Precise Laser Incisions, Corrected forPatient Respiration With an Intelligent

Aiming SystemLou Reinisch, PhD,1* Marcus H. Mendenhall, PhD,2 and

Robert H. Ossoff, DMD, MD1

1Department of Otolaryngology, Vanderbilt University, Nashville, Tennessee2Free-Electron Laser Center, Vanderbilt University, Nashville, Tennessee 37232

Background and Objective: Patient motion due to respirationand blood flow can negatively affect the precision of a laser in-cision.Study Design/Materials and Methods: The video image of the sur-gical field is monitored by a computer system, and trends in themotion are ‘‘learned’’ by the computer. The laser beam is thenadjusted to compensate for predicted motion. Occasional erraticmotion sometime causes a false prediction. In this event, the pre-diction is corrected with real-time feedback.Results: Our experience shows that even with occasional falsepredictions, the motion compensation still gives a better incision.The surgeon always maintains control of the laser. The net effectof the intelligent aiming system is to subtract away nearly allpatient motions.Conclusion: Laser surgery can be performed with greater accu-racy and reduced unwanted tissue damage with the predictivetracking of motion. Lasers Surg. Med. 20:210–215, 1997.© 1997 Wiley-Liss, Inc.

Key words: beam delivery; computer-controlled scanning; laser surgery; motiontracking; robotics

INTRODUCTION

To use a laser effectively in surgery, onemust be concerned with the control and deliveryof the laser light. Several innovative delivery andmonitor systems are being developed in the Com-puter-Assisted Surgical Techniques (CAST) pro-gram at the Vanderbilt University Medical Cen-ter [1–3]. The union of the computer and roboticsto assist the surgeon with laser beams presentsmany new and exciting applications. For in-stance, the computer is capable of tracking thenatural motions of a patient, and the computercan then adjust the direction of the laser to com-pensate for the motion. The computer can also‘‘subtract’’ these motions from the video image ofthe surgical field. Thus creating a still imageeven when motion is taking place. The trackingsoftware uses a maximum entropy analysis topredict future motion from its recent history. Theimplementation of tracking to the computer-con-

trolled scanning creates an attractive device to beused with nearly all surgical laser systems.

Machines have been used to assist manthroughout history. High technology has pro-duced machines that can perform high-precisiontasks at incredible speed. Advanced technologyand state-of-the-art electronics are not new tomodern medicine. Specific examples of high tech-nology in medicine include X-rays, laser, ultra-sound, and magnetic resonance imaging. Not onlyare these technologies firmly implanted in the

Contract grant sponsor: Department of Defense for the Med-ical Free Electron Laser Program, administered by the Officeof Naval Research; Contract grant number: N00014-87-C-0146.

*Correspondence to: Lou Reinisch, Ph.D., Department of Oto-laryngology, Vanderbilt University Medical Center, Nash-ville, TN 37232.

Accepted for publication December 18, 1995.

Lasers in Surgery and Medicine 20:210–215 (1997)

© 1997 Wiley-Liss, Inc.

U.S. medical care system, but medicine embracedthese high technologies very early in their devel-opment. It stands to reason that high technologywill continue to be imported into medicine. At thesame time, medicine will motivate and drive thedirection of selected technologies.

Most appropriately, consider the history ofthe surgical laser. Theodore Maiman firstachieved lasing in ruby on May 16, 1960 [4]. Itwas only 18 months later in December 1961 thata prototype ruby laser was used to destroy a ret-inal tumor in a patient [5]. Leon Goldman, M.D.,is often cited as the force to move the laser fromophthalmology to non-ophthalmologic medicine[6,7]. He established a medical laser laboratory in1962 at the University of Cincinnati. Laser sur-gery equipment was already in the marketplacein 1965, less than 5 years after the first workinglaser was developed in a physics laboratory.

Research continues toward the developmentof improved applications of the laser. One area ofinvestigation is the finer control of the laser. Thiswould lead to increased precision in ablationwhen compared to hand-held probes and micro-manipulators. This finer control can be achievedwith the aid of computers. A computer-directedlaser beam would offer greater precision and flex-ibility in the shape and size of incision. The cur-rently available Hexascan™ has shown this to betrue; however, the Hexascan™ is limited in thatonly hexagonal excisions can be made [8–10]. Inaddition, Sharplan has introduced two hand-pieces: the Swiftlase™ moves the CO2 laser beamin a rosette pattern, and the Silk Touch™ hand-piece moves the laser beam in a spiral, to avoidthe ‘‘hot spot’’ created at the center of the rosette.

