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.













Similar content being viewed by others
References
Kubben PL, ter Meulen KJ, Schijns OEMG, ter Laak-Poort MP, van Overbeeke JJ, van Santbrink H (2011) Intraoperative MRI-guided resection of glioblastoma multiforme: a systematic review. Lancet Oncol 12:1062–1070
Foroglou N, Zamani A, Black P (2009) Intra-operative MRI (iop-MR) for brain tumor surgery. Br J Neurosurg 23:14–22
Knauth M, Wirtz CR, Tronnier VM, Aras N, Kunze S, Sartor K (1999) Intraoperative MR imaging increases the extent of tumor resection in patients with high-grade gliomas. Am J Neuroradiol 20:1642–1646
Shinohara C, Muragaki Y, Maruyama T et al (2008) Long-term prognostic assessment of 185 newly diagnosed gliomas–Grade III glioma showed prognosis comparable to that of grade II glioma. Jpn J Clin Oncol 38:730–733
Takashi M, Masatoshi M (2013) Evaluation and quality assurance of the image quality of an intraoperative magnetic resonance image. ECR, EPOS. doi:10.1594/ecr2013/C-1673
Buades A, Coll B, Morel M (2005) A review of image de-noising algorithms, with a new one. Multiscale Model Simul 4:490–530
Perona P, Malik J (1990) Scale-space and edge detection using anisotropic diffusion. IEEE Trans Pattern Anal Mach Intell 12:629–639
Grieg G, Kubler O, Kikinis R, Jolesz FA (1992) Nonlinear anisotropic filtering of MRI data. IEEE Trans Med Imaging 11:221–232
Manjón JV, Carbonell-Caballero J, Lull JJ, García-Martí G, Martí-Bonmatí L, Robles M (2008) MRI denoising using non-local means. Med Image Anal 12:514–523
Wiest-Daesslé N, Prima S, Coupé P, Morrissey SP, Barillot C (2008) Rician noise removal by non-local means filtering for low signal-to-noise ratio MRI: applications to DT-MRI. Med Image Comput Comput Assist Interv 11:171–179
Gudbjartsson H, Patz S (1995) The Rician distribution of noisy MRI data. Magn Reson Med 34:910–914
Sijbers J, Den Dekker AJ, Van Audekerke MV, Van Dyck D (1998) Estimation of the noise in magnitude MR images. Magn Reson Imaging 16:87–90
Coupé P, Yger P, Barillot (2006) Fast non local means denoising for 3D MR images. Med Image Comput Comput Assist Interv 9:33–40
Sijbers J, den Dekker AJ, Van Dyck D (1999) Parameter estimation from magnitude MR Images. Int J Imaging Syst Technol 10:109–114
Mahmoudi M, Sapiro G (2005) Fast image and video denoising via non-local means of similar neighborhoods. IMA Preprint Series, 2052
Cohen JA (1968) Nominal scale agreement with provision for scaled disagreement or partial credit. Psychol Bull 70:213–220
Conflict of interest
Takashi Mizukuchi, Masazumi Fujii, Yuichiro Hayashi and Masatoshi Tsuzaka declare that they have no conflict of interest.
Informed consent Informed consent was obtained from all observers who participated in the study. That of patients was not obtained, because we employed anonymized MR images as secondary use of clinical data.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Mizukuchi, T., Fujii, M., Hayashi, Y. et al. Usability of unbiased nonlocal means for de-noising intraoperative magnetic resonance images in neurosurgery. Int J CARS 9, 891–903 (2014). https://doi.org/10.1007/s11548-013-0972-x
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11548-013-0972-x