Functional Magnetic Resonance Imaging for Cranial Neuronavigation: Methods for Automated and Standardized Data Processing and Management

Abstract

Preoperative fMRI is one of the best established clinical fMRI applications. Due to the difficulties in recording and coregistration of functional image data, we present methods to standardize and automate these procedures. We used a self-made interactive software package (AFI - Automated Functional Imaging) to automate the time consuming and complex analysis of fMRI data. AFI controls the BrainVoyager program, a postprocessing software package, and furthermore facilitates data management, anonymization of patient data, storage, documentation, data export to neuronavigation systems and the opportunity of spatial transformation of image data for use in group studies. By the end of 2006 we have used this method on 123 patients with brain tumors and 47 patients with trigeminal neuralgia. The fundamental basis of multimodal neuronavigation is precise coregistration. EPI images contain spatial distortions of 5-15 mm. We were able to reduce the misregistration of EPI and FLASH images in a selectable region of interest to 1-2 mm. Furthermore AFI reduces the average evaluation time for a standard clinical fMRI study (four functional measurements, one anatomical data set) by approx. 50% from 140 minutes to about 70 minutes in comparison to manual evaluation by an expert. More importantly, the personal attendance time required for the evaluation decreases by 84% to 23 minutes as the remainder of the program runs automatically. In comparison to currently available online postprocessing software tools which are more limited in use, BrainVoyager can be used for coregistration, data export to neuronavigation systems and spatial transformation.

Special Considerations/Technical Limitations of Blood-Oxygen-Level-Dependent Functional Magnetic Resonance Imaging

“…Both these techniques require additional scan time and processing time, and, for this reason, they are not routinely applied in presurgical clinical functional imaging examinations. 29 For this reason, a manual coregistration approach has been suggested by Nennig and colleagues 30 in their proposed pipeline for standardized clinical fMRI data processing. This approach has been shown to provide reliable results but is still operator dependent and requires dedicated personnel.…”

ReStNeuMap: a tool for automatic extraction of resting-state functional MRI networks in neurosurgical practice

“…18,31,40 However, the setup of a clinical rs-fMRI analysis pipeline requires considering the following methodological and practical issues. 1) Multiple data QC parameters should be provided, including a) the determination of rs-fMRI volume outliers due to head motion, physiology, or hardware; 16 b) the quality of the coregistration between functional and structural MR images; 26 and c) the accuracy of the T1-weighted image segmentation, to help with a correct interpretation of the results. 25 2) For rs-fMRI network extraction, it is preferable to use an unsupervised technique, such as independent component analysis (ICA), to take into account the anatomical variability in localization of functional areas across patients.…”