Conversion of single-energy computed tomography to parametric maps of
  dual-energy computed tomography using convolutional neural network
Sangwook Kim Jimin Lee Jungye Kim Bitbyeol Kim Chang Heon Choi Seongmoon Jung
Objectives: We propose a deep learning (DL) multi-task learning framework
using convolutional neural network (CNN) for a direct conversion of
single-energy CT (SECT) to three different parametric maps of dual-energy CT
(DECT): Virtual-monochromatic image (VMI), effective atomic number (EAN), and
relative electron density (RED).
  Methods: We propose VMI-Net for conversion of SECT to 70, 120, and 200 keV
VMIs. In addition, EAN-Net and RED-Net were also developed to convert SECT to
EAN and RED. We trained and validated our model using 67 patients collected
between 2019 and 2020. SECT images with 120 kVp acquired by the DECT (IQon
spectral CT, Philips) were used as input, while the VMIs, EAN, and RED acquired
by the same device were used as target. The performance of the DL framework was
evaluated by absolute difference (AD) and relative difference (RD).
  Results: The VMI-Net converted 120 kVp SECT to the VMIs with AD of 9.02
Hounsfield Unit, and RD of 0.41% compared to the ground truth VMIs. The ADs of
the converted EAN and RED were 0.29 and 0.96, respectively, while the RDs were
1.99% and 0.50% for the converted EAN and RED, respectively.
  Conclusions: SECT images were directly converted to the three parametric maps
of DECT (i.e., VMIs, EAN, and RED). By using this model, one can generate the
parametric information from SECT images without DECT device. Our model can help
investigate the parametric information from SECT retrospectively.
  Advances in knowledge: Deep learning framework enables converting SECT to
various high-quality parametric maps of DECT.