Volume estimation of multi-density nodules with thoracic CT

Abstract

The purpose of this work was to quantify the effect of surrounding density on the volumetric assessment of lung nodules in a phantom CT study. Eight synthetic multidensity nodules were manufactured by enclosing spherical cores in larger spheres of double the diameter and with a different uniform density. Different combinations of outer/inner diameters (20/10mm, 10/5mm) and densities (100HU/-630HU, 10HU/-630HU, -630HU/100HU, -630HU/-10HU) were created. The nodules were placed within an anthropomorphic phantom and scanned with a 16-detector row CT scanner. Ten repeat scans were acquired using exposures of 20, 100, and 200mAs, slice collimations of 16x0.75mm and 16x1.5mm, and pitch of 1.2, and were reconstructed with varying slice thicknesses (three for each collimation) using two reconstruction filters (medium and standard). The volumes of the inner nodule cores were estimated from the reconstructed CT data using a matched-filter approach with templates modeling the characteristics of the multi-density objects. Volume estimation of the inner nodule was assessed using percent bias (PB) and the standard deviation of percent error (SPE). The true volumes of the inner nodules were measured using micro CT imaging. Results show PB values ranging from -12.4 to 2.3% and SPE values ranging from 1.8 to 12.8%. This study indicates that the volume of multi-density nodules can be measured with relatively small percent bias (on the order of ±12% or less) when accounting for the properties of surrounding densities.These findings can provide valuable information for Downloaded From: http://proceedings.spiedigitallibrary.org/ on 06/23/2016 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspxunderstanding bias and variability in clinical measurements of nodules that also include local biological changes such as inflammation and necrosis.

Statistical analysis of lung nodule volume measurements with CT in a large‐scale phantom study

“…likely represent reasonable starting ranges for more complex nonuniform nodules. To test this, we initiated phantom studies of mixed-density nodules and our preliminary results were reported by Gavrielides et al 26 As mentioned but not emphasized earlier, we transformed the data for the purpose of stabilizing/equalizing the variance. This step is in fact critical for most of the analyses we performed.…”

The public cancer radiology imaging collections of The Cancer Imaging Archive

“…Phantom FDA Collection [Data Citation 5] contains CT images of the FDA anthropomorphic thorax phantom 18 configured with 8 nodule layouts scanned on a Philips Mx8000 IDT (Philips Healthcare, Andover, MA) and a Siemens Somatom Definition 64 scanner (Siemens Medical Solutions USA, Inc., Malvern, PA). Each nodule layout was scanned using 16 acquisition protocols that varied combinations of effective dose, pitch, and slice collimation, and were reconstructed with varying combinations of slice thicknesses and reconstruction kernels 19 . Ten exposures were acquired for each imaging protocol on each scanner.…”

Systematic analysis of bias and variability of texture measurements in computed tomography

“…7 To make maximal use of the spatially rich quantitative features in CT images, a considerable effort has been made to analyze the texture features. Some studies have shown that CT image acquisition can influence measurement of a single quantitative feature [8][9][10] or an ensemble of features. [11][12][13] Other phantom studies show that intrascanner and interscanner variabilities can depend on several factors including the phantom material composition, radiomic feature calculation method, and scan settings.…”