Uconnect: Synergistic Spectral CT Reconstruction With U-Nets Connecting the Energy Bins

IF 4.6 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING IEEE Transactions on Radiation and Plasma Medical Sciences Pub Date : 2023-11-03 DOI:10.1109/TRPMS.2023.3330045
Zhihan Wang;Alexandre Bousse;Franck Vermet;Jacques Froment;Béatrice Vedel;Alessandro Perelli;Jean-Pierre Tasu;Dimitris Visvikis
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Abstract

Spectral computed tomography (CT) offers the possibility to reconstruct attenuation images at different energy levels, which can be then used for material decomposition. However, traditional methods reconstruct each energy bin individually and are vulnerable to noise. In this article, we propose a novel synergistic method for spectral CT reconstruction, namely, Uconnect. It utilizes trained convolutional neural networks (CNNs) to connect the energy bins to a latent image so that the full binned data is used synergistically. We experiment on two types of low-dose data: 1) simulated and 2) real patient data. Qualitative and quantitative analysis show that our proposed Uconnect outperforms state-of-the-art model-based iterative reconstruction (MBIR) techniques as well as CNN-based denoising.
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Uconnect:利用 U 型网络连接能量盒进行协同频谱 CT 重构
光谱计算机断层扫描(CT)可以重建不同能量级别的衰减图像,然后用于材料分解。然而,传统的方法是单独重建每个能级,容易受到噪声的影响。在本文中,我们提出了一种用于光谱 CT 重建的新型协同方法,即 Uconnect。它利用训练有素的卷积神经网络(CNN)将能量分区与潜在图像连接起来,从而协同使用完整的分区数据。我们对两种低剂量数据进行了实验:1)模拟数据;2)真实患者数据。定性和定量分析表明,我们提出的 Uconnect 优于最先进的基于模型的迭代重建(MBIR)技术和基于 CNN 的去噪技术。
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来源期刊
IEEE Transactions on Radiation and Plasma Medical Sciences
IEEE Transactions on Radiation and Plasma Medical Sciences RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-
CiteScore
8.00
自引率
18.20%
发文量
109
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