Development of a Phantom for Evaluation and Assurance Of Image Quality in CT Scanning

Goodenough, David J.; Weaver, Kenneth E.; Davis, David O.

doi:10.1117/12.7972077

Cited by 18 publications

(7 citation statements)

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“…One of the most widely used such phantom is the Catphan 600 (Phantom Laboratories, New York), which has a modular structure. 1 Each module was designed for specific measurements. Performing well with this phantom is required, but not sufficient per se for accurate diagnostics.…”

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confidence: 99%

How Different Iterative and Filtered Back Projection Kernels Affect Computed Tomography Numbers and Low Contrast Detectability

Völgyes

Pedersen

Stray-Pedersen

et al. 2017

Journal of Computer Assisted Tomography

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confidence: 99%

How Different Iterative and Filtered Back Projection Kernels Affect Computed Tomography Numbers and Low Contrast Detectability

Völgyes

Pedersen

Stray-Pedersen

et al. 2017

Journal of Computer Assisted Tomography

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“…To overcome these drawbacks, a different experimental approach was proposed. 13,18 Although the methodology was originally proposed for the step-and-shoot mode, the same principle can be applied to helical CT. Instead of measuring the SSP directly along the z-axis, we perform an indirect measurement of SSP in the x direction.…”

Section: Resultsmentioning

confidence: 99%

Analytical models for multi-slice helical CT performance parameters

Hsieh¹

2003

Med. Phys.

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One of the most recent technological advancements in computed tomography (CT) is the introduction of multi-slice CT (MSCT). When combined with the helical scan mode, MSCT offers significant improvements in volume coverage, isotropic spatial resolution, and contrast utilization. Although experimental studies have been conducted on MSCT performance, there is a lack of theoretical analysis on the slice sensitivity profile (SSP) and noise performance. In this paper, we derive several closed-form expressions, for linear interpolation based helical reconstruction algorithms, to characterize these performance parameters under different detector configurations and acquisition modes. Following the common practice, the expressions are explicitly described for regions near the iso-center, although the same approach can be used to describe system performances away from the iso-center. These models are validated against phantom experiments.

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“…As such, a CT scanner is characterised in terms of its PSF or its frequency-domain representation, the modulation transfer function (MTF) (GOODENOUGH et al, 1977), and its noise properties. Departure from linearity leads to the presence of artifacts, as described in JOSEPH (1981).…”

Section: Corespondence Should Be Addressed To Drmentioning

confidence: 99%

Experimental evaluation of computerised tomography point spread function variability within the field of view: Parametric models

Doré

Kearney

2004

Med. Biol. Eng. Comput.

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The objective of the paper was to validate non-linear parametric models of computerised tomography point spread function (PSF), to investigate the role of model parameters and to verify the effect of different imaging conditions on estimated parameters. These models were then to be used experimentally to estimate the variation of PSF shape within the field of view of a scanner. Two parametric models of the PSF are presented. The Gaussian model is appropriate when PSF values are positive, and the damped cosine model can account for negative values. These models are non-linear and fully two-dimensional and do not assume radial symmetry. The models were fitted to images of a point source. The models accounted for over 99% of the variance in the PSF signal. Errors in modulation transfer function were limited to 5% when the appropriate model was selected. The difference in the blurring characteristics of three image reconstruction filters was well quantified by shape parameters, and position parameters located the PSF with subpixel accuracy. With a point source located 50mm directly above the centre of the field of view, the PSF was found to be anisotropic.

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Development of a Phantom for Evaluation and Assurance Of Image Quality in CT Scanning

Cited by 18 publications

References 0 publications

How Different Iterative and Filtered Back Projection Kernels Affect Computed Tomography Numbers and Low Contrast Detectability

How Different Iterative and Filtered Back Projection Kernels Affect Computed Tomography Numbers and Low Contrast Detectability

Analytical models for multi-slice helical CT performance parameters

Experimental evaluation of computerised tomography point spread function variability within the field of view: Parametric models

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