Spectral CT requires two or more independent measurements for each ray path in order to extract complete energydependent
information of the object attenuation. The number of required measurements is equivalent to the number of
independent basis functions needed to describe the attenuation of the imaged objects. For example, two independent
measurements are sufficient if only photoelectric absorption and Compton scattering are dominating. If additional Kedge(
s) is present in the energy range of interest, more than two measurements are necessary.
In this study, we present a pre-reconstruction decomposition method that utilizes spectral data redundancy to improve
image quality. We assume projection data are acquired with an M-energy-bin photon counting detector that generates M
independent measurements, and the attenuation of the objects can be described with N (M < M) basis functions. The
method addresses un-balanced noise level of data from different energy bins of the photon counting detector. During a
CT scan, with the non-uniform attenuation of a typical patient, spectral shape and beam intensity can change drastically
from detector to detector, from view to view. As a consequence, a detector unit is subject to significantly varying
incident x-ray spectra. Hardware adjustment approaches are limited by current detector and mechanical technology, and
almost not possible in a typical clinical CT scan with e.g., 1800 views / 0.5 s.
Our method applies adaptive noise balance weighting to data acquired from different energy bins, post data acquisition
and prior data decomposition. The results show substantially improved quality in spectral images reconstructed from
photon counting detector data.