature has settled to within degrees of theset temperature and stayed within this limit for seven temper-ature updates. Note that the power limiting term is alsoutilized for this controller. The modified controller of (5) allowsfor robust control of the temperature-time response, as discussedbelow.

E. Single-Channel Controller Development

The single-aperture CMA1 applicator was utilized in the ini-tial single-channel development of the control system. A sys-

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TABLE ITEST 1: PERTURBATIONS IN BOLUS TEMPERATURE

TABLE IITEST 2: PERTURBATIONS IN EFFECTIVE PERFUSION RATE

tematic method was utilized to design the controller for op-timum performance, as follows.

1) Simulations of Basic Controller Behavior: The responseof the basic controller of (4) can be characterized over wide vari-ations in the control parameters ( and ) by utilizing simula-tion of the phantom model described in Section II-C. The fol-lowing constraints were imposed on the simulations: an initialtemperature of 37 , a set temperature of 43 , and an initialpower of 1 W. A set of preliminary simulations and measure-ments were conducted to determine reasonable values for themaximum power and sampling time (12 s).Measurements and corresponding simulations also showed thatthe controller of (4) remained stable for and values greaterthan 50 but that the temperature response tended to become verynoisy for values greater than 20 and values greater than 10.

Once the above constraints were determined, the tempera-ture-time response was simulated for combinations of (withinthe range 0.1 to 20) and (within the range 0.1 to 10). Thissimulation allowed the effect of variation in and on the“rising” characteristics (rise time, overshoot, settling time) tobe analyzed in detail, as shown in Section III-B.

2) Rising Controller Design: The extensive simulations de-scribed above in Section II-E1 give a range of and valueswhich may be utilized to estimate a controller with optimalrising characteristics. The controller can then be “fine tuned”utilizing measurement as follows. First, variation in the mea-sured rising characteristics was analyzed for a sample of and

values to verify the accuracy of the simulated results. Mea-surements utilizing and values laying within the rangepredicted by simulation were then used to “fine-tune” a com-bination giving the most favorable response. Since the bloodperfusion rate has been shown to vary significantly during hy-perthermia treatments [35], [36], the rising characteristics weresimulated for this optimum controller over changes in the effec-tive perfusion term.

3) Steady-State Controller Design: The next step in the evo-lution of the controller is to analyze its reaction to the clinicalchallenges of rapidly varying blood perfusion and surroundingtissue temperature. Two perturbation tests were developed inorder to reproduce these clinical challenges. For Test 1 the tem-perature-time response was measured and the phantom struc-ture (load) was perturbed by varying the top and bottom bolustemperatures as detailed in Table I. Test 2, detailed in Table II,

TABLE IIITEST 3: TEMPERATURE SENSOR POSITION CHANGES

utilized simulations of the phantom model to analyze instanta-neous blood perfusion rate changes by changing the termat various times during the treatment. The simulations of Test2 were made more realistic by introducing a randomtemperature noise into the solid phantom model. Tests 1 and 2allowed for determination of and values that produce a con-troller optimally responsive to practical clinical situations.

4) Hybrid Controller Design: Both of the optimal parametersets from the “rising” and “steady-state” controller designs canbe utilized with the modified control algorithm of (5) to forma ‘hybrid’ controller. The hybrid design is configured such thatthe “rising” parameters are utilized when the set temperature ismuch greater than the current temperature and the “steady-state”parameters are utilized once the current temperature has settledto within range of the set temperature. This gives an initial op-timal rising characteristic as well as optimal responsiveness tosystem perturbations during steady state. In order to test for ro-bust operation of the “hybrid” control system, it was subjectedto the perturbation tests (Test 1 and 2) described above in Sec-tion II-E3.

To investigate the ability of the control algorithm to maintainpower control during patient movement, Test 3 was performedas summarized in Table III. This test utilized three temperatureprobes at 5 mm depth, with one probe placed in the center, oneon the side, and one on the corner of the 3 cm square DCCantenna aperture. The sensor used by the control algorithm waschanged to different positions during the test. This simulatedthe effect of patient movement shifting the position of a singletemperature probe relative to the antenna aperture.