The development of handpieces to move thelaser beam and computer-controlled scanning de-vices is progressing at a fast pace. Silver Creekhas a handpiece for the CO2 laser that produces aspiral pattern, similar to the Sharplan handpiece.In addition, some of the new handpieces allow forgreater flexiability in the pattern scanned. Co-herent is marketing the CPG or Computer Pat-tern Generator, capable of moving the laser beamin many different pre-programmed patterns. Thebeam from this scanner is not focussed, but colli-mated with a small cross section, so the scannedpatterned is always ‘‘in-focus.’’

COMPUTER-ASSISTED SURGERY TECHNIQUES

At the inception, the CAST system replacedthe joy stick of the micro-manipulator on a surgical

microscope with servos and computer direction(see Fig. 1) [1–3]. The image through the surgicalmicroscope is inserted on the computer screen. Onthe same screen, the surgeon then draws the lineor pattern that he or she wishes to incise. Usingthe computer together with the laser, the surgeonis capable of making an incision with minimallateral damage and extreme precision.

The computer-controlled scanning laserbeam must track with patient motions. The mainthrust of this research initiative is the addition oftracking to the laser scanning and the subtractionof motion from the image of the surgical field.

MATERIALS AND METHODS

The details of the CAST system can be foundin previous publications [1–3]. With the CASTsystem, the surgical field is monitored by a videocamera (MKC-301A, Ikegami, Tokyo, Japan). Thevideo signal is input into a Macintosh computer(PowerMac 7100 AV) with a built-in video card(Apple Computer Co., Cupertino, CA). The tissueis tagged with one or more pins with green heads.These pins are readily identified by the computerand serve as fiducial points. The center of the pinsis computed from the image pixels. The resolutionof position is therefore slightly greater than thepixel image size. Since the surgical field is viewedthrough a surgical microscope (OPMI 1, Zeiss,Germany) for microsurgery (up to 25× magnifi-

Fig. 1. Diagram of the basic concept behind the Computer-Assisted Surgical Techniques (CAST) system. The direction ofthe laser beam is controlled by two mirrors on servos. Theservos are controlled by a computer system. The ablation tar-get is imaged with a video camera, and those images aredisplayed, real time, on the computer screen. The x, y, and zaxis system is shown on this figure for clarity in text discus-sions.

Precise Laser Incisions 211

cation, 400 mm objective lens), and the video cam-era also images through the microscope, a singlepixel is approximately 20 mm. This is sufficientresolution for a laser beam diameter of 200 mm ormore.

The Macintosh is programmed to locate agreen spot from a video image with a combinationof Apple Script (Apple Computer Co., Cupertino,CA), LabView 2.0 (National Instruments, Austin,TX), and assembled C code (C++, SymantekCorp., Cupertino, CA). This process is one of therate-limiting steps. Several years ago we were con-cerned about the speed of the computer to handlecolor video images at a 30-Hz rate. The newestcomputers, the Power Macs with the RISC proce-sor, are sufficiently fast to process the images at 30Hz.

The motions of the pins are monitored for ashort time (approximately 30 s). In all the studiespresented here, a single fiducial point was used.The next-generation systems will use morepoints. The x-y motion of the pin is computed andrecorded by the computer. These coordinates willbe analyzed with the Maximum Entropy Method(MEM) to predict future motion.

The MEM tracks motions that are periodic orfollow a functional trend (a smoothly varying firstand second time derivative of the displacement).Any motion that is slow compared to the videoframe time (16 ms) can be tracked with MEM. Inotolaryngology–head and neck surgery, the pa-tient motions are primarily due to breathing,pulse, and heart beat. All of these motions areslow compared to 16 ms. In fact, there are veryfew physiological motions that are too fast totrack with MEM. Examples of the few motions toofast to track would include spasmodic tremors andmotions of the eye.

RESULTS

The motion of the chest on a normal, awakemale volunteer was imaged, and the motion aswell as the tracking is shown in Figure 2. Forpresentation purposes, the motion along only oneaxis is shown. This axis was chosen to representmost of the chest motion. Instead of using a pin asa fiducial point, a green dot was fixed to the chestwith rubber cement.

