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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

Probability density function for different percent volume of mucus in a small voxel of the lung.(A), (B), (C)and (D) Each voxel represents a subtree from generation 13 to 23 of the binary tree structure of lung. The x-axis is the corresponding airflow resistance and the y-axis shows the percent accessible alveoli.
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pone-0111245-g001: Probability density function for different percent volume of mucus in a small voxel of the lung.(A), (B), (C)and (D) Each voxel represents a subtree from generation 13 to 23 of the binary tree structure of lung. The x-axis is the corresponding airflow resistance and the y-axis shows the percent accessible alveoli.

Mentions: In order to expedite the computational process; Dulcinea computing clusters from the Computational Science Research Center at San Diego State University were used for collecting almost 54 million samples. The Dulcinea computing clusters contains 12 workstations each with Dual-Quad Xeon central processing unit (CPU) (E5520 2.27GHz) and Dual Tesla graphic processing unit (GPU) (M1060) which provides the total of 96 CPU cores. The cluster system utilizes 3GB of memory per CPU core for nodes 1 to 10 and utilizes 6GB of memory per CPU core for nodes 11 and 12. After obtaining these samples the probability distributions for different amount of mucus are calculated. Figure 1A to Figure 1D illustrate the probability density function for () mucus respectively. As shown in Figure 1A, when there is only mucus in a voxel, the most likely configuration has of its alveoli accessible and the voxel's resistance increases by a factor of almost . When the mucus level reaches almost there are only accessible alveoli and the voxel resistance is almost times a healthy voxel with no mucus. On the other hand in Figure 1C and 1D the number of accessible alveoli value approaches zero while the resistance value reaches infinity. This refers to a case that a voxel is almost completely filled with mucus to an extent that no more air can pass through and therefore blocks all the corresponding alveoli at the end of the branching tree.


Stochastic tracking of infection in a CF lung.

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

Probability density function for different percent volume of mucus in a small voxel of the lung.(A), (B), (C)and (D) Each voxel represents a subtree from generation 13 to 23 of the binary tree structure of lung. The x-axis is the corresponding airflow resistance and the y-axis shows the percent accessible alveoli.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0111245-g001: Probability density function for different percent volume of mucus in a small voxel of the lung.(A), (B), (C)and (D) Each voxel represents a subtree from generation 13 to 23 of the binary tree structure of lung. The x-axis is the corresponding airflow resistance and the y-axis shows the percent accessible alveoli.
Mentions: In order to expedite the computational process; Dulcinea computing clusters from the Computational Science Research Center at San Diego State University were used for collecting almost 54 million samples. The Dulcinea computing clusters contains 12 workstations each with Dual-Quad Xeon central processing unit (CPU) (E5520 2.27GHz) and Dual Tesla graphic processing unit (GPU) (M1060) which provides the total of 96 CPU cores. The cluster system utilizes 3GB of memory per CPU core for nodes 1 to 10 and utilizes 6GB of memory per CPU core for nodes 11 and 12. After obtaining these samples the probability distributions for different amount of mucus are calculated. Figure 1A to Figure 1D illustrate the probability density function for () mucus respectively. As shown in Figure 1A, when there is only mucus in a voxel, the most likely configuration has of its alveoli accessible and the voxel's resistance increases by a factor of almost . When the mucus level reaches almost there are only accessible alveoli and the voxel resistance is almost times a healthy voxel with no mucus. On the other hand in Figure 1C and 1D the number of accessible alveoli value approaches zero while the resistance value reaches infinity. This refers to a case that a voxel is almost completely filled with mucus to an extent that no more air can pass through and therefore blocks all the corresponding alveoli at the end of the branching tree.

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