Учебники / Computer-Aided Otorhinolaryngology-Head and Neck Surgery Citardi 2002

In automatic registration protocols, registration is complete after the software recognizes the location of fiducial points and calculates the relationship between these points and the defined position of the DRF. The relocatable frame does not need to be on the patient. As a result, registration accuracy in an automatic registration approach reflects the quality of the preoperative imaging data set, and surgical navigation accuracy depends upon a variety of factors, including repositioning of the relocatable frame and the registration accuracy, among other factors.

In automatic registration paradigms, RMS calculations only summarize the overall quality of the preoperative imaging study and the software’s ability to locate the fiducial points of the headset. In this approach, the geometric relationship of the fiducial points is fixed—there is no significant variation between the anticipated and real positions of the points, since the points are all fixed in an array that does not change. As a result, the concept of RMS does not apply to automatic registration in the same way as it applies to manual registration, where the deviations between the anticipated and the actual positions of fiducial points are routinely encountered. In manual registration, the array of fiducial points (analogous to the fixed fiducial system in automatic registration) is defined for each case by the selection of fiducial points on the preoperative images; as a result, the geometric relationships among these points is unique for each case. As corresponding points from the surgical field are manually mapped to these points, deviations between their actual and anticipated positions are inevitable. RMS summarizes the degree of deviation between the actual and anticipated position for each point.

The design of the system for automatic registration is important for the success of automatic registration. Because the software performs the registration with almost no opportunity for user input, the software must accurately reconstruct models based on the preoperative image data, and the software must precisely identify the location of the fiducial markers in the preoperative imaging data set. To the extent that these processes introduce errors due to miscalculation, incorrect assumptions, etc., the overall robustness of automatic registration suffers. In addition, the arrangement of the fiducial markers in the relocatable frame is also very important. Ideally, these markers should be set so that they surround the anticipated area of interest. For this reason, the fiducial system for the CBYON ENT headset (CBYON, Palo Alto, CA) is configured like a box that surrounds the region of the paranasal sinuses.

The relocatable frames can also be adapted to support a semi-automated registration paradigm (Figure 4.11). In this approach, the surgeon performs manual point mapping to points on the relocatable frame. The steps are as follows:

1.The surgeon manually selects the fiducial points on the relocatable frame in the preoperative imaging data set.

(A)

(B)

FIGURE 4.11 (A) In semi-automatic registration, the ILD (which is more accurately described as a DRF in this situation) must be directly attached to the patient. Fiducial markers on the headset in the preoperative imaging must be manually selected in the preoperative CT scan. Subsequently, the headset must be removed. Because the DRF is attached directly to the patient, the headset is no longer needed when point mapping is complete. (From CBYON, Palo Alto, CA). (B) In this photo, the CBYON headframe has been removed, since is use is redundant after semi-automatic registration has been achieved.

2.The DRF is secured directly to the patient, typically through a neurosurgical Mayfield head holder or a similar device.

3.The relocatable frame is placed on the patient.

4.The tracking system is prepared, and its function is confirmed.

5.The surgeon then localizes to the fiducial points on the relocatable frame.

6.The computer software calculates the registration.

7.The relocatable head frame is removed.

8.The surgeon must confirm the accuracy of surgical navigation.

Although semiautomatic registration has many features associated with manual registration paradigms, semiautomatic registration tends to be much faster. In semiautomatic registration, the initial selection of fiducial points in the preoperative imaging data set is very rapid, since small markers on the headset are easier to locate than standard fiducial markers and anatomical landmarks. Similarly, intraoperative localization to the fiducial points is faster for semiautomatic registration, since the points are easier to recognize than traditional fiducial points.

4.5CONTOUR MAPPING REGISTRATION PARADIGMS

Registration may also be achieved by mapping surface contours from models derived from the preoperative imaging and the corresponding areas of the operating field (Figure 4.12). In theory this approach provides a large number of fiducial points; the resultant registration should be very high quality.

The basic steps for registration that is based upon contour maps are as follows:

1.The computer builds three-dimensional models from the two-dimen- sional preoperative imaging data through a process known as segmentation. Early segmentation routines were manual and required a great deal of user input; contemporary segmentation routines are mostly automated, although some user input may be necessary.

2.The tracking system, including DRF, is set up in the standard fashion.

3.The surgeon then localizes to a large number of points on various curvilinear surfaces.

4.The computer then calculates a registration that fits the contour defined by the intraoperative localization data to the contour defined by the segmentation models.

5.The surgeon must confirm surgical navigation accuracy by localizing against known landmarks.

