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Combine multi-echoes from a multi-echo fMRI acquisition.

Project description

Multi-echo combinations

PyPI version PyPI - Python Version

This repository provides a command line tool to combine multiple echoes from a multi-echo BOLD fMRI acquisition. It currently provides three different echo avering algorithms:

algorithm description
1. average Echoes are weighted equally
2. PAID Echoes are weighted by their CNR, i.e. by their TE*tSNR contributions
3. TE Echoes are weighted by their TEs

For more information on multiecho acquisition and combination schemes, please refer to (for example):

  • Poser et al. (2006). BOLD Contrast Sensitivity Enhancement and Artifact Reduction with Multiecho EPI: Parallel-Acquired Inhomogeneity- Desensitized fMRI. Magn. Reson. Med. 55:6, pp. 1227–35.
  • Posse, Stefan (2012). Multi-Echo Acquisition. NeuroImage 62:2, pp. 665–671.

Multiecho is developed at the Donders Institute of the Radboud University.


To install, simply run:

pip install multiecho

This will give you the latest stable release of the software. To get the very latest version of the software you can install the package directly from the github source code repository:

pip install git+

Alternatively, to get the latest (possibly unreleased) code, clone this repository and run the following on the root folder of the repository:

pip install .

The tool only supports Python 3.6+.


Once installed, a command line tool called mecombine will be available in your PATH. Detailed usage information can be found by running mecombine -h:

usage: [-h] [-o OUTPUTNAME] [-a {PAID,TE,average}]
                      [-w [WEIGHTS [WEIGHTS ...]]] [-s] [-v VOLUMES]

Combine multi-echo echoes.

Tools to combine multiple echoes from an fMRI acquisition.
It expects input files saved as NIfTIs, preferably organised
according to the BIDS standard.

Currently three different combination algorithms are supported, implementing
the following weighting schemes:

1. PAID => TE * SNR
2. TE => TE
3. Simple Average => 1

positional arguments:
  pattern               Globlike search pattern with path to select the echo
                        images that need to be combined. Because of the
                        search, be sure to check that not too many files are
                        being read

optional arguments:
  -h, --help            show this help message and exit
  -o OUTPUTNAME, --outputname OUTPUTNAME
                        File output name. If not a fullpath name, then the
                        output will be stored in the same folder as the input.
                        If empty, the output filename will be the filename of
                        the first echo appended with a '_combined' suffix
                        (default: )
  -a {PAID,TE,average}, --algorithm {PAID,TE,average}
                        Combination algorithm. Default: TE (default: TE)
  -w [WEIGHTS [WEIGHTS ...]], --weights [WEIGHTS [WEIGHTS ...]]
                        Weights (e.g. = echo times) for all echoes (default:
  -s, --saveweights     If passed and algorithm is PAID, save weights
                        (default: False)
  -v VOLUMES, --volumes VOLUMES
                        Number of volumes that is used to compute the weights
                        if algorithm is PAID (default: 100)

  mecombine '/project/number/bids/sub-001/func/*_task-motor_*echo-*.nii.gz'
  mecombine '/project/number/bids/sub-001/func/*_task-rest_*echo-*.nii.gz' -a PAID
  mecombine '/project/number/bids/sub-001/func/*_acq-MBME_*run-01*.nii.gz' -w 11 22 33 -o sub-001_task-stroop_acq-mecombined_run-01_bold.nii.gz


Currently inneficient as we load all datasets into memory. We could iterate through the volumes and only keep the final combined series in memory at any given time.

You may receive a runtime warning when combining echoes with PAID. If your datasets have voxels with zeros, e.g., if they were masked, a division by 0 will lead to infinite weights. You may safely ignore the warning, but do check your data after the combination.

By default PAID will compute the weights based on the last 100 volumes of the acquisition. Whether this is optimal or not is up to discussion. If you are testing out the combination on a small subset of volumes, say 5 or so, then the weights won't be stable and your image may look noisy.

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