F. Multichannel Controller Development

After performance of a single-channel hybrid controllerwas optimized, multiple-channel control was investigated.For a large array of antennas and temperature control points,multiple-input multiple-output (MIMO) feedback schemes aretypically utilized [22]. However, a major advantage of the CMAapplicator design is the very low level of interaction betweenadjacent antennas that results from the highly directional,uncoupled power deposition characteristics of noncoherentlydriven DCC antenna array elements [13], [31], [37]. Since verylittle coupling between channels is expected, the single-channelhybrid controller of Section II-E4 can be applied indepen-dently to each channel, with no cross-channel control terms.To test for proper operation with this approach, the multipleantenna CMA2 applicator was utilized with six of its DCCantennas (in a 3X2 configuration) applying heat to the solidphantom structure. Each of the six antennas was individuallyand independently operated utilizing the hybrid controller ofSection II-E4. Recording temperatures at 5 mm depth under thecenter of each of the six antennas, set temperatures were variedto different levels for each antenna over a 60-min duration.

1010 IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 53, NO. 6, JUNE 2006

Fig. 2. Measured and corresponding modeled response of solid phantom witha constant applied power for 30 min, followed by a 5-min cool down.

III. RESULTS

A. Response of Solid Phantom Model

The model values resulting from matching the simu-lated temperature-time response of (2) to measurements are:

, ,and . Fig. 2 compares the modeled temper-ature-time response to the measured data for power levels of5, 15, and 25 W, and demonstrates that the model predicts themeasured response accurately.

B. Simulations of Basic Controller

As described in Section II-E1, the solid phantom model wasutilized in a temperature versus time simulation with the con-troller of (4). An instructive method of analyzing the results ofthe simulation is to utilize contour plots which show trends inthe overshoot, rise time, and settling time as a function of and

. Fig. 3 shows such a contour plot, with Fig. 3(a) showingovershoot; Fig. 3(b) rise time; and Fig. 3(c) settling time. Fig. 3should be observed with the goals of Section II-A in mind, anovershoot less then 1 , a rise time of 6 to 12 min, and a set-tling time of 12 min or less. For each part of Fig. 3, the dashedcontour lines represent values within the desired range and thesolid lines show values outside. Fig. 3(a) shows that the over-shoot becomes unreasonably large for small values of

combined with small values of . Fig. 3(b)shows that the range of optimal rise times fall within values of

greater than around 8 and values of less than 2.5. Finally,Fig. 3(c) shows that the settling time is approximately 12 or lesswhen is greater than 5.

Fig. 4 shows the superposition of Fig. 3(a)–(c), with the solidlines showing overshoot, dashed lines showing rise time, anddotted lines showing settling time. The shaded portion of Fig. 4shows the region where the overshoot, rise time, and settlingtime are all within the optimal specified ranges. It is clear fromthis figure that the rise time is the dominant constraint for thiscontroller.

C. Rising Controller Results

As described in Section II-E2, the accuracy of simulationunder automatic control was verified by measurement. Fig. 5shows both measured and simulated analyses of changes inovershoot, rise time, and settling time where, after choosing astarting point of ( , ), the value of is variedand is held constant. Fig. 5(a) shows the response of theovershoot, and verifies the conclusions obtained from simula-tion alone, that the term must be 6 or larger. The figure alsoverifies the accuracy of the model in estimating the heatingcharacteristics of the solid phantom. Fig. 5(b) shows that thereis also good agreement between the simulated and measuredrise and settling times. However, while simulation suggestsvalues from 6 to 10, the measurements suggest a broader rangeof approximately 6 to 16 will keep the rise time within thedesired range of 6 to 12 min. Within the 3 to 16 range for , themeasured settling times are approximately below the 12-minmaximum and correspond closely to simulation. The measuredand simulated temperature-time response was also analyzedover changes in , with held at 10, and exhibited similarmodel accuracy (data not shown).

The accuracy of the model allowed a sample of the optimalsimulated combinations of and (Fig. 4) to be confidentlyutilized in subsequent measurements to determine a combina-tion of and that gives an optimum rising charac-teristic. The measured and simulated temperature-time responseusing this optimum ‘rising’ controller is shown in Fig. 6, andresults in a measured overshoot of 0.2 , rise time of 7.6 min,and settling time of 5.6 min. Furthermore, simulations (data notshown) demonstrate that this controller has reasonable rise timesranging from around 9 to 12 min over effective perfusion varia-tion from 0.2 to 8 .

D. Steady-State Controller Results

The optimal rising controller of Section III-C ( ,) was analyzed for steady-state performance by conducting

the perturbation Tests 1 and 2 previously described in Table Iand Table II. Fig. 7(a) shows the temperature-time responsefrom the measured test of bolus temperature perturbation (Test1, Table I). At time , the 37 to 32 decrease in thebolus temperature causes a decrease in the controlled temper-ature, which reaches a minimum of 42.3 at around 30 min.Another 0.7 deviation results following the 10 bolus tem-perature increase from 32 to 42 at time . Here, thecontrolled temperature increases to a maximum of 43.7 at47 min before returning slowly to the set temperature 12 minlater.

