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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

This window shows the mucus distribution among generations.The user selects a voxel and the model displays its corresponding parameters along with the micro-level information.
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pone-0111245-g008: This window shows the mucus distribution among generations.The user selects a voxel and the model displays its corresponding parameters along with the micro-level information.

Mentions: This method can even be applied to follow a patient's progress with out fewer imaging tests. Since most CF patients take spirometry test more often than any imaging tests, we can use our model without recourse to imaging data. This time the model takes only the FEV and the FVC values of a patient as input. After applying the Metropolis algorithm (using additional rearrangements of mucus between voxels) the model can still provide an estimate of the distribution of mucus throughout the airway tree. This can be propagated using the constant any hypothetical growth rates to predict the next spirometric test. We have created a Matlab GUI version of the algorithm; its sample output is shown in Figures 7, 8 and 9.


Stochastic tracking of infection in a CF lung.

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

This window shows the mucus distribution among generations.The user selects a voxel and the model displays its corresponding parameters along with the micro-level information.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0111245-g008: This window shows the mucus distribution among generations.The user selects a voxel and the model displays its corresponding parameters along with the micro-level information.
Mentions: This method can even be applied to follow a patient's progress with out fewer imaging tests. Since most CF patients take spirometry test more often than any imaging tests, we can use our model without recourse to imaging data. This time the model takes only the FEV and the FVC values of a patient as input. After applying the Metropolis algorithm (using additional rearrangements of mucus between voxels) the model can still provide an estimate of the distribution of mucus throughout the airway tree. This can be propagated using the constant any hypothetical growth rates to predict the next spirometric test. We have created a Matlab GUI version of the algorithm; its sample output is shown in Figures 7, 8 and 9.

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