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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

Impact of misregistration on PRM. Misregistration is simulated using a feature set (aqua dots) shown on a sample lung volume (A) that is moved slightly to deform a good registration solution (4 each; Gold stages 0 to 4). Changes in PRM increase as the mean absolute misregistration across the feature set increases. PRM changes are pooled by GOLD stage and represent mean ± SD differences (B–F). Note that mean changes of 2.5 mm may include local deformation regions of up to 7.5 mm or approximately 10 voxels.
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Figure 6: Impact of misregistration on PRM. Misregistration is simulated using a feature set (aqua dots) shown on a sample lung volume (A) that is moved slightly to deform a good registration solution (4 each; Gold stages 0 to 4). Changes in PRM increase as the mean absolute misregistration across the feature set increases. PRM changes are pooled by GOLD stage and represent mean ± SD differences (B–F). Note that mean changes of 2.5 mm may include local deformation regions of up to 7.5 mm or approximately 10 voxels.

Mentions: Simulations were performed to assess how sensitive the PRM metrics were to the misregistration of paired whole-lung CT data. Widespread local misregistration showed progressive variability of PRM metrics as a function of increasing deformations (Figure 6). The largest differences in PRM were observed in GOLD stage 3 and 4 participants, where all 3 classifications provide significant contributions to the lung volume. Overall, mean misregistrations of 2.5 mm (maximum deformation of 7.5 mm) yielded PRM differences of up to 1% of the lung. Furthermore, we also investigated and quantified the impact of registration direction on PRM because this can affect overall PRM results. Registering the inspiration to the expiration images resulted in higher percentages of tissue identified as air trapping (Figure 7). Because tissue compresses upon expiration, tissue with functional small airways disease compressed less and remained a larger percentage of the total lung volume (mean: PRMfSAD = 3.1; 95% CI: 0.8, 5.4) than if measured on the inspiration volume. Differences of PRMNormal and PRMEmph were close to 0, but PRMNormal had a slightly larger 95% CI (mean: PRMNormal = 0.1; 95% CI: −4.1, 4.2) (mean: PRMEmph = −0.4; 95% CI: −1.6, 0.8).


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)

Impact of misregistration on PRM. Misregistration is simulated using a feature set (aqua dots) shown on a sample lung volume (A) that is moved slightly to deform a good registration solution (4 each; Gold stages 0 to 4). Changes in PRM increase as the mean absolute misregistration across the feature set increases. PRM changes are pooled by GOLD stage and represent mean ± SD differences (B–F). Note that mean changes of 2.5 mm may include local deformation regions of up to 7.5 mm or approximately 10 voxels.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 6: Impact of misregistration on PRM. Misregistration is simulated using a feature set (aqua dots) shown on a sample lung volume (A) that is moved slightly to deform a good registration solution (4 each; Gold stages 0 to 4). Changes in PRM increase as the mean absolute misregistration across the feature set increases. PRM changes are pooled by GOLD stage and represent mean ± SD differences (B–F). Note that mean changes of 2.5 mm may include local deformation regions of up to 7.5 mm or approximately 10 voxels.
Mentions: Simulations were performed to assess how sensitive the PRM metrics were to the misregistration of paired whole-lung CT data. Widespread local misregistration showed progressive variability of PRM metrics as a function of increasing deformations (Figure 6). The largest differences in PRM were observed in GOLD stage 3 and 4 participants, where all 3 classifications provide significant contributions to the lung volume. Overall, mean misregistrations of 2.5 mm (maximum deformation of 7.5 mm) yielded PRM differences of up to 1% of the lung. Furthermore, we also investigated and quantified the impact of registration direction on PRM because this can affect overall PRM results. Registering the inspiration to the expiration images resulted in higher percentages of tissue identified as air trapping (Figure 7). Because tissue compresses upon expiration, tissue with functional small airways disease compressed less and remained a larger percentage of the total lung volume (mean: PRMfSAD = 3.1; 95% CI: 0.8, 5.4) than if measured on the inspiration volume. Differences of PRMNormal and PRMEmph were close to 0, but PRMNormal had a slightly larger 95% CI (mean: PRMNormal = 0.1; 95% CI: −4.1, 4.2) (mean: PRMEmph = −0.4; 95% CI: −1.6, 0.8).

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