Fig. 7(b) shows the temperature-time response for the sim-ulated test of perturbation due to effective perfusionchanges (Test 2, Table II). At time , the change in from2.9 to 1.45 causes a rapid increase in temperature up to43.7 (at 32 min) which takes almost 8 min for the controllerto correct. Returning to 2.9 at time causes a decreasein temperature to 42.6 , which again takes almost 7 minto stabilize. The subsequent doubling of effective perfusioncooling rate to 5.8 at time causes a further decreasein temperature to a minimum of 42.2 , which takes another7 min to correct.

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Fig. 3. Simulated changes in temperature-time response with variations in a and c1: (a) overshoot ( C); (b) rise time (min); (c) settling time (min).

Fig. 4. Simulated changes in temperature-time response with variations in a

and c1: superposition of overshoot, rise time, and settling time.

To enable the controller to be more responsive to system per-turbations, the term can be increased. The effect of on con-troller performance in Test 1 and Test 2 is indicated in Table IV.The table lists the following characteristics: standard deviationin temperature (SD) calculated from 10 to 60 min for Test 1and from 10 to 120 min for Test 2; minimum and max-imum deviation from the 43 set temperature; over-shoot (OS); and rise time (RT). The table shows that largervalues reduce the SD, and values for both tests. Forthe largest values of , the standard deviation in temperatureapproaches the response with no system perturbations present.Although the effects of perturbations are reduced by increasing

, increased overshoot (OS) and unacceptably decreased risetime (RT) also result.

E. Hybrid Controller Results

Based on the rising controller (Section III-C) and steady-statecontroller (Section III-D) results, a hybrid controller describedby (5) was implemented with the following parameter values:

, , , , and. was set purposefully larger than the typical

maximum deviation in temperature response, to ensure that therising parameters will not be activated during minor steady-statetemperature fluctuations. was set corresponding to the max-imum allowable steady-state variation defined previously for thesettling time.

Fig. 5. Measured and simulated changes in temperature-time response withconstant c1 = 0:4 and variable a: (a) overshoot; (b) rise and settling time.

The hybrid controller was subjected to the perturbation testsof Table I and Table II. Fig. 8(a) shows a comparison of thebolus temperature perturbation test (Test 1, Table I) for three im-plementations of the control algorithm: 1) the optimum risingcontroller of Section III-C ( , ); 2) the op-timum steady-state controller of Section III-D ( ,

); 3) the hybrid controller design. Note that a smoothingfunction is utilized on the temperature data to improve clarity.The hybrid controller follows the rising controller during ini-tial warm-up, then begins following the steady-state controller.Fig. 8(b) shows a similar comparison of the rising, steady-state,and hybrid controllers for the effective perfusion test Table II.Note that a smoothing function is also used in this figure. Fig.

1012 IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 53, NO. 6, JUNE 2006

Fig. 6. Measured and simulated temperature response of optimal rising con-troller (a = 8, c1 = 0:4).

Fig. 7. Response of optimal rising controller (a = 8, c1 = 0:4) to perturbationtests: (a) Test 1, Table I; (b) Test 2, Table II.

8(b) also demonstrates that the hybrid controller tracks the risingand steady-state responses quite effectively.

As described in Section II-E4, the hybrid controller was alsotested for its ability to remain stable for changes in temperaturesensor location (Test 3, Table III). Fig. 9 shows the results of thistest. As the position is switched from the center to the side at 20min, the controller releases control from the center position andquickly brings the temperature at the side position to a stable 43

. At 40 min, a similar transition can be seen when the controlpoint changes from the side position to the corner position andthe temperature again stabilizes at 43 .

F. Multiple-Channel Control Results

The optimal hybrid controller design of Section III-E wastested in the CMA2 multichannel configuration by varyingthe set temperatures of different channels as described inSection II-F. The results of this multichannel test are shownin Fig. 10. Initially, all six channels (CH1–CH6) are set to43 . At 15 min, the set temperature goals of CH5 and CH6are changed from 43 to 42 and the phantom tissue underthe antennas quickly reaches 42 after 1.5 min. At 30 min,the set temperatures of CH5 and CH6 are increased to 45 .Here, the channels rise with a slope of around 0.5 ,reaching 45 after 6 min. The set temperature of CH1 andCH2 are changed from 43 to 44 at 15 min and take 3 minto adjust (a rising slope of 0.33 ). At 30 min, CH1and CH2 are lowered from 44 to 41 . Here CH1 transitionsfaster than CH2, with CH1 reaching 41 after 3.5 min andCH2 taking 7.5 min to make the same decrease. Finally, CH3and CH4, which were left at a constant 43 set temperature,remain stable at 43 for the entire duration of the test.

