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Computational modeling of the obstructive lung diseases asthma and COPD.

Burrowes KS, Doel T, Brightling C - J Transl Med (2014)

Bottom Line: Computational modeling offers a powerful approach for investigating this relationship between imaging measurements and disease severity, and understanding the effects of different disease subtypes, which is key to developing improved diagnostic methods.Gaining an understanding of a system as complex as the respiratory system is difficult if not impossible via experimental methods alone.We discuss application of modeling techniques to obstructive lung diseases, namely asthma and emphysema and the use of models to predict response to therapy.

View Article: PubMed Central - HTML - PubMed

ABSTRACT
Asthma and chronic obstructive pulmonary disease (COPD) are characterized by airway obstruction and airflow imitation and pose a huge burden to society. These obstructive lung diseases impact the lung physiology across multiple biological scales. Environmental stimuli are introduced via inhalation at the organ scale, and consequently impact upon the tissue, cellular and sub-cellular scale by triggering signaling pathways. These changes are propagated upwards to the organ level again and vice versa. In order to understand the pathophysiology behind these diseases we need to integrate and understand changes occurring across these scales and this is the driving force for multiscale computational modeling. There is an urgent need for improved diagnosis and assessment of obstructive lung diseases. Standard clinical measures are based on global function tests which ignore the highly heterogeneous regional changes that are characteristic of obstructive lung disease pathophysiology. Advances in scanning technology such as hyperpolarized gas MRI has led to new regional measurements of ventilation, perfusion and gas diffusion in the lungs, while new image processing techniques allow these measures to be combined with information from structural imaging such as Computed Tomography (CT). However, it is not yet known how to derive clinical measures for obstructive diseases from this wealth of new data. Computational modeling offers a powerful approach for investigating this relationship between imaging measurements and disease severity, and understanding the effects of different disease subtypes, which is key to developing improved diagnostic methods. Gaining an understanding of a system as complex as the respiratory system is difficult if not impossible via experimental methods alone. Computational models offer a complementary method to unravel the structure-function relationships occurring within a multiscale, multiphysics system such as this. Here we review the currentstate-of-the-art in techniques developed for pulmonary image analysis, development of structural models of therespiratory system and predictions of function within these models. We discuss application of modeling techniques to obstructive lung diseases, namely asthma and emphysema and the use of models to predict response to therapy. Finally we introduce a large European project, AirPROM that is developing multiscale models toinvestigate structure-function relationships in asthma and COPD.

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Combined image-processing and modeling results from a CT scan of a patient with COPD from the Synergy-COPD project. Segmentation of (A) central airways and (B) segments (calculated using both image processing and modeling approaches, color coded by segment). (C) Displays emphysematous regions (emphysema indicated in white and normal tissue in brown) extracted using thresholding of the image intensity values (emphysema <950 HU) [Doel, 2014, unpublished results].
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Figure 1: Combined image-processing and modeling results from a CT scan of a patient with COPD from the Synergy-COPD project. Segmentation of (A) central airways and (B) segments (calculated using both image processing and modeling approaches, color coded by segment). (C) Displays emphysematous regions (emphysema indicated in white and normal tissue in brown) extracted using thresholding of the image intensity values (emphysema <950 HU) [Doel, 2014, unpublished results].

Mentions: Lobe segmentation is typically performed by dividing the segmented lung volume through detection of the pulmonary fissures using various methods (see review by [25]). Some authors extend lobar segmentation ideas to extract the pulmonary segments (see review by [25]). However, validation is more difficult as the segment boundaries are not generally identifiable on CT scans. A novel technique combining imaging and modeling (via the VFB) has been used to extract pulmonary segments within the Synergy-COPD project (Figure 1, Doel, 2014, unpublished information). High resolution CT data used here was provided with permission by Hospital Clinic, IDIBAPS, University of Barcelona using a multislice spiral CT scanner (Somatom Sensation 64) as part of the Synergy-COPD project.