(A)

(B)

FIGURE 4.12 (A) The SurfaceMerge registration protocol for the LandmarX system (Medtronic Xomed, Jacksonville, FL) serves as an enhancement of the system’s standard manual registration. After initial manual registration, the surgeon can choose an additional 40 random points along curvilinear surface contours by simply localizing at these points. The computer then calculates a contour from these points and fits it to the surface contour generated by segmentation modeling of the preoperative imaging data set. This screen capture shows the distribution of points along the patient’s forehead, glabella, and external nose. Points at each tragus are not shown. (B) The VectorVision z-Touch registration system (BrainLab, Heimstetten, Germany) uses a laser light, whose reflection from the surface contour serves to define that contour. The z-Touch laser handpiece is shown here. In this approach, standard manual point mapping is not necessary, and a relocatable headset does not need to be used.

Initial contour mapping routines required multiple manual localizations (typically 30–40 or more, depending on the specific area of interest). Contour mapping has also been used as a refinement step that can be performed after simple point-mapping registration. Newer approaches utilize a laser, whose reflected light provides localization data about the intraoperative surface contour.

Contour-based registration is an attractive alternative. It seems simple, and the large number of fiducial points suggests good registration accuracy. Unfortunately, early reports have suggested that contour-based registration is less than precise than comparable routines based upon standard manual point mapping.

Contour-based registration has not been widely adopted for a number of reasons. First, the protocols for contour-based registration are newer, and surgeon familiarity with them is limited. Furthermore, these early protocols likely require additional refinement that will take additional time.

Additional technical improvements can be anticipated in the segmentation routines that drive the development of contours. This type of registration is dependent upon segmentation routines for the reconstruction of precise three-dimen- sional models, from which the contours are derived. If the segmentation routines are compromised by concerns imposed by limited computer memory and microprocessor speed, by poor preoperative imaging quality and/or by poor software design, then contour-based registration will always be suboptimal. Admittedly, faster, more affordable computer hardware is becoming available, but the software that is currently available or will be available in the foreseeable future is often designed to run on hardware with poorer performance. (It is less expensive to use older hardware, operating systems, and software drivers, and the various flaws in older computer equipment have mostly been discovered and corrected.) The quality of the preoperative imaging also must be considered. The best segmentation models require fine-cut computed tomography (CT) data (1 mm slice thickness or better); many scanners cannot provide the large number of slices of that thickness without overheating. Newer and better scanners will solve this problem. Since the high-resolution scanners are more expensive to purchase and operate, the availability of the appropriate CT scans cannot be assured.

Certain limitations are inherent to all contour-based registration protocols. It is doubtful that any amount of development will overcome these intrinsic features. The distribution of the fiducial points as well as their number influences the quality of the registration. A large number of points that are coplanar or nearly coplanar will provide a poor registration, and surgical navigation error will grow as one localizes to points further from the plane of fiducial points. In fact, the three-dimensional geometry of the fiducial points may be more important than the number of points. Contour-based registration typically relies upon a large number of points from areas such as the forehead and cheeks, which are relatively flat and coplanar; this is simply not an optimal arrangement. Furthermore, the

involved contours are not fixed; changes in facial expression, overall body weight, and skin turgor will all influence the contours.

4.6 IMAGE FUSION

Image fusion refers to a software technique that blends corresponding images from two different imaging data sets. In this way, two different CT scans or two different magnetic resonance imaging (MRI) scans can be merged. Alternatively, a CT scan and an MRI scan can be merged. In this way it is possible to morph between two different sets of images.

The process of aligning the corresponding images is a type of registration, since it involves mapping corresponding points. Registration for image fusion is relatively straightforward. It requires that the user identify corresponding fiducial points in each data set. The computer then maps the corresponding points to each other and thereby aligns the images. The fiducial points may be standard external fiducial markers, bone-anchored fiducial markers, anatomical landmarks, and/or markers built into relocatable frames. The CAS software may contain specific software tools that facilitate this process. Such tools are helpful, but not mandatory for successful registration for image fusion.

4.7 IMAGE-ENHANCED ENDOSCOPY

In image-enhanced endoscopy (IEE), a new feature in the CBYON Suite (CBYON, Palo Alto, CA), the CAS computer calculates the perspective view from the tip of the surgical telescope and projects both the virtual endoscopic view of the preoperative imaging data and the real world endoscopic view in real time (Figure 4.13). Both the perspective view generated by the software and the endoscopic view through the telescope must be tightly aligned for useful IEEE. This requires a specific registration protocol. The system must also reconstruct a high-quality perspective three-dimensional model with movement, and the tracking system must monitor instrument position. Of course, the analog video single from the endoscopic camera must be sent to the CAS computer’s video card for digital conversion.

During IEE registration, the telescope is focused on a grid pattern; the software can recognize this pattern and align a perspective model of a similar grid (Figure 4.14). In this way, the real view and virtual view are aligned.

