Estimating dual-energy CT imaging from single-energy CT data
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This study proposed a method to estimate dual-energy CT images from traditional single energy CT images together with a single view projection at a different energy level. The deep-learning algorithm is used to perform material-decomposition-like operations. The approach can provide superior material-specific images with significantly reduced noise compared with standard DECT imaging.

Dual-energy computed tomography (DECT) is of great significance for clinical practice due to its huge potential to provide material-specific information. However, DECT scanners are usually more expensive than standard single-energy CT (SECT) scanners and thus are less accessible to undeveloped regions.

In this paper, investigators show that the energy-domain correlation and anatomical consistency between standard DECT images can be harnessed by a deep learning model to provide high-performance DECT imaging from fully-sampled low-energy data together with single-view high-energy data.

They demonstrate the feasibility of the approach with two independent cohorts - the first cohort including contrast-enhanced DECT scans of 5,753 image slices from 22 patients and the second cohort including spectral CT scans without contrast injection of 2463 image slices from other 22 patients, and show its superior performance on DECT applications.

In particular, the deep-learning-based approach could be useful to further significantly reduce the radiation dose of current premium DECT scanners and has the potential to simplify the hardware of DECT imaging systems and to enable DECT imaging using standard SECT scanners.

Medical Image Analysis