Computational modeling of the obstructive lung diseases asthma and COPD.

Burrowes KS, Doel T, Brightling C - J Transl Med (2014)

Combined image-processing and modeling results from a CT scan of a patient with COPD from the Synergy-COPD project. Segmentation of (A) central airways and (B) segments (calculated using both image processing and modeling approaches, color coded by segment). (C) Displays emphysematous regions (emphysema indicated in white and normal tissue in brown) extracted using thresholding of the image intensity values (emphysema <950 HU) [Doel, 2014, unpublished results].
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4255909&req=5

Figure 1: Combined image-processing and modeling results from a CT scan of a patient with COPD from the Synergy-COPD project. Segmentation of (A) central airways and (B) segments (calculated using both image processing and modeling approaches, color coded by segment). (C) Displays emphysematous regions (emphysema indicated in white and normal tissue in brown) extracted using thresholding of the image intensity values (emphysema <950 HU) [Doel, 2014, unpublished results].
Mentions: Lobe segmentation is typically performed by dividing the segmented lung volume through detection of the pulmonary fissures using various methods (see review by [25]). Some authors extend lobar segmentation ideas to extract the pulmonary segments (see review by [25]). However, validation is more difficult as the segment boundaries are not generally identifiable on CT scans. A novel technique combining imaging and modeling (via the VFB) has been used to extract pulmonary segments within the Synergy-COPD project (Figure 1, Doel, 2014, unpublished information). High resolution CT data used here was provided with permission by Hospital Clinic, IDIBAPS, University of Barcelona using a multislice spiral CT scanner (Somatom Sensation 64) as part of the Synergy-COPD project.

Bottom Line: Computational modeling offers a powerful approach for investigating this relationship between imaging measurements and disease severity, and understanding the effects of different disease subtypes, which is key to developing improved diagnostic methods.Gaining an understanding of a system as complex as the respiratory system is difficult if not impossible via experimental methods alone.We discuss application of modeling techniques to obstructive lung diseases, namely asthma and emphysema and the use of models to predict response to therapy.

View Article: PubMed Central - HTML - PubMed

ABSTRACT
Asthma and chronic obstructive pulmonary disease (COPD) are characterized by airway obstruction and airflow imitation and pose a huge burden to society. These obstructive lung diseases impact the lung physiology across multiple biological scales. Environmental stimuli are introduced via inhalation at the organ scale, and consequently impact upon the tissue, cellular and sub-cellular scale by triggering signaling pathways. These changes are propagated upwards to the organ level again and vice versa. In order to understand the pathophysiology behind these diseases we need to integrate and understand changes occurring across these scales and this is the driving force for multiscale computational modeling. There is an urgent need for improved diagnosis and assessment of obstructive lung diseases. Standard clinical measures are based on global function tests which ignore the highly heterogeneous regional changes that are characteristic of obstructive lung disease pathophysiology. Advances in scanning technology such as hyperpolarized gas MRI has led to new regional measurements of ventilation, perfusion and gas diffusion in the lungs, while new image processing techniques allow these measures to be combined with information from structural imaging such as Computed Tomography (CT). However, it is not yet known how to derive clinical measures for obstructive diseases from this wealth of new data. Computational modeling offers a powerful approach for investigating this relationship between imaging measurements and disease severity, and understanding the effects of different disease subtypes, which is key to developing improved diagnostic methods. Gaining an understanding of a system as complex as the respiratory system is difficult if not impossible via experimental methods alone. Computational models offer a complementary method to unravel the structure-function relationships occurring within a multiscale, multiphysics system such as this. Here we review the currentstate-of-the-art in techniques developed for pulmonary image analysis, development of structural models of therespiratory system and predictions of function within these models. We discuss application of modeling techniques to obstructive lung diseases, namely asthma and emphysema and the use of models to predict response to therapy. Finally we introduce a large European project, AirPROM that is developing multiscale models toinvestigate structure-function relationships in asthma and COPD.

Show MeSH
Related in: MedlinePlus