The effects of iterative reconstruction and kernel selection on quantitative computed tomography measures of lung density. Issue 6 (12th May 2017)

• Record Type: Journal Article
• Authors: Rodriguez, Alfonso; Ranallo, Frank N.; Judy, Philip F.; Fain, Sean B.
• Is Part Of: Medical physics. Volume 44:Issue 6(2017)
• Page Start: 2267
• Page End: 2280
• Publication Date: 2017-05-12
• Subjects: computed tomography -- emphysema -- iterative reconstruction -- lung density -- perc15 -- quantitative imaging -- RA950; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1002/mp.12255
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: To determine the effects of iterative reconstruction (IR) and high‐frequency kernels on quantitative computed tomography (qCT) density measures at reduced X‐ray dose. Materials and methods: The COPDGene 2 Phantom (CTP 698, The Phantom Laboratory, Salem, NY) with four embedded lung mimicking foam densities (12lb, 20lb, and 4lb), as well as water, air, and acrylic reference inserts, was imaged using a GE 64 slice CT750 HD scanner in helical mode with four current‐time products ranging from 12 to 100 mAs. The raw acquired data were reconstructed using standard (STD — low frequency) and Bone (high frequency) kernels with filtered back projection (FBP), 100% ASiR, and Veo reconstruction algorithms. The reference density inserts were manually segmented using Slicer3D (www.slicer.org ), and the mean, standard deviation, and histograms of the segmented regions were generated using Fiji (http://fiji.sc/Fiji ) for each reconstruction. Measurements of threshold values placed on the cumulative frequency distribution of voxels determined by these measured histograms at 5%, PD5phant, and 15%, PD15phant, (analogous to the relative area below −950 HU (RA‐950) and percent density 15 (PD15) in human lung emphysema quantification, respectively), were also performed. Results: The use of high‐resolution kernels in conjunction with ASiR and Veo did not significantly affect the mean Hounsfield units (HU) of each of the density standards (< 4 HU deviation) and current‐time products within the phantom when compared with the STD+FBP reconstruction conventionally used in clinical applications. A truncation of the scanner reported HU values at −1024 that shifts the mean toward more positive values was found to cause a systematic error in lower attenuating regions. Use of IR drove convergence toward the mean of measured histograms (~100–137% increase in the number measured voxels at the mean of the histogram), while the combination of Bone+ASiR preserved the standard deviation of HU values about the mean compared to STD+FBP, with the added effect of improved spatial resolution and accuracy in airway measures. PD5phant and PD15phant were most similar between the Bone+ASiR and STD+FBP in all regions except those affected by the −1024 truncation artifact. Conclusions: Extension of the scanner reportable HU values below the present limit of −1024 will mitigate discrepancies found in qCT lung densitometry in low‐density regions. The density histogram became more sharply peaked, and standard deviation was reduced for IR, directly effecting density thresholds, PD5phant and PD15phant, placed on the cumulative frequency distribution of each region in the phantom, which serve as analogs to RA‐950 and PD15 typically used in lung density quantitation. The combination of high‐frequency kernels (Bone) with ASiR mitigates this effect and preserves density measures derived from the image histogram. Moreover, previous studies have shown improved accuracy of qCT airway measures of wall thickness (WT) and wall area percentage (WA%) when using high‐frequency kernels in combination with ASiR to better represent airway walls. The results therefore suggest an IR approach for accurate assessment of airway and parenchymal density measures in the lungs.