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Stochastic tracking of infection in a CF lung.

Zarei S, Mirtar A, Rohwer F, Salamon P - PLoS ONE (2014)

Bottom Line: Unfortunately the cost constraints limit the frequent usage of these medical imaging procedures.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.

View Article: PubMed Central - PubMed

Affiliation: Computational Science Research Center, San Diego State University, San Diego, California, United States of America.

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.

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Related in: MedlinePlus

Maximum likelihood of percent of accessible alveoli AA% and airflow resistance for a given mucus volume fraction inside a voxel.The x-axis is mucus volume% and the two y-axes show the most probable combination of the airflow resistance ratio and percent of accessible alveoli. The resistance value increases as the mucus amount rises. Number of accessible alveoli decreases as the mucus volume grows, which leads to a lower lung functionality test value or FVC value.
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pone-0111245-g002: Maximum likelihood of percent of accessible alveoli AA% and airflow resistance for a given mucus volume fraction inside a voxel.The x-axis is mucus volume% and the two y-axes show the most probable combination of the airflow resistance ratio and percent of accessible alveoli. The resistance value increases as the mucus amount rises. Number of accessible alveoli decreases as the mucus volume grows, which leads to a lower lung functionality test value or FVC value.

Mentions: Figure 2 displays the maximum likelihood combinations of AA% and resistance ratio for different amount of mucus in a voxel. As the mucus reaches almost of the available airway volume in the voxel, there is no remaining access to the alveoli and as a result there is no gas exchange taking place in that particular part of the airway tree. After collecting these distributions, the model can initiate the prediction steps as well as providing the microlevel information about the location of obstructed bronchioles. We will discuss each outcome in the next two sections. Please note that all data underlying the findings of this section have been discussed in the manuscript. Other than the massive computing power needed to produce the findings in Figure 1 and Figure 2, all the relevant data have been shared.


Stochastic tracking of infection in a CF lung.

Zarei S, Mirtar A, Rohwer F, Salamon P - PLoS ONE (2014)

Maximum likelihood of percent of accessible alveoli AA% and airflow resistance for a given mucus volume fraction inside a voxel.The x-axis is mucus volume% and the two y-axes show the most probable combination of the airflow resistance ratio and percent of accessible alveoli. The resistance value increases as the mucus amount rises. Number of accessible alveoli decreases as the mucus volume grows, which leads to a lower lung functionality test value or FVC value.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4216002&req=5

pone-0111245-g002: Maximum likelihood of percent of accessible alveoli AA% and airflow resistance for a given mucus volume fraction inside a voxel.The x-axis is mucus volume% and the two y-axes show the most probable combination of the airflow resistance ratio and percent of accessible alveoli. The resistance value increases as the mucus amount rises. Number of accessible alveoli decreases as the mucus volume grows, which leads to a lower lung functionality test value or FVC value.
Mentions: Figure 2 displays the maximum likelihood combinations of AA% and resistance ratio for different amount of mucus in a voxel. As the mucus reaches almost of the available airway volume in the voxel, there is no remaining access to the alveoli and as a result there is no gas exchange taking place in that particular part of the airway tree. After collecting these distributions, the model can initiate the prediction steps as well as providing the microlevel information about the location of obstructed bronchioles. We will discuss each outcome in the next two sections. Please note that all data underlying the findings of this section have been discussed in the manuscript. Other than the massive computing power needed to produce the findings in Figure 1 and Figure 2, all the relevant data have been shared.

Bottom Line: Unfortunately the cost constraints limit the frequent usage of these medical imaging procedures.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.

View Article: PubMed Central - PubMed

Affiliation: Computational Science Research Center, San Diego State University, San Diego, California, United States of America.

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.

Show MeSH
Related in: MedlinePlus