Renal cyst pseudoenhancement: influence of multidetector CT reconstruction algorithm and scanner type in phantom model.
OBJECTIVE
To prospectively determine the dependence of renal cyst pseudoenhancement on multidetector computed tomographic (CT) scanner type and convolution kernel in a phantom model.


METHODS
A customized anthropomorphic phantom was created to accept interchangeable 40-, 140-, and 240-HU renal inserts that contained stacked 0- and 50-HU cylindric cysts measuring 7, 10, and 15 mm in diameter. Each phantom and insert was scanned with five different multidetector CT scanners on five separate occasions by using 120 kVp, low and high tube current settings, 3.00-3.75-mm collimation, and standard and high-spatial-resolution kernels. A total of 2340 CT attenuation measurements were obtained by using standardized regions of interest. The effect of multidetector CT imaging regimen, tube current, cyst diameter, and renal attenuation on pseudoenhancement incidence was assessed by using generalized estimating equations based on a binary logistic regression model. Within this framework, a Bonferroni multiple comparison correction was used to assess pseudoenhancement frequency differences among imaging regimens.


RESULTS
Pseudoenhancement occurred in both 0- and 50-HU cysts; was significantly correlated with multidetector CT imaging regimen (P<.0001), cyst diameter (P<.0001), and renal attenuation (P<or=.032); and was independent of tube current (P>.3). When convolution kernels on specific scanners were compared, significant differences (P<.04) between kernels were identified with all five scanners in terms of observed pseudoenhancement incidence. Generational differences in equipment were noted, with pseudoenhancement incidence ranging from 1.7% to 8.3%, 1.7% to 16.7%, and 18.3% to 56.7% across relevant kernels for three scanners from one manufacturer.


CONCLUSIONS
Pseudoenhancement is strongly dependent on multidetector CT convolution kernel. Varying this parameter may mitigate this phenomenon, which is independent of volume-averaging effects.
Birnbaum
Bernard A
BA
Department of Radiology, New York University Medical Center, 560 First Ave, New York, NY 10016, USA. bernard.birnbaum@nyumc.org
Hindman
Nicole
N
Lee
Julie
J
Babb
James S
JS
D000465:Algorithms; D006801:Humans; D052177:Kidney Diseases, Cystic / Q000000981:diagnostic imaging*; D016015:Logistic Models; D019047:Phantoms, Imaging; D011446:Prospective Studies; D011856:Radiographic Image Enhancement; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed