PURPOSE: Dual-energy computed tomography (DECT) images can undergo a two-material decomposition process which results in two images containing material density information. Material density images obtained by that process result in images with increased pixel noise. Noise reduction in those images is desirable in order to improve image quality. METHODS: A noise reduction algorithm for material density images was developed and tested. A three-level wavelet approach combined with the application of an anisotropic diffusion filter was used. During each level, the resulting noise maps are further processed, until the original resolution is reached and the final noise maps obtained. Our method works in image space and, therefore, can be applied to any type of material density images obtained from any DECT vendor. A quantitative evaluation of the noise-reduced images using the signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR) and 2D noise power spectrum was done to quantify the improvements. RESULTS: The noise reduction algorithm was applied to a set of images resulting in images with higher SNR and CNR than the raw density images obtained by the decomposition process. The average improvement in terms of SNR gain was about 49 % while CNR gain was about 52 %. The difference between the raw and filtered regions of interest mean values was far from reaching statistical significance (minimum [Formula: see text], average [Formula: see text]). CONCLUSION: We have demonstrated through a series of quantitative analyses that our novel noise reduction algorithm improves the image quality of DECT material density images.
PURPOSE: Dual-energy computed tomography (DECT) images can undergo a two-material decomposition process which results in two images containing material density information. Material density images obtained by that process result in images with increased pixel noise. Noise reduction in those images is desirable in order to improve image quality. METHODS: A noise reduction algorithm for material density images was developed and tested. A three-level wavelet approach combined with the application of an anisotropic diffusion filter was used. During each level, the resulting noise maps are further processed, until the original resolution is reached and the final noise maps obtained. Our method works in image space and, therefore, can be applied to any type of material density images obtained from any DECT vendor. A quantitative evaluation of the noise-reduced images using the signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR) and 2D noise power spectrum was done to quantify the improvements. RESULTS: The noise reduction algorithm was applied to a set of images resulting in images with higher SNR and CNR than the raw density images obtained by the decomposition process. The average improvement in terms of SNR gain was about 49 % while CNR gain was about 52 %. The difference between the raw and filtered regions of interest mean values was far from reaching statistical significance (minimum [Formula: see text], average [Formula: see text]). CONCLUSION: We have demonstrated through a series of quantitative analyses that our novel noise reduction algorithm improves the image quality of DECT material density images.
Authors: Guillaume Landry; Brigitte Reniers; Patrick Vincent Granton; Bart van Rooijen; Luc Beaulieu; Joachim E Wildberger; Frank Verhaegen Journal: Radiother Oncol Date: 2011-09-15 Impact factor: 6.280
Authors: Guillaume Landry; Patrick V Granton; Brigitte Reniers; Michel C Ollers; Luc Beaulieu; Joachim E Wildberger; Frank Verhaegen Journal: Phys Med Biol Date: 2011-09-06 Impact factor: 3.609
Authors: Rafael Simon Maia; Christian Jacob; Amy K Hara; Alvin C Silva; William Pavlicek; J Ross Mitchell Journal: Int J Comput Assist Radiol Surg Date: 2015-10-13 Impact factor: 2.924