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Freddy Ståhlberg

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Tensor-valued diffusion encoding for diffusional variance decomposition (DIVIDE) : Technical feasibility in clinical MRI systems

Författare

  • Filip Szczepankiewicz
  • Jens Sjölund
  • Freddy Ståhlberg
  • Jimmy Lätt
  • Markus Nilsson

Summary, in English


Microstructure imaging techniques based on tensor-valued diffusion encoding have gained popularity within the MRI research community. Unlike conventional diffusion encoding—applied along a single direction in each shot—tensor-valued encoding employs diffusion encoding along multiple directions within a single preparation of the signal. The benefit is that such encoding may probe tissue features that are not accessible by conventional encoding. For example, diffusional variance decomposition (DIVIDE) takes advantage of tensor-valued encoding to probe microscopic diffusion anisotropy independent of orientation coherence. The drawback is that tensor-valued encoding generally requires gradient waveforms that are more demanding on hardware; it has therefore been used primarily in MRI systems with relatively high performance. The purpose of this work was to explore tensor-valued diffusion encoding on clinical MRI systems with varying performance to test its technical feasibility within the context of DIVIDE. We performed whole-brain imaging with linear and spherical b-tensor encoding at field strengths between 1.5 and 7 T, and at maximal gradient amplitudes between 45 and 80 mT/m. Asymmetric gradient waveforms were optimized numerically to yield b-values up to 2 ms/μm
2
. Technical feasibility was assessed in terms of the repeatability, SNR, and quality of DIVIDE parameter maps. Variable system performance resulted in echo times between 83 to 115 ms and total acquisition times of 6 to 9 minutes when using 80 signal samples and resolution 2×2×4 mm
3
. As expected, the repeatability, signal-to-noise ratio and parameter map quality depended on hardware performance. We conclude that tensor-valued encoding is feasible for a wide range of MRI systems—even at 1.5 T with maximal gradient waveform amplitudes of 33 mT/m—and baseline experimental design and quality parameters for all included configurations. This demonstrates that tissue features, beyond those accessible by conventional diffusion encoding, can be explored on a wide range of MRI systems.

Avdelning/ar

  • MR Physics
  • Multidimensional microstructure imaging
  • Medicinsk strålningsfysik, Lund
  • Diagnostisk radiologi, Lund
  • eSSENCE: The e-Science Collaboration
  • MultiPark: Multidisciplinary research focused on Parkinson´s disease
  • Neuroradiologi

Publiceringsår

2019

Språk

Engelska

Publikation/Tidskrift/Serie

PLoS ONE

Volym

14

Issue

3

Dokumenttyp

Artikel i tidskrift

Förlag

Public Library of Science (PLoS)

Ämne

  • Radiology, Nuclear Medicine and Medical Imaging
  • Other Physics Topics

Status

Published

Forskningsgrupp

  • MR Physics
  • Multidimensional microstructure imaging
  • Neuroradiology

ISBN/ISSN/Övrigt

  • ISSN: 1932-6203