Simulation of motion artifacts in offset flat-panel cone-beam CT

Hansis, Eberhard; Shao, L. G.

doi:10.1109/nssmic.2011.6152670

Cited by 1 publication

(1 citation statement)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We created simulated dynamic perfusion data with realistic CT noise using the physiologic simulator described above and a CT simulator, giving what we call the digital MPI-CT phantom. The numerical CT simulator (Radonis software, Philips Research and Development, Hamburg, Germany) has been applied in previous publications (Riviere and Vargas 2008, Hansis and Shao 2011) and models a realistic CT scanner (Brilliance 64, Philips), with cone beam source, finite width detector grid, x-ray pre-filtration, and Poisson noise sampling. In this work, we used only 70 keV photons in the CT simulation to generate a true mono-energetic MPI-CT data set in order to avoid confounding effects of beam-hardening artifacts.…”

Section: Experimental Methodsmentioning

confidence: 99%

The role of acquisition and quantification methods in myocardial blood flow estimability for myocardial perfusion imaging CT

Eck¹,

Muzic²,

Levi³

et al. 2018

Phys. Med. Biol.

View full text Add to dashboard Cite

In this work, we clarified the role of acquisition parameters and quantification methods in myocardial blood flow (MBF) estimability for myocardial perfusion imaging using CT (MPI-CT). We used a physiologic model with a CT simulator to generate time-attenuation curves across a range of imaging conditions, i.e. tube current-time product, imaging duration, and temporal sampling, and physiologic conditions, i.e. MBF and arterial input function width. We assessed MBF estimability by precision (interquartile range of MBF estimates) and bias (difference between median MBF estimate and reference MBF) for multiple quantification methods. Methods included: six existing model-based deconvolution models, such as the plug-flow tissue uptake model (PTU), Fermi function model, and single-compartment model (SCM); two proposed robust physiologic models (RPM1, RPM2); model-independent singular value decomposition with Tikhonov regularization determined by the L-curve criterion (LSVD); and maximum upslope (MUP). Simulations show that MBF estimability is most affected by changes in imaging duration for model-based methods and by changes in tube current-time product and sampling interval for model-independent methods. Models with three parameters, i.e. RPM1, RPM2, and SCM, gave least biased and most precise MBF estimates. The average relative bias (precision) for RPM1, RPM2, and SCM was ⩽11% (⩽10%) and the models produced high-quality MBF maps in CT simulated phantom data as well as in a porcine model of coronary artery stenosis. In terms of precision, the methods ranked best-to-worst are: RPM1 > RPM2 > Fermi > SCM > LSVD > MUP [Formula: see text] other methods. In terms of bias, the models ranked best-to-worst are: SCM > RPM2 > RPM1 > PTU > LSVD [Formula: see text] other methods. Models with four or more parameters, particularly five-parameter models, had very poor precision (as much as 310% uncertainty) and/or significant bias (as much as 493%) and were sensitive to parameter initialization, thus suggesting the presence of multiple local minima. For improved estimates of MBF from MPI-CT, it is recommended to use reduced models that incorporate prior knowledge of physiology and contrast agent uptake, such as the proposed RPM1 and RPM2 models.

show abstract

Section: Experimental Methodsmentioning

confidence: 99%