Biomedical Engineering Reference
In-Depth Information
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 cali-
bration 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 Ryan
et al
.
[29]
, to include skin points that are not visible to the
laser in the registration. The acquired laser data is over-
laid 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
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, using a mouse-driven graphical interface.
This process determines a rough initial alignment of the
data to the MR reconstruction and typically takes less
than 5 seconds.
It is also possible to automate this process, by using
search methods from the computer vision literature. In
[14]
, we describe an efficient search algorithm that
matches selected points from the patient's skin to can-
didate matches from the skin surface of the MRI model.
By using constraints on the distance and orientation be-
tween the sets of points, these algorithms can quickly
identify possible registrations of the two data sets. Ap-
plying the coordinate frame transformation defined by
each match, the full set of data points from the patient's
skin surface can then be transformed to the MRI frame of
reference. Residual distances between the transformed
data points and the MRI skin surface serve as a measure
of fit and can be used to determine good candidate initial
alignments.
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.
The key to our system is the integration of a reliable and
accurate data-to-MRI registration algorithm. Our regis-
tration process is described in detail in Grimson
et al
.
[14]
. 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.
d
Refined alignment
Given the initial alignment of the two data sets, we
typically have registrations on the order of a few centi-
meters and a few tens of degrees. We need to automat-
ically refine this alignment to a more accurate one.
Ideally, we need algorithms that can converge to an
optimal alignment from a large range of initial positions
[12-14]
.
Our method iteratively refines its estimate of the
transformation that aligns patient data and MRI data.
Given a current estimate of the transformation, it applies
that estimate to the patient data to bring it into the MRI
coordinate frame. For each transformed data point, it
then measures a Gaussian weighted distance between the
data point and the nearest surface point in the MRI
model. These Gaussian weighted distances are summed
for all data points, which defines a measure of the
goodness of fit of the current estimated transformation.
This objective function is then optimized using a gradient
descent algorithm. The role of the Gaussian weighting is
to facilitate ''pulling in'' of one data set to the other,
without needing to know the exact correspondence be-
tween data points. The process can be executed in
a multiresolution manner, by first using Gaussian distri-
butions with large spreads (to get the registration close),
then reducing the spread of the distribution, and re-
solving in a sequence of steps.
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.
Initial alignment
A manual initial alignment can be used to roughly align
the two surfaces. Accurate manual alignment can be very
difficult, but we aim only to be within 20
of the correct
transformation, for which subsequent steps will solve.
One method for achieving this uses a pair of 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
three dimensions to achieve a qualitatively close align-
ment. 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 in-
versely proportional to distance from the viewer. In each
overlay view, the laser data may be rotated and translated
in two dimensions to align the projections. An alternative
