title: Lung Nodule Volume Quantification and Shape Differentiation with an Ultra-High Resolution Technique on a Photon Counting Detector CT System.
abstract: A new ultra high-resolution (UHR) mode has been implemented on a whole body photon counting-detector (PCD) CT system. The UHR mode has a pixel size of 0.25 mm by 0.25 mm at the iso-center, while the conventional (macro) mode is limited to 0.5 mm by 0.5 mm. A set of synthetic lung nodules (two shapes, five sizes, and two radio-densities) was scanned using both the UHR and macro modes and reconstructed with 2 reconstruction kernels (4 sets of images in total). Linear regression analysis was performed to compare measured nodule volumes from CT images to reference volumes. Surface curvature was calculated for each nodule and the full width half maximum (FWHM) of the curvature histogram was used as a shape index to differentiate sphere and star shape nodules. Receiver operating characteristic (ROC) analysis was performed and area under the ROC curve (AUC) was used as a figure of merit for the differentiation task. Results showed strong linear relationship between measured nodule volume and reference standard for both UHR and macro mode. For all nodules, volume estimation was more accurate using UHR mode with sharp kernel (S80f), with lower mean absolute percent error (MAPE) (6.5%) compared with macro mode (11.1% to 12.9%). The improvement of volume measurement from UHR mode was more evident particularly for small nodule size (3mm, 5mm), or star-shape nodules. Images from UHR mode with sharp kernel (S80f) consistently demonstrated the best performance (AUC = 0.85) when separating star from sphere shape nodules among all acquisition and reconstruction modes. Our results showed the advantages of UHR mode on a PCD CT scanner in lung nodule characterization. Various clinical applications, including quantitative imaging, can benefit substantially from this high resolution mode.
authors.affiliation: Department of Radiology, Mayo Clinic, Rochester, MN, 55901.
authors.forename: W
authors.initials: W
authors.lastname: Zhou
authors.affiliation: Department of Radiology, Mayo Clinic, Rochester, MN, 55901.
authors.forename: J
authors.initials: J
authors.lastname: Montoya
authors.affiliation: CAMP, Technical University of Munich, Garching (Munich), Germany; Siemens Healthcare, Forchheim, Germany.
authors.forename: R
authors.initials: R
authors.lastname: Gutjahr
authors.affiliation: Department of Radiology, Mayo Clinic, Rochester, MN, 55901.
authors.forename: A
authors.initials: A
authors.lastname: Ferrero
authors.affiliation: Siemens Healthcare, Malvern, PA.
authors.forename: A
authors.initials: A
authors.lastname: Halaweish
authors.affiliation: Siemens Healthcare, Forchheim, Germany.
authors.forename: S
authors.initials: S
authors.lastname: Kappler
authors.affiliation: Department of Radiology, Mayo Clinic, Rochester, MN, 55901.
authors.forename: C
authors.initials: C
authors.lastname: McCollough
authors.affiliation: Department of Radiology, Mayo Clinic, Rochester, MN, 55901.
authors.forename: S
authors.initials: S
authors.lastname: Leng
keywords: Computed tomography (CT); lung nodule; photon counting detector (PCD); shape differentiation; shape index; volume