nexi 1.0

Neurite Exchange Imaging (NEXI) model estimator for diffusion MRI

nexi

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Table of Contents

• Installation
• License

Installation

pip install nexi

Prerequisites

Preprocessing

Before proceeding, make sure to preprocess your data with the following steps:

• Marchenko-Pastur principal component analysis (MP-PCA) denoising (Veraart et al., 2016). Recommended algorithm : dwidenoise from mrtrix
• Gibbs ringing correction (Kellner et al., 2016). Recommended algorithm : FSL implementation
• Distortion correction using FSL topup (Andersson et al., 2003, Andersson et al., 2016). Recommended algorithm : FSL topup
• Eddy current and motion correction (Andersson and Sotiropoulos, 2016). Recommended algorithm : FSL eddy

Additionally, you need to compute another noisemap using only the small b-values (b < 2 ms/µm²) and MP-PCA. This noisemap will be used to calculate the signal-to-noise ratio (SNR) of the data.

Furthermore, you can provide a mask of grey matter tissue if available. This mask can be used to restrict the processing to specific regions of interest. If a mask is not provided, the algorithms will be applied to the entire image, voxel by voxel, as long as there are no NaN values present.

To compute a grey matter mask, one common approach involves using a T1 image, FastSurfer, and performing registration to the diffusion (b = 0 ms/µm²) space. However, you can choose any other method to compute a grey matter mask.

Usage

Estimate NEXI parameters

estimate_nexi(dwi_path, bvals_path, td_path, lowb_noisemap_path, out_path)

License

nexi is distributed under the terms of the Apache License 2.0.