Wireless Accelerometer for Neonatal MRI Motion Artifact Correction

A wireless accelerometer has been used in conjunction with a dedicated 3T neonatal MRI system installed on a Neonatal Intensive Care Unit to measure in-plane rotation which is a common problem with neonatal MRI. Rotational data has been acquired in real-time from phantoms simultaneously with MR images which shows that the wireless accelerometer can be used in close proximity to the MR system. No artifacts were observed on the MR images from the accelerometer or from the MR system on the accelerometer output. Initial attempts to correct the raw data using the measured rotational angles have been performed, but further work will be required to make a robust correction algorithm

Rigid‐body motion correction of the liver in image reconstruction for golden‐angle stack‐of‐stars DCE MRI

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/141403/1/mrm26782_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/141403/2/mrm26782.pd

Simple algorithm for the correction of MRI image artefacts due to random phase fluctuations

Grant support: This work was supported by EPSRC [grant numbers EP/E036775/1, EP/K020293/1] and received funding from the European Union's Horizon 2020 research and innovation programme [grant agreement No 668119, project “IDentIFY”]Peer reviewedPublisher PD

Development and characterization of methodology and technology for the alignment of fMRI time series

This dissertation has developed, implemented and tested a novel computer based system (AUTOALIGN) that incorporates an algorithm for the alignment of functional Magnetic Resonance Image (fMRI) time series. The algorithm assumes the human brain to be a rigid body and computes a head coordinate system on the basis of three reference points that lie on the directions correspondent to two of the eigenvectors of inertia of the volume, at the intersections with the head boundary. The eigenvectors are found weighting the inertia components with the voxel\u27s intensity values assumed as mass. The three reference points are found in the same position, relative to the origin of the head coordinate system, in both test and reference brain images. Intensity correction is performed at sub-voxel accuracy by tri-linear interpolation. A test fMR brain volume in which controlled simulations of rigid-body transformations have been introduced has preliminarily assessed system performance. Further experimentation has been conducted with real fMRI time series. Rigid-body transformations have been retrieved automatically and the values of the motion parameters compared to those obtained by the Statistical Parametric Mapping (SPM99), and the Automatic Image Registration (AIR 3.08). Results indicated that AUTOALIGN offers subvoxel accuracy in correcting both misalignment and intensity among time points in fMR images time series, and also that its performance is comparable to that of SPM99 and AIR3.08