  The detailed reconstruction of neural anatomy for connectomics studies
requires a combination of resolution and large three-dimensional data capture
provided by serial section electron microscopy (ssEM). The convergence of high
throughput ssEM imaging and improved tissue preparation methods now allows ssEM
capture of complete specimen volumes up to cubic millimeter scale. The
resulting multi-terabyte image sets span thousands of serial sections and must
be precisely registered into coherent volumetric forms in which neural circuits
can be traced and segmented. This paper introduces a Signal Whitening Fourier
Transform Image Registration approach (SWiFT-IR) under development at the
Pittsburgh Supercomputing Center and its use to align mouse and zebrafish brain
datasets acquired using the wafer mapper ssEM imaging technology recently
developed at Harvard University. Unlike other methods now used for ssEM
registration, SWiFT-IR modifies its spatial frequency response during image
matching to maximize a signal-to-noise measure used as its primary indicator of
alignment quality. This alignment signal is more robust to rapid variations in
biological content and unavoidable data distortions than either phase-only or
standard Pearson correlation, thus allowing more precise alignment and
statistical confidence. These improvements in turn enable an iterative
registration procedure based on projections through multiple sections rather
than more typical adjacent-pair matching methods. This projection approach,
when coupled with known anatomical constraints and iteratively applied in a
multi-resolution pyramid fashion, drives the alignment into a smooth form that
properly represents complex and widely varying anatomical content such as the
full cross-section zebrafish data.
