Lung nodule volume quantification and shape differentiation with an ultra-high resolution technique on a photon counting detector CT system

W. Zhou, J. Montoya, R. Gutjahr, Andrea Ferrero, A. Halaweish, S. Kappler, Cynthia H McCollough, Shuai Leng

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

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.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2017
Subtitle of host publicationPhysics of Medical Imaging
PublisherSPIE
Volume10132
ISBN (Electronic)9781510607095
DOIs
StatePublished - 2017
EventMedical Imaging 2017: Physics of Medical Imaging - Orlando, United States
Duration: Feb 13 2017Feb 16 2017

Other

OtherMedical Imaging 2017: Physics of Medical Imaging
CountryUnited States
CityOrlando
Period2/13/172/16/17

Fingerprint

nodules
Photons
ROC Curve
lungs
Macros
counting
Stars
Detectors
Lung
high resolution
detectors
photons
Whole-Body Counting
Cone-Beam Computed Tomography
Radio
Volume measurement
Area Under Curve
Linear Models
Regression Analysis
Linear regression

Keywords

  • Computed tomography (CT)
  • Lung nodule
  • Photon counting detector (PCD)
  • Shape differentiation
  • Shape index
  • Volume

ASJC Scopus subject areas

  • Atomic and Molecular Physics, and Optics
  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Radiology Nuclear Medicine and imaging

Cite this

Zhou, W., Montoya, J., Gutjahr, R., Ferrero, A., Halaweish, A., Kappler, S., ... Leng, S. (2017). Lung nodule volume quantification and shape differentiation with an ultra-high resolution technique on a photon counting detector CT system. In Medical Imaging 2017: Physics of Medical Imaging (Vol. 10132). [101323Q] SPIE. https://doi.org/10.1117/12.2255736

Lung nodule volume quantification and shape differentiation with an ultra-high resolution technique on a photon counting detector CT system. / Zhou, W.; Montoya, J.; Gutjahr, R.; Ferrero, Andrea; Halaweish, A.; Kappler, S.; McCollough, Cynthia H; Leng, Shuai.

Medical Imaging 2017: Physics of Medical Imaging. Vol. 10132 SPIE, 2017. 101323Q.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Zhou, W, Montoya, J, Gutjahr, R, Ferrero, A, Halaweish, A, Kappler, S, McCollough, CH & Leng, S 2017, Lung nodule volume quantification and shape differentiation with an ultra-high resolution technique on a photon counting detector CT system. in Medical Imaging 2017: Physics of Medical Imaging. vol. 10132, 101323Q, SPIE, Medical Imaging 2017: Physics of Medical Imaging, Orlando, United States, 2/13/17. https://doi.org/10.1117/12.2255736
Zhou W, Montoya J, Gutjahr R, Ferrero A, Halaweish A, Kappler S et al. Lung nodule volume quantification and shape differentiation with an ultra-high resolution technique on a photon counting detector CT system. In Medical Imaging 2017: Physics of Medical Imaging. Vol. 10132. SPIE. 2017. 101323Q https://doi.org/10.1117/12.2255736
Zhou, W. ; Montoya, J. ; Gutjahr, R. ; Ferrero, Andrea ; Halaweish, A. ; Kappler, S. ; McCollough, Cynthia H ; Leng, Shuai. / Lung nodule volume quantification and shape differentiation with an ultra-high resolution technique on a photon counting detector CT system. Medical Imaging 2017: Physics of Medical Imaging. Vol. 10132 SPIE, 2017.
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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.",
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