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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

Generating the Micro-level information on obstructed bronchioles.This flowchart shows how the algorithm produces the mucus distribution of a selected voxel. Using the lung functionality test values: FEV1 and FVC and the data obtained from MRI lung imaging we can obtain the corresponding mucus distributions among the different generations of lung.
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pone-0111245-g003: Generating the Micro-level information on obstructed bronchioles.This flowchart shows how the algorithm produces the mucus distribution of a selected voxel. Using the lung functionality test values: FEV1 and FVC and the data obtained from MRI lung imaging we can obtain the corresponding mucus distributions among the different generations of lung.

Mentions: Figure 3 displays the flowchart of this process. As shown in this flowchart, once we have all the voxels' parameters, we can select certain voxels for further analysis. We again apply the Metropolis algorithm on the selected voxel in order to visualize the distribution of mucus in its airway tree structure. For the chosen voxel, we have its mucus volume, its resistance and its AA%. We randomly fill out the voxels' airway tree to reach their corresponding mucus volume. At each iteration the new resistance and AA% are collected. To move to the neighbor configuration we move a unit volume of mucus in a bronchiole to keep the total mucus volume of the voxel fixed. The state energy we use for this step is as follows: (3)where . After we obtained enough samples we constructed the corresponding mucus distribution for the selected voxels. Figures 4 displays two examples of the mucus distribution for voxels that contained () and () mucus. As shown in these figures, as the percent mucus increases, the dominantly filled generation moves to bigger bronchioles. The y axis repressnts represents % normalized mucus where: (4)


Stochastic tracking of infection in a CF lung.

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

Generating the Micro-level information on obstructed bronchioles.This flowchart shows how the algorithm produces the mucus distribution of a selected voxel. Using the lung functionality test values: FEV1 and FVC and the data obtained from MRI lung imaging we can obtain the corresponding mucus distributions among the different generations of lung.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0111245-g003: Generating the Micro-level information on obstructed bronchioles.This flowchart shows how the algorithm produces the mucus distribution of a selected voxel. Using the lung functionality test values: FEV1 and FVC and the data obtained from MRI lung imaging we can obtain the corresponding mucus distributions among the different generations of lung.
Mentions: Figure 3 displays the flowchart of this process. As shown in this flowchart, once we have all the voxels' parameters, we can select certain voxels for further analysis. We again apply the Metropolis algorithm on the selected voxel in order to visualize the distribution of mucus in its airway tree structure. For the chosen voxel, we have its mucus volume, its resistance and its AA%. We randomly fill out the voxels' airway tree to reach their corresponding mucus volume. At each iteration the new resistance and AA% are collected. To move to the neighbor configuration we move a unit volume of mucus in a bronchiole to keep the total mucus volume of the voxel fixed. The state energy we use for this step is as follows: (3)where . After we obtained enough samples we constructed the corresponding mucus distribution for the selected voxels. Figures 4 displays two examples of the mucus distribution for voxels that contained () and () mucus. As shown in these figures, as the percent mucus increases, the dominantly filled generation moves to bigger bronchioles. The y axis repressnts represents % normalized mucus where: (4)

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