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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 10 mm slice intervals on PRM. Spacing between slices is illustrated in (A). PRM differences from full resolution are presented per GOLD stage for a standard reconstruction kernel (B). Variations in PRMNormal are greater for mild COPD. PRMfSAD varies the most in mild COPD, whereas similar but small variations in all 3 metrics are seen in moderate-to-severe COPD. The lines on the box plot are as follows: The lines on the box plot are as follows: center line, median; bottom and top boxes, 25% and 75% quantiles, respectively; bottom and top bars, 1.5 times the box size or the minimum value if no values fall in that range. Note for normally distributed data approximately 95% of the data are expected to lie between the distal fences (n = 4 per GOLD stage).
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Figure 5: Impact of 10 mm slice intervals on PRM. Spacing between slices is illustrated in (A). PRM differences from full resolution are presented per GOLD stage for a standard reconstruction kernel (B). Variations in PRMNormal are greater for mild COPD. PRMfSAD varies the most in mild COPD, whereas similar but small variations in all 3 metrics are seen in moderate-to-severe COPD. The lines on the box plot are as follows: The lines on the box plot are as follows: center line, median; bottom and top boxes, 25% and 75% quantiles, respectively; bottom and top bars, 1.5 times the box size or the minimum value if no values fall in that range. Note for normally distributed data approximately 95% of the data are expected to lie between the distal fences (n = 4 per GOLD stage).

Mentions: The impact of noncontiguous CT scans with a 10-mm gap spacing on PRM were evaluated next (Figure 5). Figure 5A shows a schematic of the simulated distribution of CT slices with 10-mm gaps superimposed on a full-inspiration lung scan. Overall, mean differences in PRM values between gapped and full-resolution (ie, contiguous slices) CT data were generally small, with moderate-to-severe emphysema (<1%) being the least affected. Differences were found to be slightly elevated in GOLD stage 0 (PRMNormal < <3%) and GOLD stage 1 (PRMNormal = <2%; PRMfSAD = <1.5%) participants (Figure 5B). Nevertheless, differences in all PRM metrics were relatively low and likely resulted from the diffuse nature of COPD in the cases analyzed.


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 10 mm slice intervals on PRM. Spacing between slices is illustrated in (A). PRM differences from full resolution are presented per GOLD stage for a standard reconstruction kernel (B). Variations in PRMNormal are greater for mild COPD. PRMfSAD varies the most in mild COPD, whereas similar but small variations in all 3 metrics are seen in moderate-to-severe COPD. The lines on the box plot are as follows: The lines on the box plot are as follows: center line, median; bottom and top boxes, 25% and 75% quantiles, respectively; bottom and top bars, 1.5 times the box size or the minimum value if no values fall in that range. Note for normally distributed data approximately 95% of the data are expected to lie between the distal fences (n = 4 per GOLD stage).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 5: Impact of 10 mm slice intervals on PRM. Spacing between slices is illustrated in (A). PRM differences from full resolution are presented per GOLD stage for a standard reconstruction kernel (B). Variations in PRMNormal are greater for mild COPD. PRMfSAD varies the most in mild COPD, whereas similar but small variations in all 3 metrics are seen in moderate-to-severe COPD. The lines on the box plot are as follows: The lines on the box plot are as follows: center line, median; bottom and top boxes, 25% and 75% quantiles, respectively; bottom and top bars, 1.5 times the box size or the minimum value if no values fall in that range. Note for normally distributed data approximately 95% of the data are expected to lie between the distal fences (n = 4 per GOLD stage).
Mentions: The impact of noncontiguous CT scans with a 10-mm gap spacing on PRM were evaluated next (Figure 5). Figure 5A shows a schematic of the simulated distribution of CT slices with 10-mm gaps superimposed on a full-inspiration lung scan. Overall, mean differences in PRM values between gapped and full-resolution (ie, contiguous slices) CT data were generally small, with moderate-to-severe emphysema (<1%) being the least affected. Differences were found to be slightly elevated in GOLD stage 0 (PRMNormal < <3%) and GOLD stage 1 (PRMNormal = <2%; PRMfSAD = <1.5%) participants (Figure 5B). Nevertheless, differences in all PRM metrics were relatively low and likely resulted from the diffuse nature of COPD in the cases analyzed.

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