Abstract:
Typically 3-D MR and CT scans have a relatively high resolution in the
scanning X-Y plane, but much lower resolution in the axial Z direction.
This non-uniform sampling of an object can miss small or thin structures.
One way to address this problem is to scan the same object from multiple
directions. In this paper we describe a method for deforming a level set
model using velocity information derived from multiple volume datasets with
non-uniform resolution in order to produce a single high-resolution 3D
model. The method locally approximates the values of the multiple datasets
by fitting a distance-weighted polynomial using moving least-squares. The
proposed method has several advantageous properties: its computational cost
is proportional to the object surface area, it is stable with respect to
noise, imperfect registrations and abrupt changes in the data, it provides
gain-correction, and it employs a distance-based weighting to ensures that
the contributions from each scan are properly merged into the final result.
We have demonstrated the effectiveness of our approach on four multi-scan
datasets, a griffin laser scan reconstruction, a CT scan of a teapot and
MR scans of a mouse embryo and a zucchini.
