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Proceedings Paper

Quantitative evaluation of noise reduction algorithms for very low dose renal CT perfusion imaging
Author(s): Xin Liu; Andrew N. Primak; Lifeng Yu; Hua Li; James D. Krier; Lilach O. Lerman; Cynthia H. McCollough
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Paper Abstract

In this paper, we demonstrate a methodology for quantitative evaluation of noise reduction algorithms for very low-dose (1/10th typical dose) renal CT perfusion imaging. Three types of noise reduction algorithms are evaluated, including the commonly used low pass filtering, edge-preserving algorithms, and spatial-temporal filtering algorithms, such as recently introduced local highly constrained back projection (HYPR-LR) technique and multi-band filtering (MBF). The performance of these noise reduction methods was evaluated in terms of background signal-to-noise ratio (SNR), spatial resolution, fidelity of the time-attenuation curves of renal cortex, and computational speed. The spatial resolution was quantified by an on-scene modulation transfer function (MTF) measurement method. The fidelity of time-attenuation curves was quantified by statistical analysis using a Chi-square test. The results indicate that algorithms employing spatial-temporal correlations of images, such as HYPR and MBF, can achieve spatial resolution similar to the images acquired using routine dose levels. Edge-preserving algorithms, such as anisotropic diffusion and bilateral filtering, also show good performance in terms of background SNR and spatial resolution, but they are rather slow compared to HYPR and MBF. However, edge-preserving algorithms can be applied in the situations where images do not have strong spatial-temporal correlation. Finally, all the noise reduction algorithms show a high fidelity of the time-attenuation curves, which can be explained by a strong iodine attenuation signal in the highly perfused kidney.

Paper Details

Date Published: 12 March 2009
PDF: 8 pages
Proc. SPIE 7258, Medical Imaging 2009: Physics of Medical Imaging, 72581T (12 March 2009); doi: 10.1117/12.813777
Show Author Affiliations
Xin Liu, Mayo Clinic (United States)
Andrew N. Primak, Mayo Clinic (United States)
Lifeng Yu, Mayo Clinic (United States)
Hua Li, Mayo Clinic (United States)
James D. Krier, Mayo Clinic (United States)
Lilach O. Lerman, Mayo Clinic (United States)
Cynthia H. McCollough, Mayo Clinic (United States)

Published in SPIE Proceedings Vol. 7258:
Medical Imaging 2009: Physics of Medical Imaging
Ehsan Samei; Jiang Hsieh, Editor(s)

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