  Background: Dual-energy CT (DECT) and material decomposition play vital roles
in quantitative medical imaging. However, the decomposition process may suffer
from significant noise amplification, leading to severely degraded image
signal-to-noise ratios (SNRs). While existing iterative algorithms perform
noise suppression using different image priors, these heuristic image priors
cannot accurately represent the features of the target image manifold. Although
deep learning-based decomposition methods have been reported, these methods are
in the supervised-learning framework requiring paired data for training, which
is not readily available in clinical settings.
  Purpose: This work aims to develop an unsupervised-learning framework with
data-measurement consistency for image-domain material decomposition in DECT.
