Low contrast detectability and spatial resolution with model-based Iterative reconstructions of MDCT images: a phantom and cadaveric study.
OBJECTIVE
To compare image quality [low contrast (LC) detectability, noise, contrast-to-noise (CNR) and spatial resolution (SR)] of MDCT images reconstructed with an iterative reconstruction (IR) algorithm and a filtered back projection (FBP) algorithm.


METHODS
The experimental study was performed on a 256-slice MDCT. LC detectability, noise, CNR and SR were measured on a Catphan phantom scanned with decreasing doses (48.8 down to 0.7 mGy) and parameters typical of a chest CT examination. Images were reconstructed with FBP and a model-based IR algorithm. Additionally, human chest cadavers were scanned and reconstructed using the same technical parameters. Images were analyzed to illustrate the phantom results.


RESULTS
LC detectability and noise were statistically significantly different between the techniques, supporting model-based IR algorithm (p < 0.0001). At low doses, the noise in FBP images only enabled SR measurements of high contrast objects. The superior CNR of model-based IR algorithm enabled lower dose measurements, which showed that SR was dose and contrast dependent. Cadaver images reconstructed with model-based IR illustrated that visibility and delineation of anatomical structure edges could be deteriorated at low doses.


CONCLUSIONS
Model-based IR improved LC detectability and enabled dose reduction. At low dose, SR became dose and contrast dependent.


CONCLUSIONS
• Model- based Iterative Reconstruction improves detectability of low contrast object. • With model- based Iterative Reconstruction, spatial resolution is dose and contrast dependent. • Model-based Iterative Reconstruction algorithms enable improved IQ combined with dose-reduction possibilities. • Improvement of SR and LC detectability on the same IMR data set would reduce reconstructions.
Department of Radiology and Medical Imaging, Cliniques Universitaires Saint Luc, Université Catholique de Louvain, Avenue Hippocrate 10, 1200, Brussels, Belgium. domitille.millon@uclouvain.be.
Domitille
D
Millon
Philips Healthcare, Rue des deux gares 80, 1070, Brussels, Belgium.
Alain
A
Vlassenbroek
Statistics Unit, King Albert II Cancer Institute, Université Catholique de Louvain, Avenue Hippocrate 10, 1200, Brussels, Belgium.
Aline G
AG
Van Maanen
Statistics Unit, King Albert II Cancer Institute, Université Catholique de Louvain, Avenue Hippocrate 10, 1200, Brussels, Belgium.
Samantha E
SE
Cambier
Department of Radiology and Medical Imaging, Cliniques Universitaires Saint Luc, Université Catholique de Louvain, Avenue Hippocrate 10, 1200, Brussels, Belgium.
Emmanuel E
EE
Coche
D000465:Algorithms; D002102:Cadaver; D006801:Humans; D061330:Multidetector Computed Tomography / Q000379:methods*; D019047:Phantoms, Imaging; D011829:Radiation Dosage; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D013902:Radiography, Thoracic / Q000379:methods*; D015203:Reproducibility of Results; D013909:Thorax / Q000000981:diagnostic imaging
Dose and Image Quality; Human cadavers; Model-Based Iterative Reconstruction; Multi Detector CT; Phantom