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Stochastic tracking of infection in a CF lung.

ABSTRACT: Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) scan are the two ubiquitous imaging sources that physicians use to diagnose patients with Cystic Fibrosis (CF) or any other Chronic Obstructive Pulmonary Disease (COPD). Unfortunately the cost constraints limit the frequent usage of these medical imaging procedures. In addition, even though both CT scan and MRI provide mesoscopic details of a lung, in order to obtain microscopic information a very high resolution is required. Neither MRI nor CT scans provide micro level information about the location of infection in a binary tree structure the binary tree structure of the human lung. In this paper we present an algorithm that enhances the current imaging results by providing estimated micro level information concerning the location of the infection. The estimate is based on a calculation of the distribution of possible mucus blockages consistent with available information using an offline Metropolis-Hastings algorithm in combination with a real-time interpolation scheme. When supplemented with growth rates for the pockets of mucus, the algorithm can also be used to estimate how lung functionality as manifested in spirometric tests will change in patients with CF or COPD.

SUBMITTER: Zarei S

PROVIDER: S-EPMC4216002 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Stochastic tracking of infection in a CF lung.

Zarei Sara S Mirtar Ali A Rohwer Forest F Salamon Peter P

PloS one 20141031 10

Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) scan are the two ubiquitous imaging sources that physicians use to diagnose patients with Cystic Fibrosis (CF) or any other Chronic Obstructive Pulmonary Disease (COPD). Unfortunately the cost constraints limit the frequent usage of these medical imaging procedures. In addition, even though both CT scan and MRI provide mesoscopic details of a lung, in order to obtain microscopic information a very high resolution is required. Neither ...[more]

PMID: 25360611

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Project description:BackgroundLung disease as major cause for morbidity in patients with cystic fibrosis (CF) starts early in life. Its large phenotypic heterogeneity is partially explained by the genotype but other contributing factors are not well delineated. The close relationship between mucus, inflammation and infection, drives morpho-functional alterations already early in pediatric CF disease, The TRACK-CF cohort has been established to gain insight to disease onset and progression, assessed by lung function testing and imaging to capture morpho-functional changes and to associate these with risk and protective factors, which contribute to the variation of the CF lung disease progression.Methods and designTRACK-CF is a prospective, longitudinal, observational cohort study following patients with CF from newborn screening or clinical diagnosis throughout childhood. The study protocol includes monthly telephone interviews, quarterly visits with microbiological sampling and multiple-breath washout and as well as a yearly chest magnetic resonance imaging. A parallel biobank has been set up to enable the translation from the deeply phenotyped cohort to the validation of relevant biomarkers. The main goal is to determine influencing factors by the combined analysis of clinical information and biomaterials. Primary endpoints are the lung clearance index by multiple breath washout and semi-quantitative magnetic resonance imaging scores. The frequency of pulmonary exacerbations, infection with pro-inflammatory pathogens and anthropometric data are defined as secondary endpoints.DiscussionThis extensive cohort includes children after diagnosis with comprehensive monitoring throughout childhood. The unique composition and the use of validated, sensitive methods with the attached biobank bears the potential to decisively advance the understanding of early CF lung disease.Ethics and trial registrationThe study protocol was approved by the Ethics Committees of the University of Heidelberg (approval S-211/2011) and each participating site and is registered at clinicaltrials.gov (NCT02270476).

Dataset Information

Stochastic tracking of infection in a CF lung.

Publications

Stochastic tracking of infection in a CF lung.

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