As a limiting case, we show the tracking ofthe abduction and adduction of the vocal foldsfrom an anesthetized canine in Figure 3. Again,the motion along one axis is shown. The axis wasselected to be parallel to the major motion. The

tracking missed one point during an irregularrespiration pattern. The tracking recovered at thenext point and continued to track the motion withminimal deviations.

DISCUSSION

After the motion is tracked for some initialtime, the computer will start to predict the mo-tion, make corrections and input the actual mo-tion. The predictions will be adjusted for the ac-tual motion. We have tried this technique tofollow the breathing from a person at rest and theabduction and adduction of the vocal folds from acanine larynx. The dog was anesthetized by injec-tion and the vocal folds were videotaped with anendoscopic camera. The coordinates of the fiducialpoints were then analyzed with the MEM and themotion was predicted. After each prediction, theactual motion was added to the MEM calculation.As shown in Figures 2–3, we have the motion andthe predicted motion. Excellent agreement isseen. In Figure 3, the fourth breath cycle wasshorter than normal. Still, the predictive analysiswas able to adapt after one missed prediction.

One could look at the one ‘‘missed’’ point ofthe prediction and might conclude that the sys-tem does not work. That is unfair. This is the firstprediction ever made, and it is remarkably accu-rate without any refinements. Also, the onemissed point is far superior to no tracking, wherethe majority of the points would ‘‘miss.’’ We notethat a scanner without tracking would simplymove along the x-axis in Figures 2 and 3. It is alsoimportant that the tracking does not become‘‘lost’’ after missing a single point and fail to cor-rect itself quickly.

The ability to predict the motion as shown in

Fig. 2. The recorded motion of a chest during respiration. Thesolid line is the measured displacement. The gray, dashedline is the predicted motion using the maximum entropymethod (MEM). The prediction correlates quite well with themeasured displacement.

212 Reinisch et al.

Figures 2 and 3 is useless to a surgeon in the formof a plot. The motion prediction must be used toredirect a laser or surgical tool or to subtract themotion from the real-time video image of the sur-gical field. In this first phase of the research, weused the motion predictions to adjust the videoimage on the computer monitor. If the adjustmentis done correctly, the motions of the tissue do notshow on the video monitor.

Thus far, all the motion has been from themoving of a single fiducial point. It is then as-sumed that the object does not change shape.Instead, the object is assumed to translate uni-formly. In reality, the shape is often not pre-served. Also, objects can rotate. We will increasethe monitoring to three fiducial points per image.The motion in the x and y directions can then bethe sum of translations, rotations, and linear dis-tortions (a simple spring model of tissue). If thishomogeneous linear model is not sufficient to sub-tract the motions, more fiducial points and higher-order equations can be used to describe the mo-tion. It will be clear from the video image if themotion is being subtracted appropriately. Jump-ing and transient distortions in the image willresult if the motions are subtracted inappropri-ately.

We anticipate that three to five fiducialpoints will be sufficient to describe the motion.Three fiducial points will provide six data points(changes in the x and y position for each point).We can use this information to determine fivechanges in the tissue: x translation, y translation,rotation, stretch of the x direction, and stretch ofthe y direction. This may not perfectly describe

the motion. The use of additional fiducial pointswould permit nonuniform stretch in the x or ydirection. There is a limit to the number of fidu-cial points and the complexity of the motion. Wemust have all the calculations completed in 16 msto keep the system real-time. We expect the newPower Mac computers to be sufficiently fast tomake any reasonable calculation. In addition, wehave an optimized compiler for the computer andeach step of the programming is being carefullyoptimized for speed.

We are not concerned with motions in the zdirection (toward and away from the observer) atthis point. The surgical microscope is routinelyused with a 400-mm objective lens. We use400-mm objective lenses to image through laryn-goscopes. The very large f number (f $ 20) of themicroscanner and the long wavelength of a CO2laser translates into a large depth of field. Objectscan displace nearly 1 cm in the z direction andstay in focus with the microscanner. Motion in thez direction might become important in other sys-tems. We feel that it is a significant problem, butone that can be solved.

Since we are limiting our system to the com-monly used surgical microscope and the mostcommon surgical laser, we can fortunately ignoremotions in the z direction. That does not mean theproblem is difficult to solve. There are severalmethods on the market that keep the object infocus along the z-axis. Coherent has build a hand-piece that delivers collimated laser beams withsmall cross sections. This eliminates the focal dis-tance problem. The problem has also been solvedon most new automatic 35-mm cameras. In fact,Canon and Nikon also have predictive auto-focusor auto-focus tracking on their professional cam-eras.

