1 research outputs found
Interpretable Modeling and Reduction of Unknown Errors in Mechanistic Operators
Prior knowledge about the imaging physics provides a mechanistic forward
operator that plays an important role in image reconstruction, although myriad
sources of possible errors in the operator could negatively impact the
reconstruction solutions. In this work, we propose to embed the traditional
mechanistic forward operator inside a neural function, and focus on modeling
and correcting its unknown errors in an interpretable manner. This is achieved
by a conditional generative model that transforms a given mechanistic operator
with unknown errors, arising from a latent space of self-organizing clusters of
potential sources of error generation. Once learned, the generative model can
be used in place of a fixed forward operator in any traditional
optimization-based reconstruction process where, together with the inverse
solution, the error in prior mechanistic forward operator can be minimized and
the potential source of error uncovered. We apply the presented method to the
reconstruction of heart electrical potential from body surface potential. In
controlled simulation experiments and in-vivo real data experiments, we
demonstrate that the presented method allowed reduction of errors in the
physics-based forward operator and thereby delivered inverse reconstruction of
heart-surface potential with increased accuracy.Comment: 11 pages, Conference: Medical Image Computing and Computer Assisted
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