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

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.

Adapting non-local means of de-noising in intraoperative magnetic resonance imaging for brain tumor surgery.

In image-guided brain tumor surgery, intraoperative magnetic resonance imaging (iMRI) is a powerful tool for updating navigational information after brain shift, controlling the resection of brain tumors, and evaluating intraoperative complications. Low-field iMRI scans occasionally generate a lot of noise, the reason for which is yet to be determined. This noise adversely affects the neurosurgeons' interpretations.

Development of quality control system for flat-panel detectors.

The characteristics of flat-panel detectors (FPD) are degraded by exposure to radiation. Degradation in a FPD progresses locally and has a nonlinear relationship to the radiation dose. In order to manage FPD systems properly, one must perform quality control (QC) such as evaluation of image degradation.

Skin shift and its effect on navigation accuracy in image-guided neurosurgery.

Neuronavigation systems have been developed for image-guided neurosurgery to aid in the accurate resection of malignant brain tumors. Therefore, the accuracy of the neuronavigation is important. However, many factors can reduce the navigation accuracy during surgery.