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Related Experiment Video

Updated: Nov 21, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Published on: November 30, 2022

Generation of Brain Dual-Energy CT from Single-Energy CT Using Deep Learning.

Chi-Kuang Liu¹, Chih-Chieh Liu², Cheng-Hsun Yang³

¹Department of Medical Imaging, Changhua Christian Hospital, 135 Nanxiao St, 500, Changhua County, Taiwan.

Journal of Digital Imaging

|January 12, 2021

Summary

This summary is machine-generated.

Deep learning models can create high-quality dual-energy CT images from low-kV scans, offering a cost-effective alternative to specialized scanners. The low-to-high kV mapping proved superior, enhancing signal-to-noise ratios in brain imaging.

Keywords:

Deep learning Dual-energy computed tomography U-net

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Area of Science:

Medical Imaging
Artificial Intelligence
Radiology

Background:

Deep learning (DL) shows promise in converting between imaging modalities.
DL can synthesize high-kilovoltage (kV) computed tomography (CT) images from low-kV CT images, potentially enabling dual-energy CT (DECT) acquisition without new hardware.

Purpose of the Study:

To investigate the efficacy of low-to-high kV versus high-to-low kV mapping for synthesizing DECT images.
To assess the performance of a U-Net and a double U-Net model in generating DECT images from single-energy CT scans.
To evaluate the image quality, signal-to-noise ratio (SNR), and CT number accuracy of DL-generated DECT images compared to true DECT images.

Main Methods:

A U-Net model was employed for kV CT image conversions.
A double U-Net model was proposed to enhance original single-energy CT image quality.
Ninety-eight patient brain DECT scans were used for training, validation, and testing the DL models.

Main Results:

Low-to-high kV conversion demonstrated superior performance compared to high-to-low kV conversion.
DL-based DECT images exhibited improved SNRs over true DECT images, with a minor reduction in spatial resolution.
Mean CT number differences between true and DL-based DECT images were within ±1 HU, with no statistically significant difference (p > 0.05).

Conclusions:

DL holds potential for generating brain DECT images from single-energy CT scans.
The DL-based approach can produce low-noise virtual monoenergetic images due to improved SNR.
This method offers a feasible alternative for obtaining DECT data without specialized scanners.