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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

FEV  and FVC probability density function estimation.The x-axis shows the predicted FEV values and the y-axis is the predicted FVC values for a hypothetical the synthetic lung example described in the text.
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pone-0111245-g006: FEV and FVC probability density function estimation.The x-axis shows the predicted FEV values and the y-axis is the predicted FVC values for a hypothetical the synthetic lung example described in the text.

Mentions: In this section we use the mucus distribution and growth model presented in [9] to make predictions about the lung functionality of a CF patient. As can be seen in Figure 5 we use the imaging voxels' data and the constant mucus growth rate from [9], or, if available, infection and treatment specific growth rates specific to each voxel to predict the mucus growth in each pocket of infection. The model again resorts to the Monte Carlo method to randomly select the AA% and resistance from our model described in the PDFE-2D section and calculates the new FEV and FVC at each iteration and stores their values. After collecting enough samples we can predict the FEV and FVC distributions for a patient after the indicated time period. For example, Figure 6 displays the probability density function of the predicted FEV and FVC. As shown in the figure it is predicted that FEV and FVC of our CF patient are approximately and respectively.


Stochastic tracking of infection in a CF lung.

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

FEV  and FVC probability density function estimation.The x-axis shows the predicted FEV values and the y-axis is the predicted FVC values for a hypothetical the synthetic lung example described in the text.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0111245-g006: FEV and FVC probability density function estimation.The x-axis shows the predicted FEV values and the y-axis is the predicted FVC values for a hypothetical the synthetic lung example described in the text.
Mentions: In this section we use the mucus distribution and growth model presented in [9] to make predictions about the lung functionality of a CF patient. As can be seen in Figure 5 we use the imaging voxels' data and the constant mucus growth rate from [9], or, if available, infection and treatment specific growth rates specific to each voxel to predict the mucus growth in each pocket of infection. The model again resorts to the Monte Carlo method to randomly select the AA% and resistance from our model described in the PDFE-2D section and calculates the new FEV and FVC at each iteration and stores their values. After collecting enough samples we can predict the FEV and FVC distributions for a patient after the indicated time period. For example, Figure 6 displays the probability density function of the predicted FEV and FVC. As shown in the figure it is predicted that FEV and FVC of our CF patient are approximately and respectively.

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