A new image-registration technique that matches multiple structures on
complementary imaging data sets (e.g., CT and MRI) has been developed
and tested with both phantom and patient data. The algorithm assumes
a rigid-body transformation and is suitable for correlating structures
within the cranium or at the skull base. The basic premise of the new
technique is that an optimum transformation is achieved when the rela
tive volume lying outside of the intersection between a structure and
its transformed counterpart is a minimum. This relative volume is calc
ulated numerically using a random sampling approach, and a binary sear
ching algorithm was used to step through the nine-dimensional paramete
r space consisting of three rotation angles, three scaling factors and
three components of a translation vector. For the nine tests using ph
antom data, the automated structure-matching technique was able to pre
dict the correct rotation angles to within +/-1 degrees. The expected
clinical performance of the new technique was assessed by comparing re
sults obtained with the new method to those obtained using other techn
iques for 12 patients who were treated with charged particles at Lawre
nce Berkeley Laboratory (LBL) and who had image-registration studies p
erformed as part of their treatment plan. For 9 of the 12 patients con
sidered, the new structure-matching technique produced a significantly
better registration than the older methods, as measured by the result
ant average relative volume lying outside of the intersection between
any structure and its transformed counterpart. For the other three pat
ients, results were not significantly different for the new structure-
matching method and the older techniques.