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Richard Ansorge

Publications and source records attributed to Richard Ansorge.

2 recordsLinked to original sources

An evaluation of the use of passive shimming to improve frontal sensitivity in fMRI.

The presence of the head in an MRI scanner leads to inhomogeneities in the magnetic field. These cause the 'susceptibility artifacts' of image distortion and signal dropout. In this paper, we evaluate a technique called passive shimming, which has the potential to reduce field inhomogeneities and the resultant artifacts. A piece of a magnetically active material (pyrolytic graphite) is held on the roof of the participant's mouth by a plastic mouth mould. We evaluate the effects in several different ways. We show that the presence of a shim reduces field inhomogeneity across much of the brain. From field maps, we generate simulations of EPI image intensity and BOLD sensitivity. Both of these are mainly improved by the presence of shim, although there were small reductions in some regions. Measured EPI image intensity also mostly increased. Finally, we ran a reward-punishment task in our subjects, and found that the presence of a shim increased functional sensitivity in the orbitofrontal cortex. Using the BOLD sensitivity measure, we provide estimates of the improvement to be expected in functional studies for a range of neural structures. Passive shims are quick to make and reasonably comfortable to wear, and have substantial potential for researchers investigating inferior frontal brain regions using MRI.

Artifacts↗

Inference of multiple fiber orientations in high angular resolution diffusion imaging.

A method is presented that is capable of determining more than one fiber orientation within a single voxel in high angular resolution diffusion imaging (HARDI) data sets. This method is an extension of the Markov chain method recently introduced to diffusion tensor imaging (DTI) analysis, allowing the probability density function of up to 2 intra-voxel fiber orientations to be inferred. The multiple fiber architecture within a voxel is then assessed by calculating the relative probabilities of a 1 and 2 fiber model. It is demonstrated that for realistic signal to noise ratios, it is possible to accurately characterize the directions of 2 intersecting fibers using a 2 fiber model. The shortcomings of under-fitting a 2 fiber model, or over-fitting a 1 fiber model, are explored. This new algorithm enhances the tools available for fiber tracking.

Algorithms↗