Publication

3D Residual Eddy Current Field Characterisation: applied to Diffusion Weighted Magnetic Resonance Imaging

Rolf Gruetter, Alessandro Daducci, Gunnar Krüger, Kieran O'Brien, François Lazeyras
2013
Journal Articles

Abstract

Clinical use of the Stejskal-Tanner diffusion weighted images is hampered by the geometric distortions that result from the large residual 3D eddy current field induced. In this work we aimed to predict, using linear response theory, the residual 3D eddy current field required for geometric distortion correction based on phantom eddy current field measurements. The predicted 3D eddy current field induced by the diffusion-weighting gradients was able to reduce the root mean square error of the residual eddy current field to ~1Hz. The model’s performance was tested on diffusion weighted images of 4 normal volunteers, following distortion correction, the image quality of the Stejskal-Tanner diffusion-weighted images was found to have comparable image quality to image registration based corrections (FSL) at low b-values. Unlike registration techniques the correction was not hindered by low SNR at high b-values, and results in improved image quality relative to FSL. Characterization of the 3D eddy current field with linear response theory enables the prediction of the 3D eddy current field required to correct eddy current induced geometric distortions for a wide range of clinical and high b-value protocols.

Official source

https://infoscience.epfl.ch/entities/publication/cc93647e-5f30-46a0-851d-aec94aeaecbd

About this result

This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Rolf Gruetter, Alessandro Daducci, Gunnar Krüger, Kieran O'Brien, François Lazeyras
2013
Journal Articles

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/cc93647e-5f30-46a0-851d-aec94aeaecbd

About this result

Related concepts (32)

Diffusion MRI

Diffusion-weighted magnetic resonance imaging (DWI or DW-MRI) is the use of specific MRI sequences as well as software that generates images from the resulting data that uses the diffusion of water molecules to generate contrast in MR images. It allows the mapping of the diffusion process of molecules, mainly water, in biological tissues, in vivo and non-invasively. Molecular diffusion in tissues is not random, but reflects interactions with many obstacles, such as macromolecules, fibers, and membranes.

Eddy current

In electromagnetism, eddy currents (also called Foucault's currents) are loops of electric current induced within conductors by a changing magnetic field in the conductor according to Faraday's law of induction or by the relative motion of a conductor in a magnetic field. Eddy currents flow in closed loops within conductors, in planes perpendicular to the magnetic field. They can be induced within nearby stationary conductors by a time-varying magnetic field created by an AC electromagnet or transformer, for example, or by relative motion between a magnet and a nearby conductor.

Mean squared error

In statistics, the mean squared error (MSE) or mean squared deviation (MSD) of an estimator (of a procedure for estimating an unobserved quantity) measures the average of the squares of the errors—that is, the average squared difference between the estimated values and the actual value. MSE is a risk function, corresponding to the expected value of the squared error loss. The fact that MSE is almost always strictly positive (and not zero) is because of randomness or because the estimator does not account for information that could produce a more accurate estimate.

Related publications (28)

Partial discharge localization in power transformer tanks using machine learning methods

Farhad Rachidi-Haeri, Marcos Rubinstein, Hamidreza Karami

This paper presents a comparison of machine learning (ML) methods used for three-dimensional localization of partial discharges (PD) in a power transformer tank. The study examines ML and deep learning (DL) methods, ranging from support vector machines (SV ...

Nature Publishing Group2024

Spectral Estimators for High-Dimensional Matrix Inference

Farzad Pourkamali

A key challenge across many disciplines is to extract meaningful information from data which is often obscured by noise. These datasets are typically represented as large matrices. Given the current trend of ever-increasing data volumes, with datasets grow ...

EPFL2024

Intermediate complexity atmospheric modeling in complex terrain: is it right?

Michael Lehning, Dylan Stewart Reynolds, Michael Haugeneder

Dynamic downscaling of atmospheric forcing data to the hectometer resolution has shown increases in accuracy for landsurface models, but at great computational cost. Here we present a validation of a novel intermediate complexity atmospheric model, HICAR, ...

Frontiers Media Sa2024