 |
Registration is the process by which the MRI or CT data is transformed to the coordinate frame of the patient. The most common form of registration uses fiducials [1,15,20,23]: either markers attached to the skin or bone prior to imaging or anatomically salient features on the head. The fiducials are manually localized in both the MR or CT imagery and on the patient and the resulting correspondences are used to solve for the registration. Fiducial systems may not be as accurate as frame-based methods-Peters [17] reports fiducial accuracy about an order of magnitude worse than frame-based methods, but Maciunas [15] reports high accuracy achieved with novel implantable fiducials.
Another registration approach is surface alignment in which the MRI skin surface is aligned with the patient's scalp surface in the operating room. Ryan [18] generates the patient's scalp surface by probing about 150 points with a trackable medical instrument. Colchester [5] uses an active stereo system to construct the scalp surface. We also perform the registration using surface alignment[9], benefiting from its dense data representation, but use either a laser scanner to construct the patient's scalp surface or a trackable probe to obtain data points from the patient's skin surface for registration.
We have used two related methods to register the reconstructed model to the actual patient position. In the first method, we use a laser scanner to collect 3D data of the patient's scalp surface as positioned on the operating table. The scanner is a laser striping triangulation system consisting of a laser unit (low power laser source and cylindrical lens mounted on a stepper motor) and a video camera. The laser is calibrated a priori by using a calibration gauge of known dimensions to calculate the camera parameters and the sweeping angles of the laser. In the operating room the laser scanner is placed to maximize coverage of the salient bony features of the head, such as nose and eye orbits. To ensure accurate registration we can supplement the laser data with points probed with a Flashpoint pointer, similar to [18], to include skin points that are not visible to the laser in the registration. The acquired laser data is overlaid on the laser scanner's video image of the patient for specification of the region of interest. This process uses a simple mouse interface to outline the region of the head on which we want to base the registration. This process need not be perfect--the registration is designed to deal robustly with outliers. The laser scan takes about 30 seconds once the sensor is appropriately placed above the patient.
An alternative method is to simply use a trackable probe to acquire data. In this case, we trace paths on the skin of the patient with the trackable probe, recording positional information at points along each path. These points are not landmarks, but simply replace the lines of laser data. The registration process is the same, whether matching laser data or trackable probe data to the skin surface of the MRI model.
One of the keys to our system is the integration of a reliable and
accurate data-to-MRI registration algorithm. Our registration process
is described in detail in [11]. It is a three step process
performing an optimization on a six parameter rigid transformation,
which aligns the data surface points with the MRI skin surface. The steps
consist of:
(1) A manual initial alignment in which we roughly align the two
surfaces. Accurate manual alignment can be very difficult, but we
aim only to be within 
of the correct transformation which
subsequent steps solve for. This process is performed using two
displays
and takes about 60 seconds. In
one display, the rendered MRI skin is overlaid on the laser
scanner's video view of the patient, and the MRI data is rotated and
translated in 3D to achieve a qualitatively close alignment. In the
second display, the laser data is projected onto three
orthogonal projections of the MRI data. The projected MRI data is
colored such that intensity is inversely proportional to distance from
the viewer. In each overlay view, the laser data may
be rotated and translated in 2D to align the projections.
An alternative to manual initial alignment is to record three known points using the
trackable probe (e.g. tip of the nose, tip of the ear), then identify
roughly the same point in the MRI model. This process
determines a rough initial alignment of the data to the MR
reconstruction, and typically takes less then 5 seconds.
(2) Automated interpolated alignment which performs optimization
over a large region of convergence
[9,10,11]. This process runs in about 10
seconds on a Sun UltraSPARC workstation. The method basically solves
for the transform that optimizes a Gaussian weighted least-squares fit
of the two data sets.
(3) Automated detailed alignment which performs optimization to
accurately localize the best surface data to MRI transformation[9,10,11].
This process runs in about 10 seconds on a Sun UltraSPARC
workstation. The method basically solves a truncated
least-squares fit of the two data sets, refining the transformation
obtained in the previous step.
Three verification tools are used to inspect the registration results as the objective functions optimized by the registration algorithm may not be sufficient to guarantee the correct solution. One verification tool overlays the MRI skin on the video image of the patient, (Figure 2), except that we animate the visualization by varying the blending of the MRI skin and video image. A second verification tool overlays the sensed data on the MRI skin by color-coding the sensed data by distance between the data points and the nearest MRI skin points. Such a residual error display identifies possible biases remaining in the registration solution. A third verification tool compares locations of landmarks. Throughout the surgery, the surgeon uses the optically tracked probe to point to distinctive anatomical structures. The offset of the probe position from the actual point in the MR volume is then observed in the display.