The high definition impedance imaging (HDII) Electroscan algorithm casts the error norm problem into the interior of
the region and iteratively minimizes the difference norm calculated between solutions achieved for applied currents (i.e.
the Neumann problem) and the solution achieved for the measured voltages (i.e. the Dirichlet problem) - in the
electrical-excitation case. This results in very sparse matrices instead of densely-packed Jacobian matrices.
Minimization of the error yields a three-dimensional image of the conductivity distribution. The paper presents a rapid,
sparse-matrix methodology for high definition admittivity imaging involving a very large number of voxels. It is a least-square
algorithm, simultaneously involving all excitations, and it is error resilient and well-conditioned. The solution
iterative procedure is accelerated by a variety of means such as: solution of mutually-constrained, three-dimensional
field equations; successive point-iterative overrelaxation; multi-acceleration factors; measurements at a multiplicity of
electrodes; and excitation modification for image enhancement. Laboratory, field, and simulation case studies are
presented. Spatially restricted-region and open-region solutions are compared. Signal-source modeling is not required.
Conductivity and, generally, admittivity values are able to be determined. And so, the imaging process has diagnostic
capability. It is applicable to non-contact standoff excitations, e.g. magnetic fields, microwave/radar, sonic and elasticity