Evidence map›Paper›PMID 42076848›Full record

ArticleMagnetic resonance in medicine2026

Simultaneous Acquisition of Magnetic Resonance Elastography (MRE) and Diffusion Tensor Imaging (DTI) Optimized for Human Brain.

Shujun Lin, Bradley P Sutton, Richard L Magin, Aaron T Anderson, Dieter Klatt

Abstract read
In one paragraph

Article in Magnetic resonance in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Shujun LinRichard and Loan Hill Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, Illinois, USA.
Bradley P SuttonBiomedical Imaging Center, Beckman Institute, University of Illinois Urbana Champaign, Urbana, Illinois, USA.ORCID https://orcid.org/0000-0002-8443-0408
Richard L MaginRichard and Loan Hill Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, Illinois, USA.
Aaron T AndersonBiomedical Imaging Center, Beckman Institute, University of Illinois Urbana Champaign, Urbana, Illinois, USA.ORCID https://orcid.org/0000-0003-0440-3416
Dieter KlattRichard and Loan Hill Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, Illinois, USA.

Funding

Adding MRE to DTI for freeR21EB026238 · NIBIB · UNIVERSITY OF ILLINOIS AT CHICAGO · PI KLATT, DIETER · 2018 to 2019
$479k
NIH HHS R21EB026238
6 · The paper itself

Abstract

purposeIncrease clinical acceptance of MRE and DTI by shortening the scan time by half and providing diffusive and mechanical property maps that are inherently co-registered. This proof-of-concept study evaluates the simultaneous DTI-MRE acquisition in the human brain and compares its results to conventional DTI and MRE measurements.

methodsWe optimized experimental parameters for in vivo human brain DTI-MRE acquisitions based on signal-to-noise ratio, encoding efficiency, and intravoxel phase dispersion mitigation. Five healthy subjects underwent scanning on a 3 T Siemens Prisma scanner, using two optimized parameter sets. Diffusive (mean diffusivity, fractional anisotropy) and mechanical (shear stiffness) property maps were computed and compared with conventional methods using Pearson's correlation analysis.

resultsDTI-MRE correlated with conventional DTI and MRE methods across all subjects. Mean diffusivity and fractional anisotropy maps showed excellent fidelity between methods (Pearson's coefficients of global mean values > 0.86, Pearson's coefficients of voxel-wise values > 0.7). Spatially averaged shear stiffness values were slightly lower in DTI-MRE.

conclusionThis proof-of-concept study confirms the feasibility of simultaneous DTI-MRE acquisition in the human brain, offering an efficient method for obtaining both diffusive and mechanical property maps. Further refinements in encoding strategies and inversion algorithms, such as the incorporation of a multi-frequency actuation approach in the DTI-MRE framework, could enhance accuracy, enabling broader clinical applications in neurology, neurosurgery, and brain injury assessment.

Indexed as

BrainDiffusion Tensor ImagingElasticity Imaging TechniquesImage Processing, Computer-AssistedAdultAlgorithmsAnisotropyFemaleHumansImage EnhancementImage Interpretation, Computer-AssistedMaleReproducibility of ResultsSensitivity and SpecificitySignal-To-Noise Ratiobrain microstructurediffusion tensor imagingDTI‐MREintravoxel phase dispersionmagnetic resonance elastographyshear stiffness

Identifiers

PMID42076848
PMCPMC13327488

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.