DACO: Distortion/artefact correction for diffusion MRI data

In this paper, we propose a registration-based algorithm to co rrect various d istortions or a rtefacts (DACO) com-monly observed in diffusion-weighted (DW) magnetic resonance images (MRI). The registration in DACO is ac-complished by means of a pseudo bo image, which is synthesized from the anatomical images such as T1-weighted image or T2-weighted image, and a pseudo diffusion MRI (dMRI) data, which is derived from the Gaussian model of diffusion tensor imaging (DTI) or the Hermite model of mean apparent propagator (MAP)-MRI. DACO corrects (1) the susceptibility-induced distortions and (2) the misalignment between the dMRI data and anatomical images by registering the real bo image to the pseudo bo image, and corrects (3) the eddy current-induced distortions and (4) the head motions by registering each image in the real dMRI data to the corresponding image in the pseudo dMRI data. DACO estimates the models of artefacts simultaneously in an iterative and interleaved man-ner. The mathematical formulation of the models and the estimation procedures are detailed in this paper. Using the human connectome project (HCP) data the evaluation shows that DACO could estimate the model parameters accurately. Furthermore, the evaluation conducted on the real human data acquired from clinical MRI scanners reveals that the method could reduce the artefacts effectively. The DACO method leverages the anatomical im-age, which is routinely acquired in clinical practice, to correct the artefacts, omitting the additional acquisitions needed to conduct the algorithm. Therefore, our method should be beneficial to most dMRI data, particularly to those acquired without field maps or reverse phase-encoding images.

Automated summary

This paper proposes a registration-based algorithm to correct various distortions or artifacts commonly observed in diffusion-weighted magnetic resonance images. The algorithm uses anatomical images and a pseudo diffusion MRI data for registration, and estimates the models of artifacts simultaneously. The evaluation shows that the method accurately estimates model parameters and effectively reduces artifacts. This method is beneficial for most dMRI data, especially those acquired without additional field maps or reverse phase-encoding images.