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本文引用的文献

1
Real-Time Implementation of Calderón's Method on Subject-Specific Domains.基于特定个体域的卡尔德隆方法的实时实现。
IEEE Trans Med Imaging. 2017 Sep;36(9):1868-1875. doi: 10.1109/TMI.2017.2695893. Epub 2017 Apr 19.
2
Incorporating a Spatial Prior into Nonlinear D-Bar EIT Imaging for Complex Admittivities.将空间先验纳入用于复杂电导率的非线性D-bar电阻抗断层成像中。
IEEE Trans Med Imaging. 2017 Feb;36(2):457-466. doi: 10.1109/TMI.2016.2613511. Epub 2016 Sep 26.
3
There is no cephalocaudal gradient of computed tomography densities or lung behavior in supine patients with acute respiratory distress syndrome.急性呼吸窘迫综合征仰卧位患者的计算机断层扫描密度或肺部表现不存在头足梯度。
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A Real-time D-bar Algorithm for 2-D Electrical Impedance Tomography Data.一种用于二维电阻抗断层成像数据的实时D-bar算法。
Inverse Probl Imaging (Springfield). 2014 Nov 1;8(4):1013-1031. doi: 10.3934/ipi.2014.8.1013.
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Real-time ventilation and perfusion distributions by electrical impedance tomography during one-lung ventilation with capnothorax.在伴有二氧化碳胸的单肺通气期间,通过电阻抗断层扫描技术实现的实时通气和灌注分布。
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Regional intratidal gas distribution in acute lung injury and acute respiratory distress syndrome assessed by electric impedance tomography.电阻抗断层成像评估急性肺损伤和急性呼吸窘迫综合征的区域性潮气量分布。
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Electrical impedance tomography reconstruction using a monotonicity approach based on a priori knowledge.基于先验知识采用单调性方法的电阻抗断层成像重建
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Impedance tomography as a new monitoring technique.阻抗断层成像作为一种新的监测技术。
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用于人体通气电阻抗断层成像D-bar重建的动态优化先验

DYNAMIC OPTIMIZED PRIORS FOR D-BAR RECONSTRUCTIONS OF HUMAN VENTILATION USING ELECTRICAL IMPEDANCE TOMOGRAPHY.

作者信息

Alsaker Melody, Mueller Jennifer L, Murthy Rashmi

机构信息

Department of Mathematics; Gonzaga University, Spokane, WA 99258 USA,

Department of Mathematics and School of Biomedical Engineering, Colorado State University, CO 80523 USA,

出版信息

J Comput Appl Math. 2019 Dec 15;362:276-294. doi: 10.1016/j.cam.2018.07.039. Epub 2018 Aug 13.

DOI:10.1016/j.cam.2018.07.039
PMID:31379404
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6677406/
Abstract

A method of including dynamic spatial priors in the 2-D D-bar reconstruction algorithm is presented for use on time-difference reconstructions of human subject thoracic data. The conductivity values for the prior are updated at each frame in the reconstruction using an optimization method applied to the scattering transform. The updates of the dynamic spatial priors are guided by a principle component analysis of the data to determine the timepoint in the ventilatory (or cardiac) cycle. The effectiveness of the method is demonstrated on human subject ventilatory data.

摘要

提出了一种在二维D-bar重建算法中纳入动态空间先验的方法,用于人体胸部数据的时差重建。使用应用于散射变换的优化方法,在重建的每一帧更新先验的电导率值。通过对数据进行主成分分析来确定通气(或心脏)周期中的时间点,从而指导动态空间先验的更新。该方法在人体通气数据上的有效性得到了验证。