Lesion detection performance of cone beam CT images with anatomical background noise: Single-slice vs. multi-slice human and model observer study

Minah Han, Hanjoo Jang, Jongduk Baek

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

Abstract

We investigate lesion detectability and its trends for different noise structures in single-slice and multislice CBCT images with anatomical background noise. Anatomical background noise is modeled using a power law spectrum of breast anatomy. Spherical signal with a 2 mm diameter is used for modeling a lesion. CT projection data are acquired by the forward projection and reconstructed by the Feldkamp-Davis-Kress algorithm. To generate different noise structures, two types of reconstruction filters (Hanning and Ram-Lak weighted ramp filters) are used in the reconstruction, and the transverse and longitudinal planes of reconstructed volume are used for detectability evaluation. To evaluate single-slice images, the central slice, which contains the maximum signal energy, is used. To evaluate multislice images, central nine slices are used. Detectability is evaluated using human and model observer studies. For model observer, channelized Hotelling observer (CHO) with dense difference-of-Gaussian (D-DOG) channels are used. For all noise structures, detectability by a human observer is higher for multislice images than single-slice images, and the degree of detectability increase in multislice images depends on the noise structure. Variation in detectability for different noise structures is reduced in multislice images, but detectability trends are not much different between single-slice and multislice images. The CHO with D-DOG channels predicts detectability by a human observer well for both single-slice and multislice images.

Original languageEnglish
Title of host publicationMedical Imaging 2018
Subtitle of host publicationImage Perception, Observer Performance, and Technology Assessment
EditorsFrank W. Samuelson, Robert M. Nishikawa
PublisherSPIE
ISBN (Electronic)9781510616431
DOIs
Publication statusPublished - 2018 Jan 1
EventMedical Imaging 2018: Image Perception, Observer Performance, and Technology Assessment - Houston, United States
Duration: 2018 Feb 112018 Feb 12

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume10577
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2018: Image Perception, Observer Performance, and Technology Assessment
CountryUnited States
CityHouston
Period18/2/1118/2/12

Fingerprint

Cone-Beam Computed Tomography
background noise
lesions
Noise
Cones
cones
Architectural Accessibility
projection
trends
filters
ram
anatomy
Anatomy
Breast
ramps
breast
evaluation

All Science Journal Classification (ASJC) codes

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

Cite this

Han, M., Jang, H., & Baek, J. (2018). Lesion detection performance of cone beam CT images with anatomical background noise: Single-slice vs. multi-slice human and model observer study. In F. W. Samuelson, & R. M. Nishikawa (Eds.), Medical Imaging 2018: Image Perception, Observer Performance, and Technology Assessment [105770N] (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 10577). SPIE. https://doi.org/10.1117/12.2286028
Han, Minah ; Jang, Hanjoo ; Baek, Jongduk. / Lesion detection performance of cone beam CT images with anatomical background noise : Single-slice vs. multi-slice human and model observer study. Medical Imaging 2018: Image Perception, Observer Performance, and Technology Assessment. editor / Frank W. Samuelson ; Robert M. Nishikawa. SPIE, 2018. (Progress in Biomedical Optics and Imaging - Proceedings of SPIE).
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abstract = "We investigate lesion detectability and its trends for different noise structures in single-slice and multislice CBCT images with anatomical background noise. Anatomical background noise is modeled using a power law spectrum of breast anatomy. Spherical signal with a 2 mm diameter is used for modeling a lesion. CT projection data are acquired by the forward projection and reconstructed by the Feldkamp-Davis-Kress algorithm. To generate different noise structures, two types of reconstruction filters (Hanning and Ram-Lak weighted ramp filters) are used in the reconstruction, and the transverse and longitudinal planes of reconstructed volume are used for detectability evaluation. To evaluate single-slice images, the central slice, which contains the maximum signal energy, is used. To evaluate multislice images, central nine slices are used. Detectability is evaluated using human and model observer studies. For model observer, channelized Hotelling observer (CHO) with dense difference-of-Gaussian (D-DOG) channels are used. For all noise structures, detectability by a human observer is higher for multislice images than single-slice images, and the degree of detectability increase in multislice images depends on the noise structure. Variation in detectability for different noise structures is reduced in multislice images, but detectability trends are not much different between single-slice and multislice images. The CHO with D-DOG channels predicts detectability by a human observer well for both single-slice and multislice images.",
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Han, M, Jang, H & Baek, J 2018, Lesion detection performance of cone beam CT images with anatomical background noise: Single-slice vs. multi-slice human and model observer study. in FW Samuelson & RM Nishikawa (eds), Medical Imaging 2018: Image Perception, Observer Performance, and Technology Assessment., 105770N, Progress in Biomedical Optics and Imaging - Proceedings of SPIE, vol. 10577, SPIE, Medical Imaging 2018: Image Perception, Observer Performance, and Technology Assessment, Houston, United States, 18/2/11. https://doi.org/10.1117/12.2286028

Lesion detection performance of cone beam CT images with anatomical background noise : Single-slice vs. multi-slice human and model observer study. / Han, Minah; Jang, Hanjoo; Baek, Jongduk.

Medical Imaging 2018: Image Perception, Observer Performance, and Technology Assessment. ed. / Frank W. Samuelson; Robert M. Nishikawa. SPIE, 2018. 105770N (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 10577).

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

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Han M, Jang H, Baek J. Lesion detection performance of cone beam CT images with anatomical background noise: Single-slice vs. multi-slice human and model observer study. In Samuelson FW, Nishikawa RM, editors, Medical Imaging 2018: Image Perception, Observer Performance, and Technology Assessment. SPIE. 2018. 105770N. (Progress in Biomedical Optics and Imaging - Proceedings of SPIE). https://doi.org/10.1117/12.2286028