Quantitative evaluation of noise reduction algorithms for very low dose renal CT perfusion imaging

Xin Liu; Andrew N. Primak; Lifeng Yu; Hua Li; James D. Krier; Lilach O. Lerman; Cynthia H. McCollough

doi:10.1117/12.813777

12 March 2009 Quantitative evaluation of noise reduction algorithms for very low dose renal CT perfusion imaging

Xin Liu, Andrew N. Primak, Lifeng Yu, Hua Li, James D. Krier, Lilach O. Lerman, Cynthia H. McCollough

Proceedings Volume 7258, Medical Imaging 2009: Physics of Medical Imaging; 72581T (2009) https://doi.org/10.1117/12.813777
Event: SPIE Medical Imaging, 2009, Lake Buena Vista (Orlando Area), Florida, United States

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.

Citation Download Citation

Xin Liu, Andrew N. Primak, Lifeng Yu, Hua Li, James D. Krier, Lilach O. Lerman, and Cynthia H. McCollough "Quantitative evaluation of noise reduction algorithms for very low dose renal CT perfusion imaging", Proc. SPIE 7258, Medical Imaging 2009: Physics of Medical Imaging, 72581T (12 March 2009); https://doi.org/10.1117/12.813777

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