Routine use of a higher order interpolator and bone algorithm in thoracic CT.

Hopper, Kenneth D.; Kasales, Claudia J.; Mahraj, Rickhesvaar; Eggli, Kathleen D.; Patrone, S; TenHave, Thomas R.; Fisher, Alicia L.

doi:10.2214/ajr.167.4.8819390

Cited by 6 publications

(1 citation statement)

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“…Despite the wide endorsement of using lung kernel reconstruction algorithms for the diagnosis of chest CT patterns, there is no recent evidence supporting those recommendations. Results published prior to the introduction of helical CT in the early 1990 s indicated the beneficial use of lung kernel reconstructions on chest CT 9 17 18 19 . Despite the advances in CT technique, to our knowledge no systematic investigation of image reconstruction algorithms and their impact on lung parenchymal assessment has been conducted.…”

Section: Introductionmentioning

confidence: 99%

Influence of Lung Reconstruction Algorithms on Interstitial Lung Pattern Recognition on CT

Klaus

Christodoulidis

Peters

et al. 2022

Rofo

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Background Despite current recommendations, there is no recent scientific study comparing the influence of CT reconstruction kernels on lung pattern recognition in interstitial lung disease (ILD). Purpose To evaluate the sensitivity of lung (i70) and soft (i30) CT kernel algorithms for the diagnosis of ILD patterns. Materials and Methods We retrospectively extracted between 15–25 pattern annotations per case (1 annotation = 15 slices of 1 mm) from 23 subjects resulting in 408 annotation stacks per lung kernel and soft kernel reconstructions. Two subspecialized chest radiologists defined the ground truth in consensus. 4 residents, 2 fellows, and 2 general consultants in radiology with 3 to 13 years of experience in chest imaging performed a blinded readout. In order to account for data clustering, a generalized linear mixed model (GLMM) with random intercept for reader and nested for patient and image and a kernel/experience interaction term was used to analyze the results. Results The results of the GLMM indicated, that the odds of correct pattern recognition is 12 % lower with lung kernel compared to soft kernel; however, this was not statistically significant (OR 0.88; 95%-CI, 0.73–1.06; p = 0.187). Furthermore, the consultants’ odds of correct pattern recognition was 78 % higher than the residents’ odds, although this finding did not reach statistical significance either (OR 1.78; 95%-CI, 0.62–5.06; p = 0.283). There was no significant interaction between the two fixed terms kernel and experience. Intra-rater agreement between lung and soft kernel was substantial (κ = 0.63 ± 0.19). The mean inter-rater agreement for lung/soft kernel was κ = 0.37 ± 0.17/κ = 0.38 ± 0.17. Conclusion There is no significant difference between lung and soft kernel reconstructed CT images for the correct pattern recognition in ILD. There are non-significant trends indicating that the use of soft kernels and a higher level of experience lead to a higher probability of correct pattern identification. Key points: Citation Format

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