Performance assessment of texture reproduction in high-resolution CT

Hui Shi, Grace J. Gang, Junyuan Li, Eleni Liapi, Craig Abbey, J. Webster Stayman

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

Abstract

Assessment of computed tomography (CT) images can be complex due to a number of dependencies that affect system performance. In particular, it is well-known that noise in CT is object-dependent. Such objectdependence can be more pronounced and extend to resolution and image textures with the increasing adoption of model-based reconstruction and processing with machine learning methods. Moreover, such processing is often inherently nonlinear complicating assessments with simple measures of spatial resolution, etc. Similarly, recent advances in CT system design have attempted to improve fine resolution details - e.g., with newer detectors, smaller focal spots, etc. Recognizing these trends, there is a greater need for imaging assessment that are considering specific features of interest that can be placed within an anthropomorphic phantom for realistic emulation and evaluation. In this work, we devise a methodology for 3D-printing phantom inserts using procedural texture generation for evaluation of performance of high-resolution CT systems. Accurate representations of texture have previously been a hindrance to adoption of processing methods like model-based reconstruction, and texture serves as an important diagnostic feature (e.g. heterogeneity of lesions is a marker for malignancy). We consider the ability of different systems to reproduce various textures (as a function of the intrinsic feature sizes of the texture), comparing microCT, cone-beam CT, and diagnostic CT using normal- and high-resolution modes. We expect that this general methodology will provide a pathway for repeatable and robust assessments of different imaging systems and processing methods.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2020
Subtitle of host publicationImage Perception, Observer Performance, and Technology Assessment
EditorsFrank W. Samuelson, Sian Taylor-Phillips
PublisherSPIE
ISBN (Electronic)9781510633995
DOIs
StatePublished - 2020
EventMedical Imaging 2020: Image Perception, Observer Performance, and Technology Assessment - Houston, United States
Duration: Feb 19 2020Feb 20 2020

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume11316
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2020: Image Perception, Observer Performance, and Technology Assessment
CountryUnited States
CityHouston
Period2/19/202/20/20

ASJC Scopus subject areas

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

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  • Cite this

    Shi, H., Gang, G. J., Li, J., Liapi, E., Abbey, C., & Stayman, J. W. (2020). Performance assessment of texture reproduction in high-resolution CT. In F. W. Samuelson, & S. Taylor-Phillips (Eds.), Medical Imaging 2020: Image Perception, Observer Performance, and Technology Assessment [113160R] (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 11316). SPIE. https://doi.org/10.1117/12.2550579