Evidence map›Paper›PMID 40851334›Full record

ArticleMagnetic resonance in medicine2026

Advanced microstructure imaging at high b-values and high resolution combining ultra-high performance gradient diffusion imaging and model-based deep learning demonstrated using 3D multi-slab acquisition.

Chu-Yu Lee, Reza Ghorbani, Mahsa Rajabi, Merry Mani

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. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Chu-Yu LeeDepartment of Radiology, University of Iowa, Iowa City, Iowa, USA.
Reza GhorbaniDepartment of Electrical and Computer Engineering, University of Virginia, Charlottesville, USA.
Mahsa RajabiDepartment of Electrical and Computer Engineering, University of Virginia, Charlottesville, USA.
Merry ManiDepartment of Radiology and Biomedical Engineering, University of Virginia, Charlottesville, Virginia, USA.ORCID 0000-0001-8602-1109

Funding

University of Iowam - Whole Body 7T MRI ScannerS10RR028821 · NCRR · UNIVERSITY OF IOWA · PI MAGNOTTA, VINCENT A · 2010 to 2010
$8.0M
University of Iowa – 3T ESP MRI ScannerS10OD030220 · OD · UNIVERSITY OF IOWA · PI MAGNOTTA, VINCENT A · 2021 to 2021
$1.9M
University of Iowa - Research 3T MRI Connectome UpgradeS10OD025025 · OD · UNIVERSITY OF IOWA · PI MAGNOTTA, VINCENT A · 2018 to 2018
$1.9M
Fast Multi-dimensional Diffusion MRI with Sparse Sampling and Model-basedDeep Learning ReconstructionR01EB031169 · NIBIB · UNIVERSITY OF VIRGINIA · PI MANI, MERRY · 2021 to 2024
$1.7M
NCRR NIH HHS S10 RR028821NIBIB NIH HHS 1S10OD025025-01NIBIB NIH HHS 1S10OD030220-01NIBIB NIH HHS 1S10RR028821-01NIBIB NIH HHS EB031169-02S1NIBIB NIH HHS NIH R01 EB031169NIBIB NIH HHS R01 EB031169NIH HHS S10 OD025025NIH HHS S10 OD030220
6 · The paper itself

Abstract

purposeTo demonstrate the extended capabilities of 3D multi-slab diffusion-weighted acquisition (3D-msDWI) on high-performance gradients (HPG) to support advanced microstructure modeling for in-vivo human studies at high resolutions.

methodsDespite optimal SNR-efficiency, the application of 3D-msDWI has been limited by the long volume acquisition times (VAT) required for encoding the 3D k-space using multi-shot approaches. Substantial reduction of VAT is possible by employing optimized 3D k-space under-sampling methods. We demonstrate that with reduced VAT, 3D-msDWI can be successfully utilized for advanced brain microstructure modeling at high resolution. HPG systems (e.g.,

resultsThe accelerated 3D-msDWI framework enabled the generation of whole-brain parametric maps of a three-compartment model, at 1mm isotropic resolution, using a 3-shell, 66-direction acquisition completed in

conclusionThe inherent optimal SNR-efficiency of the 3D-msDWI framework can be harnessed for whole-brain high-resolution advanced microstructure modeling for in-vivo human studies, using advanced hardware and reconstruction.

Indexed as

BrainDeep LearningDiffusion Magnetic Resonance ImagingImaging, Three-DimensionalAlgorithmsHumansImage Processing, Computer-AssistedMaleSignal-To-Noise Ratio3D k‐space acceleration3D multi‐slab diffusion‐weighted imagingcompartment modelshigh‐performance gradientsMAGNUSmodel‐based deep learningmulti‐dimensional diffusion encodingplug‐and‐play priorsq‐space trajectory imaging (QTI)

Identifiers

PMID40851334
PMCPMC12620157

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Registered trials

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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.