IEE must be especially robust, since even a small amount of error renders IEE useless. Obviously, the major factor in the IEE registration process is its software, since so much of the entire process is software-derived segmentation and modeling. Powerful computers with sophisticated graphics cards are necessary to run this graphics-intensive software.

4.8 REGISTRATION FAILURES

It is important to distinguish between problems with surgical navigation and problems with registration. Since registration maps corresponding fiducial points in the preoperative imaging data set volume and the operating field volume, registration failures reflect misalignment of these points. Surgical navigation fails when the estimated accuracy of the system against known landmarks is suboptimal. In fact, surgical navigation errors may arise, and the registration may still be in tact. (Admittedly, surgical navigation and registration errors can overlap, and from a clinical perspective the implications can be very similar. However, users of CAS must be able to understand the fundamental issues so that they can troubleshoot the technology effectively.)

Probably the most important factor in the fidelity of registration is the quality of the fiducial points as well as their arrangement in 3D space around the area of surgical interest. The best fiducial points can be easily recognized on the preoperative images and in the operating field. Even in automatic registration paradigms, the software routines must be able to locate the fiducial markers in the preoperative imaging. During manual registration using anatomical fiducial landmarks, it is critical that the surgeon precisely identify the selected points in the operative field during registration and that these points precisely match the fiducial points selected on the preoperative images.

All registration approaches require a theoretical minimum of three fiducial points. In practice, a greater number of fiducial points are used, since the greater number tends to enhance the precision of the resultant registration; however, above a certain number (approximately 6–12 points, depending on the application), the impact of each additional fiducial point on the overall registration is progressively less critical. For this reason, large numbers of fiducial points are not used unless contour mapping is the basis of the registration. (In registration based on contour mapping, corresponding contours are aligned; these contours are defined by large numbers of individual points, but the individual points themselves are not mapped to their corresponding points.) In fact, a large number of fiducial points may degrade the net registration accuracy, since an aberrant fiducial point, which becomes more likely as all of the preferred fiducial points have been used, will have a profound impact on the registration process.

The spatial distribution of the fiducial points influences registration fidelity. Ideally, the fiducial points are distributed in 3D space around the entire surgical area. If some or all of the fiducial points are coplanar (i.e., in the same plane) or the fiducial points are located far from the operative field, then the registration in the area of interest will be poor. For instance, if all fiducial points are located on the skin surface overlying the frontal bone, then the registration may be acceptable in the immediate frontal area; however, the registration will deteriorate as one moves from the area of fiducial points to other regions of the paranasal si-

nuses. On the other hand, arranging the fiducial points so that they surround the sinuses will yield a much higher registration throughout the paranasal sinuses. In this example, the placement of fiducial points at the tragus or temple areas as well as the anterior frontal area provides very good registration.

The quality of the preoperative imaging data set greatly influences the results of all types of registration. Higher resolution scans provide better data for the localization of fiducial markers preoperatively. This is true both in automatic systems, where software locates the markers automatically, and in manual systems, where the user must choose the fiducial points. Simply stated, a thin 1 mm axial CT is always preferable to a similar CT scan performed at a greater slice thickness. In a sense, the thickness of the slice determines the functional limit of registration accuracy (and surgical navigation). The system cannot be more precise than the slice thickness in the preoperative imaging data. As a result, recommendations for the use of 2 mm (or 3 mm) slices effectively sets the lower limit of accuracy at 2 mm (or 3 mm) under the best possible circumstances. When these thicker slices are used, it may be unreasonable to anticipate accuracy of better than 2 mm.

Since registration procotols are driven by the software that runs them, the precision of the software processes influences registration. In early CAS systems, limited hardware resources for manipulation of large amounts of image data led programmers and designers to approximate some image manipulations so that the software would run efficiently. Obviously, these choices compromised the ultimate fidelity of calculations for the sake of speed. In addition, inadvertent software code error (poor programming) also can degrade registration. For many applications (especially early applications), these issues were not significant; however, as the surgical applications have grown more sophisticated and as the resultant demands on the CAS systems have increased accordingly, these limitations may be more problematic, especially when older CAS systems are used for more complicated applications.

In manual registration, the tracking system also must function well so that the surgeon can perform point mapping. Failure of the tracking apparatus and damaged (i.e., bent) instruments can compromise intraoperative tracking.

User error can also cause poor manual registration. To the extent that the actual location of the CAS probe tip differs from the position intended by user during point mapping, the calculated RMS value for the registration will rise and the resultant registration will suffer. In other words, if the user intends to place the CAS probe tip at one location, but the tracking system localizes the probe tip to another location, then that difference is a mapping error. Each mapping error contributes to the total error of the registration. Contemporary tracking systems can track instruments with a precision of 1 mm (or even better) in laboratory settings; when a registration error occurs due to misalignment of corresponding points, failure of the tracking system rarely causes this problem. The real issue