  Cone-beam CT (CBCT) is installed in the treatment room to facilitate online
clinical applications, including image guidance in radiation and surgery.
Half-fan and short-can are the commonly used modes in clinical applications to
expand the imaging volume and reduce radiation exposure. Anatomical structures
are faithfully reconstructed using CBCT while deep information including
material composition is not feasible in its current form. Dual-energy computed
tomography (DECT) is an advanced medical imaging technology with superior
capability of material differentiation by scanning the patient using two
different energy spectra. Conventional DECT systems require expensive and
sophisticated hardware components, significantly limiting DECT applications to
cone-beam CT (CBCT) machines. In this work, we propose a dual-energy cone-beam
CT (DECBCT) system by placing a dedicatedly designed beam modulator in front of
the detector surface to acquire dual-energy projection data in a single scan. A
deep learning-based data restoration model is introduced to generate complete
dual-energy data from the detector-modulated data for DECT image reconstruction
and material decomposition. The performance of the proposed method is evaluated
using an electron density phantom and clinical CT. The mean error for electron
density calculation is lower than 1.16% in the electron density phantom study.
The mean errors for reconstructed linear attenuation coefficient (LAC) are
lower than 1.41% and 0.89% in the head and pelvis studies, respectively. The
results indicate that the proposed method is a promising solution for
single-scan DECBCT in clinical practice.
