Cookies on this website

We use cookies to ensure that we give you the best experience on our website. If you click 'Accept all cookies' we'll assume that you are happy to receive all cookies and you won't see this message again. If you click 'Reject all non-essential cookies' only necessary cookies providing core functionality such as security, network management, and accessibility will be enabled. Click 'Find out more' for information on how to change your cookie settings.

Motion during diffusion-weighted imaging (DWI) introduces phase errors that can cause significant artifacts in brain images. One method of correcting these errors uses additional navigator data to measure the phase corruptions. Standard navigator methods correct for rigid-body motion but cannot correct for nonrigid deformations of the brain related to the cardiac cycle. This work derives a generalized reconstruction that corrects for nonrigid motion based on a least-squares formulation. Since this reconstruction has the disadvantage of being computationally expensive, an approximation is presented, called a refocusing reconstruction. The refocusing reconstruction is both efficient and straightforward. Each readout is multiplied in image space by the phase conjugate of the navigator image, and these rephased readouts are then summed. The conditions under which the refocusing reconstruction is sufficient are considered and methods to improve the quality of refocused images are discussed. In particular, synchronization of the acquisition to the cardiac cycle can provide data that is well-conditioned to the refocusing reconstruction without incurring the large time penalty traditionally associated with cardiac gating. These methods are applied to steady-state DWI, a promising pulse sequence that is particularly sensitive to motion-induced phase artifacts. The refocusing reconstruction is shown to significantly improve SS-DWI over standard rigid-body corrections.

Original publication




Journal article


Magn Reson Med

Publication Date





343 - 353


Artifacts, Brain, Diffusion Magnetic Resonance Imaging, Humans, Image Processing, Computer-Assisted, Motion