论文标题
评论“直接网络：用于定量双能量CT成像的统一相互域材料分解网络”，Med。Phys。2022，第49卷，第49卷，第917-934页
Comments on "DIRECT-Net: A unified mutual-domain material decomposition network for quantitative dual-energy CT Imaging'', Med. Phys. 2022, Vol. 49, pgs. 917-934
论文作者
论文摘要
双能计算机断层扫描（CT）中的定量图像重建仍然是一个积极研究的话题。我们引起了``直接网络：统一的互域材料分解网络''的阅读，该网络用于定量双能CT成像'，它出现在2月2022年的Med Phys上。在本文中，作者提出了一种深入学习（DL）方法，称为直接网络方法，以直接从全扫描双能量CT（DECT）中的数据直接从数据中解决定量图像重建问题。我们在本文中对研究和结论发表评论。对此评论的答复在medphys.org上的通信下显示：https：//www.medphys.org/communications/reply-pan_response-su.pdf，以便为回复提供上下文，我们提供了我们的评论的全文。
Quantitative image reconstruction in dual-energy computed tomography (CT) remains a topic of active research. We read with interest ``DIRECT-Net: A unified mutual-domain material decomposition network for quantitative dual-energy CT imaging,'' which appears in the 2022 February Issue of Med Phys. In the paper the authors propose a deep-learning (DL) method, referred to as the Direct-Net method, to address the problem of quantitative image reconstruction directly from data in full-scan dual-energy CT (DECT). We comment on the study and conclusion in the paper. The Reply to this comment appears under Communications on medphys.org: https://www.medphys.org/Communications/Reply-Pan_Response-Su.pdf In order to have context for the Reply, we provide the full text of our Comments.
