Simulation of scanner- and patient-specific low-dose CT imaging from existing CT images

Journal Article (2017)
Author(s)

Robiel E. Naziroglu (TU Delft - ImPhys/Quantitative Imaging)

VF van Ravesteijn (TU Delft - ImPhys/Quantitative Imaging)

Lucas J van Vliet (TU Delft - ImPhys/Quantitative Imaging)

G.J. Streekstra (Universiteit van Amsterdam)

F. M. Vos (Universiteit van Amsterdam, TU Delft - ImPhys/Quantitative Imaging)

Research Group
ImPhys/Quantitative Imaging
Copyright
© 2017 R.E. Naziroglu, V.F. van Ravesteijn, L.J. van Vliet, G.J. Streekstra, F.M. Vos
DOI related publication
https://doi.org/10.1016/j.ejmp.2017.02.009
More Info
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Publication Year
2017
Language
English
Copyright
© 2017 R.E. Naziroglu, V.F. van Ravesteijn, L.J. van Vliet, G.J. Streekstra, F.M. Vos
Research Group
ImPhys/Quantitative Imaging
Bibliographical Note
Accepted Author Manuscript@en
Volume number
36
Pages (from-to)
12-23

Abstract

Purpose:
Simulating low-­dose Computed Tomography (CT) facilitates in-­silico studies into the required dose for a diagnostic task. Conventionally, low-­‐dose CT images are created by adding noise to the projection data. However, in practice the raw data is often simply not available. This paper presents a new method for simulating patient-­‐specific, low-dose CT images without the need
of the original projection data.

Methods:
The low-­dose CT simulation method included the following: (1) computation of a virtual sinogram from a high dose CT image through a
radon transform; (2) simulation of a 'reduced'­‐dose sinogram with appropriate
amounts of noise; (3) subtraction of the high-­‐dose virtual sinogram from the
reduced-­‐dose sinogram; (4) reconstruction of a noise volume via filtered back-projection; (5) addition of the noise image to the original high-dose image. The
required scanner-­Specific parameters, such as the apodization window, bowtie
filter, the X-ray tube output parameter (reflecting the photon flux) and the detector read-­out noise, were retrieved from calibration images of a water
cylinder. The low-­‐dose simulation method was evaluated by comparing the
noise characteristics in simulated images with experimentally acquired
data.

Results:
The models used to recover the scanner-­specific parameters fitted accurately to
the calibration data, and the values of the parameters were comparable to values
reported in literature. Finally, the simulated low-dose images accurately reproduced the noise characteristics in experimentally acquired low-dose­‐volumes.

Conclusion:
The developed methods truthfully simulate low-­dose CT imaging for a specific
scanner and reconstruction using filtered backprojection. The scanner-­‐specific
parameters can be estimated from calibration data.

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