Conversion of single-energy computed tomography to parametric maps of dual-energy computed tomography using convolutional neural network

TL;DR

This paper introduces a deep learning framework that directly converts single-energy CT images into dual-energy CT parametric maps, enabling retrospective analysis without dual-energy CT devices.

Contribution

The study presents a novel multi-task CNN model that accurately transforms SECT into VMIs, EAN, and RED maps, facilitating parametric imaging from standard CT scans.

Findings

High accuracy in converting SECT to VMIs with low absolute and relative differences.

Effective conversion of SECT to EAN and RED with minimal error.

Potential for retrospective parametric analysis using standard CT images.

Abstract

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.