MEF refers to the multi-echo FLASH sequence.

MEF provides several advantages over simple FLASH sequence or even the MPRAGE sequence, in that it has less distortion, higher contrast-to-noise ratio for subcortical structures, and it allows the direct estimation of underlying tissue parameters. One disadvantage is that the large dimensionality of the data (multiple-volumes for the same subject) makes it harder for the data analysis.

FreeSurfer is not optimized for the processing of MEF data yet, and this page describes some possible processing schemes.

Scheme #1 (synthesize a single image and then apply the standard FreeSurfer pipeline):

Like in typically FreeSurfer processing stream where multiple scans (of the same contrast property) are averaged after motion correction to generate a single volume for later processing, the multiple volumes from the MEF sequence can also be combined to get one volume of good quality.

Due to the difference in image contrast of images acquired at different flip angle or echo time settings, a simple average would fail. Weighted average or nonlinear combination is thus necessary. FreeSurfer has tools for both approaches.

The weighted linear averaging approach is described in our thickness validation paper. The idea is to apply the Linear Discriminant Analysis (LDA) technique in order to find a optimal set of weights (a projection vector) such that the weighted averging volume has the best contrast-to-noise ratio between a pair of tissue classes (usually between WM and GM).

The program "mri_ms_LDA" can be used to compute this weighting, and the usage is:

• mri_ms_LDA -lda 2 3 -label manual_label.mgz -weight weights.txt input_vol1 input_vol2 ... input_volN

where "-lda 2 3" indicates optimizing for the two tissue classes with label 2 and 3. "manual_label.mgz" is the manual labeling volume (maybe partially labelled) that should contain voxels with values 2 or 3.