학술논문

Noise-reducing algorithms do not necessarily provide superior dose optimisation for hepatic lesion detection with multidetector CT.
Document Type
Article
Source
British Journal of Radiology. Mar2013, Vol. 86 Issue 1023, p1-8. 8p.
Subject
*ALGORITHMS
*MULTIDETECTOR computed tomography
*MATHEMATICAL optimization
*LIVER disease diagnosis
*JACKKNIFE (Statistics)
Language
ISSN
0007-1285
Abstract
Objective: To compare the dose-optimisation potential of a smoothing filtered backprojection (FBP) and a hybrid FBP/iterative algorithm to that of a standard FBP algorithm at three slice thicknesses for hepatic lesion detection with multidetector CT. Methods: A liver phantom containing a 9.5-mm opacity with a density of 10 HU below background was scanned at 125, 100, 75, 50 and 25 mAs. Data were reconstructed with standard FBP (B), smoothing FBP (A) and hybrid FBP/iterative (iDose4) algorithms at 5-, 3- and 1-mm collimation. 10 observers marked opacities using a four-point confidence scale. Jackknife alternative free-response receiver operating characteristic figure of merit (FOM), sensitivity and noise were calculated. Results: Compared with the 125-mAs/5-mm setting for each algorithm, significant reductions in FOM (p<0.05) and sensitivity (p<0.05) were found for all three algorithms for all exposures at 1-mm thickness and for all slice thicknesses at 25 mAs, with the exception of the 25-mAs/5-mm setting for the B algorithm. Sensitivity was also significantly reduced for all exposures at 3-mm thickness for the A algorithm (p<0.05). Noise for the A and iDose4 algorithms was approximately 13% and 21% lower, respectively, than for the B algorithm. Conclusion: Superior performance for hepatic lesion detection was not shown with either a smoothing FBP algorithm or a hybrid FBP/iterative algorithm compared with a standard FBP technique, even though noise reduction with thinner slices was demonstrated with the alternative approaches. Advances in knowledge: Reductions in image not necessarily translate to an improvement noise with non-standard CT algorithms do in low-contrast object detection. [ABSTRACT FROM AUTHOR]