4D CT lung ventilation images are affected by the 4D CT sorting method. Issue 10 (11th September 2013)

• Record Type: Journal Article
• Authors: Yamamoto, Tokihiro; Kabus, Sven; Lorenz, Cristian; Johnston, Eric; Maxim, Peter G.; Diehn, Maximilian; Eclov, Neville; Barquero, Cristian; Loo, Billy W.; Keall, Paul J.
• Is Part Of: Medical physics. Volume 40:Issue 10(2013)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2013-09-11
• Subjects: Computed tomography -- Pneumodyamics, respiration -- Cancer -- Segmentation; cancer -- computerised tomography -- correlation methods -- diagnostic radiography -- image segmentation -- lung -- medical image processing; lung -- functional imaging -- ventilation -- four‐dimensional (4D) CT -- artifact; Computerised tomographs -- Biological material, e.g. blood, urine; Haemocytometers -- Digital computing or data processing equipment or methods, specially adapted for specific applications -- Image data processing or generation, in general; Computed tomography -- Medical imaging -- Lungs -- Single photon emission computed tomography -- Medical image artifacts -- Cancer -- Medical image segmentation -- Spatial dimensions -- Spatial analysis -- Digital image processing; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4820538
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: : Four‐dimensional (4D) computed tomography (CT) ventilation imaging is a novel promising technique for lung functional imaging. The current standard 4D CT technique using phase‐based sorting frequently results in artifacts, which may deteriorate the accuracy of ventilation imaging. The purpose of this study was to quantify the variability of 4D CT ventilation imaging due to 4D CT sorting. Methods: : 4D CT image sets from nine lung cancer patients were each sorted by the phase‐based method and anatomic similarity‐based method, designed to reduce artifacts, with corresponding ventilation images created for each method. Artifacts in the resulting 4D CT images were quantified with the artifact score which was defined based on the difference between the normalized cross correlation for CT slices within a CT data segment and that for CT slices bordering the interface between adjacent CT data segments. The ventilation variation was quantified using voxel‐based Spearman rank correlation coefficients for all lung voxels, and Dice similarity coefficients (DSC) for the spatial overlap of low‐functional lung volumes. Furthermore, the correlations with matching single‐photon emission CT (SPECT) ventilation images (assumed ground truth) were evaluated for three patients to investigate which sorting method provides higher physiologic accuracy. Results: : Anatomic similarity‐based sorting reduced 4D CT artifacts compared to phase‐based sorting (artifact score, 0.45 ± 0.14 vs 0.58 ± 0.24, p = 0.10 at peak‐exhale; 0.63 ± 0.19 vs 0.71 ± 0.31, p = 0.25 at peak‐inhale). The voxel‐based correlation between the two ventilation images was 0.69 ± 0.26 on average, ranging from 0.03 to 0.85. The DSC was 0.71 ± 0.13 on average. Anatomic similarity‐based sorting yielded significantly fewer lung voxels with paradoxical negative ventilation values than phase‐based sorting (5.0 ± 2.6% vs 9.7 ± 8.4%, p = 0.05), and improved the correlation with SPECT ventilation regionally. Conclusions: : The variability of 4D CT ventilation imaging due to 4D CT sorting was moderate overall and substantial in some cases, suggesting that 4D CT artifacts are an important source of variations in 4D CT ventilation imaging. Reduction of 4D CT artifacts provided more physiologically convincing and accurate ventilation estimates. Further studies are needed to confirm this result.