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
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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. @en