Other Tracking Systems

We have considered many types of trackingsystems before selecting the MEM. Tracking isused in various fields. In the field of biomechan-ics, computers assist in the tracking of humanmotion [11]. These tracking programs are analyt-ical, providing an analysis of rotation of limbs andforce. The programs are not optimized for predic-tion. Yet, we do follow the research to learn ofnew methods of digitizing images and target iden-tification. For instance, a ‘‘TrakEye’’ system isused to film a horse running [12]. It digitizes theimages and monitors the motion of the fetlock.The system tracks amazingly well, but does notoperate in a real-time mode.

Fig. 3. The recorded motion of the canine vocal fold. The solidline is the measured motion. The dashed line is the predictedmotion using the maximum entropy method (MEM). Thelarge overshoot in the fourth cycle is due to the faster thannormal adduction of the vocal folds. The feedback quicklycorrects the problem.

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Target tracking and real-time adjustmentsare investigated for a wide range of applicationsin ophthalmology. In a very recent study, a laserDoppler velocimeter was used to stabilize the eyefor hemodynamic measurements [13]. The mo-tions of the eye are fast and quasi-random. So,these systems use fast ‘‘cameras’’ and fast proces-sors to measure the motion and compensate forthe motion after the fact. There is no predictioninvolved. The system we are proposing uses theMEM to predict motion from the recent history.Since many of the physiological motions arequasi-periodic, we will utilize the predictive capa-bilities of the MEM. This permits us to use slowercameras. These cost concerns are important forpotential commercial applications.

The Department of Defense funds a largeamount of tracking research. There are two pur-poses behind their funding. They are searchingfor ways to track the jitter of communication sat-ellites to optimize information transfer. They arealso searching for ways to track targets to in-crease weapon effectiveness. The jitter in the mo-tion of a communication satellite comes frommany different sources, such as gyroscopes, ser-vos, the earth’s gravitational field, elastic forcesin tension and bending, solar and lunar gravity,solar radiation, and micrometeroid impacts, justto name a few [14]. Systems developed for point-ing, acquisition, and tracking (PAT) attempt tominimize the jitter effects [14,15]. Like the eyemotion, the satellite jitter is fast, compared to thesampling time, and random. The PAT systemsmeasure the motion and compensate.

A recent publication compared three differ-ent methods of target tracking [16]. A compari-son is made between the Simple Threshold (ST)Method, the Truncated Sequential ProbabilityRatio Test (SPRT), and the Retinal Motion Detec-tor (RMD). The RMD is an algorithm that isloosely modeled after biology [17,18]. The tech-nique is interesting and is being considered indetail. The tests reported by the authors indicatethat the RMD works as well as the ST method orSPRT (but not better) in good lighting conditions.However, in conditions of low light, luminancegradients and contrast reversals, the RMD out-performed the other methods. In general, thelighting in a surgical field is very good. Neverthe-less, we might be able to adapt some of the RMDalgorithm into the MEM system.

One final remark concerns a study of thecontrol strategies used to direct the hand to inter-cept a moving target [19]. In experiments per-

formed with humans and an object moving at aconstant velocity, a latency period was measuredbetween the appearance of the target and the mo-tion of the hand. The latency period was modeledas the sum of two components: a fixed processingtime, and the time taken for target motion to bedetected. If the target moved faster, the time todetect motion decreased and so did the latencyperiod. The hand and eye are relatively good attracking moving targets. This research suggests apredictive algorithm is used to control the handafter the initial velocity is determined. If there isa random motion of the target, the initial motionof the hand was independent of the target veloc-ity. However, after a time period elapsed for thebrain to compute the target velocity, the handwould increase its velocity. The peak hand veloc-ity depended upon the target velocity and wassimilar for all subjects. This again suggests pre-dictive control of the hand. The time at which thepeak occurred varied substantially among thesubjects. This suggests some ‘‘guessing’’ or othervariation in the control mechanism.

ACKNOWLEDGMENTS

This work was supported, in part, by a grantfrom the Department of Defense for the MedicalFree Electron Laser Program, administered bythe Office of Naval Research under grantN00014-87-C-0146.

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