activity
20042021
most citedEddy current compensated double diffusion encoded (DDE) MRI

9 citations · 9 across the 2 of their papers we have counts for

collaborators

8 papers

physics.med-ph2021

NMR diffusion pore imaging: Experimental phase detection by double diffusion encoding

Kerstin Demberg, Frederik Bernd Laun, Johannes Windschuh +3

Diffusion pore imaging is an extension of diffusion-weighted nuclear magnetic resonance imaging enabling the direct measurement of the shape of arbitrarily formed, closed pores by…

physics.med-ph2021

Experimental determination of pore shapes using phase retrieval from q-space NMR diffraction

Kerstin Demberg, Frederik Bernd Laun, Marco Bertleff +2

This paper presents a novel approach on solving the phase problem in nuclear magnetic resonance (NMR) diffusion pore imaging, a method, which allows imaging the shape of arbitrary…

physics.med-ph2021

On the field strength dependence of bi- and triexponential intravoxel incoherent motion (IVIM) parameters in the liver

Andreas Julian Riexinger, Jan Martin, Susanne Rauh +8

Background: Studies on intravoxel incoherent motion (IVIM) imaging are carried out with different acquisition protocols. Purpose: Investigate the dependence of IVIM parameters on t…

physics.med-ph20219 cited

Eddy current compensated double diffusion encoded (DDE) MRI

Lars Mueller, Andreas Wetscherek, Tristan Anselm Kuder +1

Purpose: Eddy currents might lead to image distortions in diffusion weighted echo planar imaging. A method is proposed to reduce their effects on double diffusion encoding (DDE) MR…

physics.med-ph2020

Contrast-to-noise ratio analysis of microscopic diffusion anisotropy indices in q-space trajectory imaging

Jan Martin, Sebastian Endt, Andreas Wetscherek +5

Diffusion anisotropy in diffusion tensor imaging (DTI) is commonly quantified with normalized diffusion anisotropy indices (DAIs). Most often, the fractional anisotropy (FA) is use…

cs.CV2018

Domain Adaptation for Deviating Acquisition Protocols in CNN-based Lesion Classification on Diffusion-Weighted MR Images

Jennifer Kamphenkel, Paul F. Jaeger, Sebastian Bickelhaupt +8

End-to-end deep learning improves breast cancer classification on diffusion-weighted MR images (DWI) using a convolutional neural network (CNN) architecture. A limitation of CNN as…