  Several methods to perform extrapolation of unknown anatomy were evaluated.
The primary application is to enhance surgical procedures that may use partial
medical images or medical images of incomplete anatomy. Le Fort-based,
face-jaw-teeth transplant is one such procedure. From CT data of 36 skulls and
21 mandibles separate Statistical Shape Models of the anatomical surfaces were
created. Using the Statistical Shape Models, incomplete surfaces were projected
to obtain complete surface estimates. The surface estimates exhibit non-zero
error in regions where the true surface is known; it is desirable to keep the
true surface and seamlessly merge the estimated unknown surface. Existing
extrapolation techniques produce non-smooth transitions from the true surface
to the estimated surface, resulting in additional error and a less
aesthetically pleasing result. The three extrapolation techniques evaluated
were: copying and pasting of the surface estimate (non-smooth baseline), a
feathering between the patient surface and surface estimate, and an estimate
generated via a Thin Plate Spline trained from displacements between the
surface estimate and corresponding vertices of the known patient surface.
Feathering and Thin Plate Spline approaches both yielded smooth transitions.
However, feathering corrupted known vertex values. Leave-one-out analyses were
conducted, with 5% to 50% of known anatomy removed from the left-out patient
and estimated via the proposed approaches. The Thin Plate Spline approach
yielded smaller errors than the other two approaches, with an average vertex
error improvement of 1.46 mm and 1.38 mm for the skull and mandible
respectively, over the baseline approach.
