nifreeze 0.1.0

Model-based estimation and correction of head motion and eddy current distortions in 4D neuroimaging.

NiFreeze is a flexible framework for volume-to-volume motion estimation and correction in d/fMRI and PET, and eddy-current-derived distortion estimation in dMRI.

Diffusion and functional MRI (d/fMRI) generally employ echo-planar imaging (EPI) for fast whole-brain acquisition. Despite the rapid collection of volumes, typical repetition times are long enough for head motion to occur, which has been proven detrimental to both diffusion [1] and functional [2] MRI. In the case of dMRI, additional volume-wise, low-order spatial distortions are caused by eddy currents (EC), which appear as a result of quickly switching diffusion gradients. Unaccounted for EC distortion can result in incorrect local model fitting and poor downstream tractography results [3], [4]. FSL’s eddy [5] is the most popular tool for EC distortion correction, and implements a leave-one-volume-out approach to estimate EC distortions. However, FSL has commercial restrictions that hinder application within open-source initiatives such as NiPreps [6]. In addition, FSL’s development model discourages the implementation of alternative data-modeling approaches to broaden the scope of application (e.g., modalities beyond dMRI). NiFreeze is an open-source implementation of eddy’s approach to estimate artifacts that permits alternative models that apply to, for instance, head motion estimation in fMRI and positron-emission tomography (PET) data.