Usability of unbiased nonlocal means for de-noising intraoperative magnetic resonance images in neurosurgery

Abstract

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. 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. 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). Unbiased nonlocal means filter for iMRI de-noising proved very useful for image quality enhancement and assistance in the interpretation of iMR images.