Bayford R, Kantartzis P, Tizzard A, Yerworth R, Liatsis P, Demosthenous A
Department of Natural Sciences, Middlesex University, London, UK.
Physiol Meas. 2008 Jun;29(6):S125-38. doi: 10.1088/0967-3334/29/6/S11. Epub 2008 Jun 10.
Objective, non-invasive measures of lung maturity and development, oxygen requirements and lung function, suitable for use in small, unsedated infants, are urgently required to define the nature and severity of persisting lung disease, and to identify risk factors for developing chronic lung problems. Disorders of lung growth, maturation and control of breathing are among the most important problems faced by the neonatologists. At present, no system for continuous monitoring of neonate lung function to reduce the risk of chronic lung disease in infancy in intensive care units exists. We are in the process of developing a new integrated electrical impedance tomography (EIT) system based on wearable technology to integrate measures of the boundary diameter from the boundary form for neonates into the reconstruction algorithm. In principle, this approach could provide a reduction of image artefacts in the reconstructed image associated with incorrect boundary form assumptions. In this paper, we investigate the required accuracy of the boundary form that would be suitable to minimize artefacts in the reconstruction for neonate lung function. The number of data points needed to create the required boundary form is automatically determined using genetic algorithms. The approach presented in this paper is to assist quality of the reconstruction using different approximations to the ideal boundary form. We also investigate the use of a wavelet algebraic multi-grid (WAMG) preconditioner to reduce the reconstruction computation requirements. Results are presented that demonstrate a full 3D model is required to minimize artefact in the reconstructed image and the implementation of a WAMG for EIT.
迫切需要客观、非侵入性的方法来测量肺成熟度和发育情况、氧气需求以及肺功能，这些方法适用于未使用镇静剂的小婴儿，以便确定持续性肺病的性质和严重程度，并识别慢性肺部问题的风险因素。肺生长、成熟和呼吸控制障碍是新生儿科医生面临的最重要问题之一。目前，重症监护病房中不存在用于持续监测新生儿肺功能以降低婴儿期慢性肺病风险的系统。我们正在基于可穿戴技术开发一种新的集成电阻抗断层成像（EIT）系统，将新生儿边界形态的边界直径测量值整合到重建算法中。原则上，这种方法可以减少与不正确的边界形态假设相关的重建图像中的图像伪影。在本文中，我们研究了适合最小化新生儿肺功能重建中伪影的边界形态所需的精度。使用遗传算法自动确定创建所需边界形态所需的数据点数。本文提出的方法是使用对理想边界形态的不同近似来辅助重建质量。我们还研究了使用小波代数多重网格（WAMG）预处理器来降低重建计算要求。结果表明，需要一个完整的三维模型来最小化重建图像中的伪影以及为EIT实现WAMG。
