Reconstruction Algorithms to Monitor Neonate Lung Function

Abstract

Disorders of lung growth, maturation and control of breathing are among the most important problems faced by the neonatologist. 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. At present, no system for continuous monitoring of neonate lung function to reduce the risk of CLDI in intensive care units (ITUs) exists.We present the development of image reconstruction algorithms to monitor neonate lung function in ITU’s, and a method base on wearable technology to integrate measures of the boundary diameter from the boundary form. This approach provides a reduction of image artefacts in the reconstructed image associated with incorrect boundary form assumptions. In terms of image reconstruction, we utilise the concept of subspace invariance to design a block adaptive preconditioning scheme, which yields a smaller error norm and can provide improvements in the condition number of the coefficients matrix, as compared to incomplete Cholesky factorization, followed by the application of conjugate gradient.