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Proceedings Paper

Performance assessment of texture reproduction in high-resolution CT
Author(s): Hui Shi; Grace J. Gang; Junyuan Li; Eleni Liapi; Craig Abbey; J. Webster Stayman
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Paper 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.

Paper Details

Date Published: 16 March 2020
PDF: 6 pages
Proc. SPIE 11316, Medical Imaging 2020: Image Perception, Observer Performance, and Technology Assessment, 113160R (16 March 2020); doi: 10.1117/12.2550579
Show Author Affiliations
Hui Shi, Johns Hopkins Univ. (United States)
Grace J. Gang, Johns Hopkins Univ. (United States)
Junyuan Li, Johns Hopkins Univ. (United States)
Eleni Liapi, Johns Hopkins Univ. (United States)
Craig Abbey, Univ. of California, Santa Barbara (United States)
J. Webster Stayman, Johns Hopkins Univ. (United States)


Published in SPIE Proceedings Vol. 11316:
Medical Imaging 2020: Image Perception, Observer Performance, and Technology Assessment
Frank W. Samuelson; Sian Taylor-Phillips, Editor(s)

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