IV. DISCUSSION

Development of a controller suitable for application in theCMA system has led to several significant advancements. First,a practical experimental tissue-equivalent phantom structureand corresponding computer model were designed to analyzethe expected performance of an automatic controller duringsimulated hyperthermia treatment tests. The experimentalphantom facilitated tests of: basic control with no systemperturbations; temperature perturbations due to water bolustemperature changes (Test 1); set temperature changes; and per-turbations due to temperature sensor position change (Test 3).The corresponding model utilizes a straightforward single-pointequation that was simplified to express the temperature as afunction of the available power provided by the RF generator.The model clearly demonstrates the ability to accurately predictthe open-loop temperature-time response (Fig. 2), as well asthe response during automatic control (Figs. 5 and 6). Thus,the model facilitated extensive studies of control algorithmresponse to variations in numerous model parameters.

An existing automatic controller for hyperthermia was exten-sively characterized for practical implementation and optimiza-tion in terms of the “rising” and “steady-state” response. Figs. 3and 4 demonstrate some important relationships among , ,and the rising response. Most significantly, these figures clearlydemonstrate that the rise time is the dominant constraint for thecontroller to meet the goals of the CMA system. This allows therising controller development to be focused on meeting the risetime goals with confidence that the resulting overshoot and set-tling time will remain within specification. Taking a sample ofthe control parameters falling within the optimal range (shownin Fig. 4), further measurement and simulation allowed devel-opment of a controller giving an optimal rising characteristic(Fig. 6). Beyond simply analyzing the controller in terms of therising characteristics, steady-state stability and responsivenesswas tested. Fig. 7 clearly demonstrates that a controller whichgives optimal rising characteristics may still be susceptible toperturbations to the system during steady-state. The tempera-ture fluctuations exhibited in Fig. 7(a) and (b) take a significant

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TABLE IVEFFECTS OF CHANGES IN c1 PARAMETER (a = 8) WITH PERTURBATIONS IN BOLUS TEMPERATURE

Fig. 8. Comparison of rising, steady-state and hybrid controllers subjected toperturbation tests: (a) Test 1, Table I; (b) Test 2, Table II.

amount of time to re-stabilize, thus compromising the temper-ature uniformity of the response. Increasing the value of theparameter is a good technique to compensate for controller sus-ceptibility to perturbations, as demonstrated in Table IV. How-ever, raising increases the rise time [Fig. 3(b)], revealingan inverse relationship between acceptable rise times and con-troller susceptibility to perturbations. This relationship led to thedevelopment of a hybrid design that accommodates the differingrequirements of the rising and steady-state regions.

Since combinations of and meeting all design require-ments were not obtainable with the basic control algorithm of(4), it was expanded into the improved hybrid controller of (5).The comparison plots in Fig. 8(a) and (b) clearly demonstratethe usefulness of using the hybrid controller implementation.The temperature response of the rising controller wanders atpoints of bolus temperature or effective perfusion change [as

Fig. 9. Temperature-Time response of hybrid controller. Controlling sensor po-sition changed from center to side at 20 min, and from side to corner at 40 min.

Fig. 10. Temperature-Time response during 6 antenna hyperthermia session:initial set temperature of 43 C for all channels; Ch 1 and 2 set to 44 C at15 min and 41 C at 30 min; Ch 5 and 6 set to 42 C at 15 min and 45 C at30 min.

demonstrated at times and in Fig. 8(a) and inFig. 8(b)]. The steady-state controller is insensitive to the per-turbations, but has significant overshoot and a rise time wellbelow the 6-min limit. The hybrid controller gives an ideal re-sponse, with good rise time, small overshoot, and insensitivityto perturbations.

The hybrid controller also performed well when subjected tofurther testing that simulated the practical clinical application ofheating large area chestwall disease under an array applicator.Fig. 9 (Test 3) demonstrates that changing the position of the

1014 IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 53, NO. 6, JUNE 2006

controlling temperature sensor does not cause any instability inthe controller. The figure also reaffirms that the heating patternof the DCC antenna is fairly uniform, resulting in only arounda variation in all temperatures when changing controlpositions underneath the 3 cm square aperture.

