FEAT/FIELDMAPS

# Making Fieldmap Images for FEAT |

## Introduction

This page outlines some of the most common ways to construct the required fieldmap images for B0 unwarping in FEAT. Also see the documentation on FUGUE and FEAT for more information on fieldmapping and its use in FEAT pre-stats processing.

### Fieldmap Data

Each scanner/site/sequence can give you different data. As the phase is of great importance in fieldmapping, but is normally not saved in other sequences, these images are often quite different from the standard images.

The first step in practice is to find out what kind of fieldmap acquisition data you can get from the scanner, operator or reconstruction software. There are three main types:

1. Complex data containing images at two different echo times
2. Separate phase images for the two different echo times
3. A single, real fieldmap image (showing the field inhomogeneity in each voxel)

If the images from the scanner are in ANALYZE or NIFTI form then you can determine their type, use `fslinfo`. If not, you must convert to either ANALYZE or NIFTI format before progressing. See the FSL FAQ for information about conversion.

When `fslinfo` is run it will display a datatype string or number (the value 32 represents a complex image). You can convert a complex image into its phase and magnitude components by using `fslcomplex` or, alternatively, leave it in this format. The number of images contained in each file is shown as the `dim4` value.

To further check what sort of images you have, use `slices` to display the image and then see which of the following images it most closely resembles. Note that for complex images, slices will display two separate images - one phase and one magnitude. If you only have a single image (dim4=1) then it is possible that it has already been reconstructed fully into a fieldmap.

Typical complex images

A phase image (wrapped)

A phase image (unwrapped and masked)

A magnitude image

A real fieldmap image (masked)

Note that the fieldmap image contains structure and shows substantial inhomogeneities mainly in the inferior regions of the brain.

It is most common to have wrapped phase and magnitude images (case 2 below). If the images appear to have unwrapped phase then the the PRELUDE stage (step 3) should be skipped. However, if the range of values within the image is within 2 pi radians (6.28) then it is likely that there are wraps present (even if they are hard to find by eye) and so, in this case, PRELUDE should still be run.

Once you have determined the type, you need to do some or all of the following steps. As a guide (and this may vary in some cases) the steps that are required are:

1. For complex data you need to do steps 1a, 2a, 3, 4a and 5
2. For a pair of phase images you need to do steps 1b, 1c, 2b, 3, 4a and 5
3. For a single, real fieldmap you need to do steps 1b, 1c, 4b and 5

## Processing Steps

Step 1 - Getting the magnitude image
(a) If you start with a complex Analyze or Nifti volume that contains the scans at two echo times then you need to do:

    fslcomplex -realabs complex_acq fieldmap_mag

(b) If you have separate phase images or a single, fieldmap image, then you need to also get a magnitude image that is (i) undistorted and (ii) registered with this phase/fieldmap image. Usually the sequence used to acquire the phase or fieldmap image also contains data that can give you this magnitude image. Check with your scanner operator, physicists and/or analysis people as to how to reconstruct this image - often it just requires extraction from the original DICOM or vendor-specific format.

(c) Check that the magnitude image and the phase/fieldmap images have the same resolution. You can do this by looking at the dim and pixdim entries (only the first three of each) as reported by `fslinfo`.

If they are not the same then they must be resampled to be equal. In this case choose the one with the best resolution and use this as a reference image in `flirt` with the `-applyxfm` option to resample the other images.
For example, if the magnitude image has a better resolution (smaller pixdims) then do the following:

    flirt -in original_phase0 -ref fieldmap_mag -applyxfm -out orig_phase0
    flirt -in original_phase1 -ref fieldmap_mag -applyxfm -out orig_phase1

Once this is done, check that the output images (e.g. orig_phase0) have the same dimensions and resolution as the reference (using `fslinfo`) and also check that they are aligned correctly by loading both the output and reference images into `fslview` and visually inspecting them.

Step 2 - Getting (wrapped) phase in radians
(a) If you have complex volumes then do:

    fslcomplex -realphase complex_acq phase0_rad 0 1
    fslcomplex -realphase complex_acq phase1_rad 1 1

These phase volumes will now be in radians.

(b) If you have seperate phase volumes that are in integer format then do:

    fslmaths orig_phase0 -mul 3.14159 -div 2048 phase0_rad -odt float
    fslmaths orig_phase1 -mul 3.14159 -div 2048 phase1_rad -odt float

Note that the value of 2048 needs to be adjusted for each different site/scanner/sequence in order to be correct. The final range of the phase0_rad image should be approximately 0 to 6.28. If this is not the case then this scaling is wrong.

If you have separate phase volumes are not in integer format, you must still check that the units are in radians, and if not scale them appropriately using `fslmaths`.

Step 3 - Unwrapping the phase images
Use PRELUDE to do the required phase unwrapping

    prelude -a fieldmap_mag -p phase0_rad -o phase0_unwrapped_rad
    prelude -a fieldmap_mag -p phase1_rad -o phase1_unwrapped_rad

Step 4 - Getting the fieldmap in rad/s
(a) For separate phase images do:

    fslmaths phase1_unwrapped_rad -sub phase0_unwrapped_rad -mul 1000 -div TE fieldmap_rads -odt float

where TE must be replaced with the appropriate difference in echo times (in units of milliseconds).

(b) If you have a single, real fieldmap then you must determine the units of this fieldmap (ask an operator/physicist) and rescale to radians per second if it is not already in these units. Common other units are (i) Hz (scale these by 6.28 to get rad/s) and (ii) Telsa (scale these by 2.68e8 to get rad/s).

Step 5 - Regularising the fieldmap
Fieldmaps can often be noisy or be contaminated around the edges of the brain. To correct for this you can regularise the fieldmap using `fugue`. Note that the "best" regularisation will depend on many factors in the acquisition and must be determined separately for each site/scanner/sequence. Look at the fieldmap (e.g. using `fslview`) to decide what is the best regularisation to use - which could also be to do no regularisation.

Some regularisation options are - Gaussian smoothing, despiking and median filtering. Examples of these (in order) are:

    fugue --loadfmap=fieldmap_rads -s 1 --savefmap=fieldmap_rads
    fugue --loadfmap=fieldmap_rads --despike --savefmap=fieldmap_rads
    fugue --loadfmap=fieldmap_rads -m --savefmap=fieldmap_rads

Any combination of these regularisations can be performed. See the FUGUE help and documentation for more information aspects of regularisation.

#### Mark Jenkinson
Copyright © 2006, University of Oxford