Deep neural network vs. commercial algorithm in low-dose CT image reconstruction

Commercial iterative reconstruction techniques help to reduce the radiation dose of computed tomography (CT), but altered image appearance and artefacts can limit their adoptability and potential use. Here, we design a modularized neural network for LDCT and compare it with commercial iterative reconstruction methods from three leading CT vendors. This study confirms that our deep learning approach performs either favourably or comparably in terms of noise suppression and structural fidelity, and is much faster than commercial iterative reconstruction algorithms.

Source: www.nature.com