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The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B5. Beijing 2008 
CT series 
The first step of finding the 6 DOF patient alignment is to 
perform an initial alignment (pre-alignment in figure 1). In this 
pre-alignment it is not possible to find the alignment in full 6 
degrees of freedom, but the CT series has to be projected into 
the digital flat-panel (DFP) plane only a few times. 
However, through the pre-alignment we gain a good approach 
to the real patient pose. Next, we identify portions of the DR 
images that can be excluded from further image comparison. 
We can identify these regions in the planes of the DRR images 
as well. They are then excluded from the rendering process, to 
speed-up the computation. 
In the last step, the full 6 DOF alignment, we fine-tune the 
initially detected pose to achieve the desired accuracy (here 0.5 
mm). Therefore, image comparisons between DRRs for 
different alignments are performed and the similarity is 
maximized. As this procedure requires a large number of 
consecutive CT projections, it benefits from the fact, that areas 
could be excluded from the rendering process. 
3.1 Pre-alignment 
To find the patient alignment we first perform a step that we call 
pre-alignment. This is done by 2D registrations of two X-ray 
images to the respective DRRs. The results of the registrations 
are inversely projected into 3D space and used to update the 
DRRs with the new alignment. 
3.1.1 The Registration Process: There exists a wide range 
of gray-value based image comparators in the scope of 
registration. As methods like cross-correlation or usage of 
difference images are not applicable for images that differ in 
much more aspects than contrast and intensity, we decided to 
use Mutual Information (MI) as image similarity measure 
(PLUIM et al., 2003). 
A joint histogram is built-up by reading the gray-values of both 
images at the position of two overlaid pixels. A cell of the two- 
dimensional histogram is then incremented by one at the 
respective coordinates, defined by the two gray-values. The 
Mutual Information value MI is calculated by equation 1: 
MI(R,F) = H( < R) + H( < F)-H{R,F) (1) 
where M1(R,F) = Mutual Information value 
R, F = reference (DR) and floating image (DRR) 
H(R), H(F) = Shannon Entropies of the images 
H(R,F) = Joint Entropy of R and F 
The negative MI value is minimized by a Downhill Simplex 
minimization algorithm as described in (Press et al., 1982). The 
three free transformation parameters are the floating image 
shifts in X- and Y- direction of the image plane and rotation of 
the image plane around its normal vector. 
3.1.2 Inverse Projection: After each registration, the results 
are back-projected into a common coordinate system. The 
underlying geometry is shown in figure 2. 
Figure 2. Geometry of the treatment equipment 
Figure 2 depicts only the relevant parts of the equipment. The 
image detectors and the X-ray tubes determine the geometric 
properties that are of essential importance for the DRR 
rendering and for the inverse projection of the registration 
results. The patient table determines the coordinate system used 
for patient alignment. 
3.1.3 DRR Update and User ROIs: The DRRs are created 
by ray-tracing. When scattering is neglected, the intensity of an 
X-ray passing through the respective object is given by the line 
integral along the virtual X-ray: 
- \f(x,mx+b)ix 
I = I 0 *e ■“ (2) 
where I 0 - intensity of the X-ray at the source 
/ = intensity of the DRR gay-value 
we choose I 0 to normalize the expression in equation 2 to a 
resulting intensity range of