  Due to the rapid growth of Electrical Capacitance Tomography (ECT)
applications in several industrial fields, there is a crucial need for
developing high quality, yet fast, methodologies of image reconstruction from
raw capacitance measurements. Deep learning, as an effective non-linear mapping
tool for complicated functions, has been going viral in many fields including
electrical tomography. In this paper, we propose a Conditional Generative
Adversarial Network (CGAN) model for reconstructing ECT images from capacitance
measurements. The initial image of the CGAN model is constructed from the
capacitance measurement. To our knowledge, this is the first time to represent
the capacitance measurements in an image form. We have created a new massive
ECT dataset of 320K synthetic image measurements pairs for training, and
testing the proposed model. The feasibility and generalization ability of the
proposed CGAN-ECT model are evaluated using testing dataset, contaminated data
and flow patterns that are not exposed to the model during the training phase.
The evaluation results prove that the proposed CGAN-ECT model can efficiently
create more accurate ECT images than traditional and other deep learning-based
image reconstruction algorithms. CGAN-ECT achieved an average image correlation
coefficient of more than 99.3% and an average relative image error about 0.07.
