Takashi Mizukuchi1, Masazumi Fujii, Yuichiro Hayashi, Masatoshi Tsuzaka. 1. Department of Radiological and Medical Laboratory Sciences, Graduate School of Medicine, Nagoya University, 1-1-20 Daiko-minami, Higashi-ku, Nagoya , 461-8673, Japan, mizukuchi.takashi@a.mbox.nagoya-u.ac.jp.
Abstract
PURPOSE: Intraoperative magnetic resonance imaging (iMRI) is a powerful tool that allows real-time image-guided excision of brain tumors. However, low magnetic field iMRI devices may produce low-quality images due to nonideal imaging conditions in the operating room and additional noise of unknown origin. The purpose of this study was to evaluate a three-dimensional unbiased nonlocal means filter for iMRI (UNLM-i) that we developed in order to enhance image quality and increase the diagnostic value of iMRI. METHODS: We first evaluated the effect of UNLM by assessing the modulation transfer function (MTF) and Weiner spectrum (WS) of UNLM in simulated imaging. We then tested the diagnostic value of UNLM-i de-noising by applying it to a series of randomly chosen iMR images that were assessed by 4 neurosurgeons and 4 radiological technologists using a 5-point rating scale to compare 13 parameters, including tumor visibility, edema, and sulci, before and after de-noising. RESULTS: Unbiased nonlocal means provided better MTF in comparison with other filters, and the WS for UNLM de-noising was reduced for all spatial frequencies. Postprocessing UNLM-i allowed de-noising with preserved edges and >twofold improvement in the signal-to-noise ratio without extending the MRI scanning time (p< 0.001) . The diagnostic value of UNLM-i de-noising was rated as "superior" or "better" in >80 % of cases in terms of contrast between white and gray matter and visibility of sulci, tumor, and edema (p< 0.001). CONCLUSIONS: Unbiased nonlocal means filter for iMRI de-noising proved very useful for image quality enhancement and assistance in the interpretation of iMR images.
PURPOSE: Intraoperative magnetic resonance imaging (iMRI) is a powerful tool that allows real-time image-guided excision of brain tumors. However, low magnetic field iMRI devices may produce low-quality images due to nonideal imaging conditions in the operating room and additional noise of unknown origin. The purpose of this study was to evaluate a three-dimensional unbiased nonlocal means filter for iMRI (UNLM-i) that we developed in order to enhance image quality and increase the diagnostic value of iMRI. METHODS: We first evaluated the effect of UNLM by assessing the modulation transfer function (MTF) and Weiner spectrum (WS) of UNLM in simulated imaging. We then tested the diagnostic value of UNLM-i de-noising by applying it to a series of randomly chosen iMR images that were assessed by 4 neurosurgeons and 4 radiological technologists using a 5-point rating scale to compare 13 parameters, including tumor visibility, edema, and sulci, before and after de-noising. RESULTS: Unbiased nonlocal means provided better MTF in comparison with other filters, and the WS for UNLM de-noising was reduced for all spatial frequencies. Postprocessing UNLM-i allowed de-noising with preserved edges and >twofold improvement in the signal-to-noise ratio without extending the MRI scanning time (p< 0.001) . The diagnostic value of UNLM-i de-noising was rated as "superior" or "better" in >80 % of cases in terms of contrast between white and gray matter and visibility of sulci, tumor, and edema (p< 0.001). CONCLUSIONS: Unbiased nonlocal means filter for iMRI de-noising proved very useful for image quality enhancement and assistance in the interpretation of iMR images.
Authors: Nicolas Wiest-Daesslé; Sylvain Prima; Pierrick Coupé; Sean Patrick Morrissey; Christian Barillot Journal: Med Image Comput Comput Assist Interv Date: 2008
Authors: José V Manjón; José Carbonell-Caballero; Juan J Lull; Gracián García-Martí; Luís Martí-Bonmatí; Montserrat Robles Journal: Med Image Anal Date: 2008-02-29 Impact factor: 8.545
Authors: Pieter L Kubben; Karlien J ter Meulen; Olaf E M G Schijns; Mariël P ter Laak-Poort; Jacobus J van Overbeeke; Henk van Santbrink Journal: Lancet Oncol Date: 2011-08-23 Impact factor: 41.316