Finally, the single-channel hybrid controller was expanded toa multichannel control situation. Fig. 10 demonstrates the abilityof the control system to independently adjust power levels to sixantennas to achieve stable system operation with multiple tem-perature control points. The rising and falling times achievedin the multichannel configuration resulted in favorable tempera-ture-time slopes, with temperature decreases being rapid and in-creases being close to the desired 0.5 –1 slope.The relative noninteraction among antennas in the DCC CMAapplicator design allows the use of independent control algo-rithms for each antenna. Although it would be feasible to addMIMO algorithms to the controller, the performance tests in thiseffort demonstrate that the current hybrid controller should besufficient for well regulated control of large CMA applicators,as well as other systems with little interaction among elements.

V. CONCLUSION

An in-depth analysis of an automatic control routine tailoredfor accurate temperature control of hyperthermia treatmentswith multiple-element array applicators was presented. Anexisting control algorithm was improved for optimum respon-siveness in both the rising and steady-state regions of operation.The controller was applied to a six element sub-section of aclinical CMA hyperthermia system and its performance testedwith simulations and experimental measurement in a tissueequivalent phantom model that incorporated variable thermalloading. Typical thermal load testing resulted in general con-troller response relationships useful for applying the controllerto other systems. The controller presented in this paper is inthe process of being implemented in the 32-channel clinicalCMA system at the University of California, San Francisco.This should greatly improve treatment of large area surfacedisease by increasing overall uniformity of heating both spa-tially and temporally, and simplifying treatment proceduresfor the operator. The simulation and controller optimizationtechniques presented in this paper should prove useful forothers seeking to develop temperature controllers for mid- tolarge-area hyperthermia applicators.

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Jessi E. Johnson (S’98–M’04) received the B.S. de-gree in electrical engineering and the Ph.D. degreein electrical engineering, with a specialization in RFand microwave circuit design, from the University ofCalifornia, Davis, in 1999 and 2004, respectively.

From 2004–2005 he conducted a postdoctoral fel-lowship in the Microwave Hyperthermia Laboratoryat the University of California, San Francisco. Heis currently a Visiting Research Fellow with Dr. T.Takenaka’s research group at Nagasaki University,Japan. There he is working on the development of

microwave imaging systems for biomedical applications. His other researchinterests include design of microwave hyperthermia systems and microwaveradiometry.

Paolo F. Maccarini (S’97–M’03) was born inTorino, Italy, in 1971. Selected for a DoubleDiplome European Program in 1993, he receivedthe Diplome in telecommunications (1995) from theENSERG, Grenoble, France (1995), the Laurea inmicrowaves and optoelectronics from Politecnicodi Torino, Torino, Italy (2001), the M.S. degree ininstrumentation from INPG, Grenoble, France, andthe M.S. and PhD degrees in circuits and solid-statetechnology at the University of California at SantaBarbara, California (2002).

He worked at CERN, Geneva, Switzerland (1996–1997). He also worked atApple Research Labs in Paris, France (1995). He is now a Postdoctoral Fellowat the University of California, San Francisco, working on microwave hyper-thermia treatment and radiometric diagnostics for oncology and other medicalapplications.

Daniel Neuman received the B.S. degree in physicsfrom the University of California, Santa Cruz, in1997 and the M.S. degree in physics from SanFrancisco State University, San Francisco, CA, in2000.

He is currently working as a Development En-gineer for the Hyperthermia Research Group atthe Mount Zion Cancer Center, San Francisco.His research interests include hyperthermia cancertreatments with microwave radiation, radiometricsensing, and medical instrument design and control.

Paul R. Stauffer (M’81) received the BA degree inphysics at the College of Wooster, Wooster, OH, in1975 and M.S.E.E. degree in electrical engineeringfrom the University of Arizona, Tucson, in 1979.

He is currently Professor in the Department of Ra-diation Oncology at the University of California, SanFrancisco, and Director of Physics and Engineeringfor Hyperthermia. His current research interests in-clude engineering development and testing of RF, mi-crowave, and ultrasound applicators for uniform heattreatment of superficial and deep lying tissues. He has

published over 125 papers and book chapters in the field of hyperthermiaDr. Stauffer is board certified in clinical engineering (1983) and medical

physics (1991). He is an Associate Editor for the International Journal ofHyperthermia.