Limits...
The Impact of Sources of Variability on Parametric Response Mapping of Lung CT Scans.

Boes JL, Bule M, Hoff BA, Chamberlain R, Lynch DA, Stojanovska J, Martinez FJ, Han MK, Kazerooni EA, Ross BD, Galbán CJ - Tomography (2015)

Bottom Line: Increasing levels of image misregistration and CT slice spacing were found to have a minor effect on PRM measurements.PRM-derived values were found to be most sensitive to lung volume levels and mismatched reconstruction kernels.As with other quantitative imaging techniques, reliable PRM measurements are attainable when consistent clinical and CT protocols are implemented.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Radiology, University of Michigan, Center for Molecular Imaging, Ann Arbor, MI.

ABSTRACT

Parametric response mapping (PRM) of inspiration and expiration computed tomography (CT) images improves the radiological phenotyping of chronic obstructive pulmonary disease (COPD). PRM classifies individual voxels of lung parenchyma as normal, emphysematous, or nonemphysematous air trapping. In this study, bias and noise characteristics of the PRM methodology to CT and clinical procedures were evaluated to determine best practices for this quantitative technique. Twenty patients of varying COPD status with paired volumetric inspiration and expiration CT scans of the lungs were identified from the baseline COPD-Gene cohort. The impact of CT scanner manufacturer and reconstruction kernels were evaluated as potential sources of variability in PRM measurements along with simulations to quantify the impact of inspiration/expiration lung volume levels, misregistration, and image spacing on PRM measurements. Negligible variation in PRM metrics was observed when CT scanner type and reconstruction were consistent and inspiration/expiration lung volume levels were near target volumes. CT scanner Hounsfield unit drift occurred but remained difficult to ameliorate. Increasing levels of image misregistration and CT slice spacing were found to have a minor effect on PRM measurements. PRM-derived values were found to be most sensitive to lung volume levels and mismatched reconstruction kernels. As with other quantitative imaging techniques, reliable PRM measurements are attainable when consistent clinical and CT protocols are implemented.

No MeSH data available.


Related in: MedlinePlus

Schematic of modes of variability at various stages in the PRM workflow. Clinical protocol consists of patients being trained to hold their breath at full inspiration and full or relaxed expiration. CT protocol guides acquisition at both points using site-specific CT systems, acquisition parameters, and reconstruction kernels. Data processing consists of lungs from the serial CT scans being segmented from the thoracic cavity and then spatially aligned to a single geometric frame using site-specific algorithms. PRM quantification is performed by classifying voxels with paired HU values as normal parenchyma (green voxels), functional small airways disease (yellow voxels), or emphysema (red voxels).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4643661&req=5

Figure 1: Schematic of modes of variability at various stages in the PRM workflow. Clinical protocol consists of patients being trained to hold their breath at full inspiration and full or relaxed expiration. CT protocol guides acquisition at both points using site-specific CT systems, acquisition parameters, and reconstruction kernels. Data processing consists of lungs from the serial CT scans being segmented from the thoracic cavity and then spatially aligned to a single geometric frame using site-specific algorithms. PRM quantification is performed by classifying voxels with paired HU values as normal parenchyma (green voxels), functional small airways disease (yellow voxels), or emphysema (red voxels).

Mentions: This study evaluates the sensitivity of PRM to sources of variability present in a standardized CT clinical acquisition and processing protocol that may affect quantitative CT measurements (Figure 1). Although many of the parameters investigated had a minimal impact on expected PRM values, this work demonstrates the effect of lung inspiration/expiration volume at CT acquisition on PRM measurements. These results reveal that establishing the correct lung inhalation and exhalation volumes during image acquisition is the most important quality control measure for longitudinal CT imaging.


The Impact of Sources of Variability on Parametric Response Mapping of Lung CT Scans.

Boes JL, Bule M, Hoff BA, Chamberlain R, Lynch DA, Stojanovska J, Martinez FJ, Han MK, Kazerooni EA, Ross BD, Galbán CJ - Tomography (2015)

Schematic of modes of variability at various stages in the PRM workflow. Clinical protocol consists of patients being trained to hold their breath at full inspiration and full or relaxed expiration. CT protocol guides acquisition at both points using site-specific CT systems, acquisition parameters, and reconstruction kernels. Data processing consists of lungs from the serial CT scans being segmented from the thoracic cavity and then spatially aligned to a single geometric frame using site-specific algorithms. PRM quantification is performed by classifying voxels with paired HU values as normal parenchyma (green voxels), functional small airways disease (yellow voxels), or emphysema (red voxels).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4643661&req=5

Figure 1: Schematic of modes of variability at various stages in the PRM workflow. Clinical protocol consists of patients being trained to hold their breath at full inspiration and full or relaxed expiration. CT protocol guides acquisition at both points using site-specific CT systems, acquisition parameters, and reconstruction kernels. Data processing consists of lungs from the serial CT scans being segmented from the thoracic cavity and then spatially aligned to a single geometric frame using site-specific algorithms. PRM quantification is performed by classifying voxels with paired HU values as normal parenchyma (green voxels), functional small airways disease (yellow voxels), or emphysema (red voxels).
Mentions: This study evaluates the sensitivity of PRM to sources of variability present in a standardized CT clinical acquisition and processing protocol that may affect quantitative CT measurements (Figure 1). Although many of the parameters investigated had a minimal impact on expected PRM values, this work demonstrates the effect of lung inspiration/expiration volume at CT acquisition on PRM measurements. These results reveal that establishing the correct lung inhalation and exhalation volumes during image acquisition is the most important quality control measure for longitudinal CT imaging.

Bottom Line: Increasing levels of image misregistration and CT slice spacing were found to have a minor effect on PRM measurements.PRM-derived values were found to be most sensitive to lung volume levels and mismatched reconstruction kernels.As with other quantitative imaging techniques, reliable PRM measurements are attainable when consistent clinical and CT protocols are implemented.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Radiology, University of Michigan, Center for Molecular Imaging, Ann Arbor, MI.

ABSTRACT

Parametric response mapping (PRM) of inspiration and expiration computed tomography (CT) images improves the radiological phenotyping of chronic obstructive pulmonary disease (COPD). PRM classifies individual voxels of lung parenchyma as normal, emphysematous, or nonemphysematous air trapping. In this study, bias and noise characteristics of the PRM methodology to CT and clinical procedures were evaluated to determine best practices for this quantitative technique. Twenty patients of varying COPD status with paired volumetric inspiration and expiration CT scans of the lungs were identified from the baseline COPD-Gene cohort. The impact of CT scanner manufacturer and reconstruction kernels were evaluated as potential sources of variability in PRM measurements along with simulations to quantify the impact of inspiration/expiration lung volume levels, misregistration, and image spacing on PRM measurements. Negligible variation in PRM metrics was observed when CT scanner type and reconstruction were consistent and inspiration/expiration lung volume levels were near target volumes. CT scanner Hounsfield unit drift occurred but remained difficult to ameliorate. Increasing levels of image misregistration and CT slice spacing were found to have a minor effect on PRM measurements. PRM-derived values were found to be most sensitive to lung volume levels and mismatched reconstruction kernels. As with other quantitative imaging techniques, reliable PRM measurements are attainable when consistent clinical and CT protocols are implemented.

No MeSH data available.


Related in: MedlinePlus