Evidence map›Paper›PMID 35917693›Full record

ArticleMedical image analysis2022

Rapid Diffusion Magnetic Resonance Imaging Using Slice-Interleaved Encoding.

Tiantian Xu, Ye Wu, Yoonmi Hong, Sahar Ahmad, Khoi Minh Huynh, Zhixing Wang, Weili Lin, Wei-Tang Chang, Pew-Thian Yap

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Medical image analysis, 2022. 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

9 authors.

Tiantian XuDepartment of Computer Science, University of North Carolina, Chapel Hill, NC 27599, USA.
Ye WuDepartment of Radiology, University of North Carolina, Chapel Hill, NC 27599, USA; Biomedical Research Imaging Center (BRIC), University of North Carolina, Chapel Hill, NC 27599, USA.
Yoonmi HongDepartment of Radiology, University of North Carolina, Chapel Hill, NC 27599, USA; Biomedical Research Imaging Center (BRIC), University of North Carolina, Chapel Hill, NC 27599, USA.
Sahar AhmadDepartment of Radiology, University of North Carolina, Chapel Hill, NC 27599, USA; Biomedical Research Imaging Center (BRIC), University of North Carolina, Chapel Hill, NC 27599, USA.
Khoi Minh HuynhDepartment of Biomedical Engineering, University of North Carolina, Chapel Hill, NC 27599, USA.
Zhixing WangDepartment of Biomedical Engineering, University of Virginia, Charlottesville, VA 22904, USA.
Weili LinDepartment of Radiology, University of North Carolina, Chapel Hill, NC 27599, USA; Biomedical Research Imaging Center (BRIC), University of North Carolina, Chapel Hill, NC 27599, USA.
Wei-Tang ChangDepartment of Radiology, University of North Carolina, Chapel Hill, NC 27599, USA; Biomedical Research Imaging Center (BRIC), University of North Carolina, Chapel Hill, NC 27599, USA.
Pew-Thian YapDepartment of Computer Science, University of North Carolina, Chapel Hill, NC 27599, USA; Department of Biomedical Engineering, University of North Carolina, Chapel Hill, NC 27599, USA; Department of Radiology, University of North Carolina, Chapel Hill, NC 27599, USA; Biomedical Research Imaging Center (BRIC), University of North Carolina, Chapel Hill, NC 27599, USA. Electronic address: ptyap@med.unc.edu.

Funding

Development of Robust Brain Measurement Tools Informed by Ultrahigh Field 7T MRIR01EB006733 · NIBIB · UNIV OF NORTH CAROLINA CHAPEL HILL · PI YAP, PEW-THIAN · 2008 to 2020
$4.5M
Computational Diffusion MRI for Studying Early Human Brain DevelopmentR01MH125479 · NIMH · UNIV OF NORTH CAROLINA CHAPEL HILL · PI YAP, PEW-THIAN · 2021 to 2025
$2.1M
Comprehensive MR Fingerprinting for Infants and Young Children at Risk for Developmental Delays.R01HD112923 · NICHD · DUKE UNIVERSITY · PI Dan Ma, DEANNE E WILSON-COSTELLO · 2024 to 2026
$1.7M
NIBIB NIH HHS R01 EB006733NICHD NIH HHS R01 HD112923NIMH NIH HHS R01 MH125479
6 · The paper itself

Abstract

In this paper, we present a robust reconstruction scheme for diffusion MRI (dMRI) data acquired using slice-interleaved diffusion encoding (SIDE). When combined with SIDE undersampling and simultaneous multi-slice (SMS) imaging, our reconstruction strategy is capable of significantly reducing the amount of data that needs to be acquired, enabling high-speed diffusion imaging for pediatric, elderly, and claustrophobic individuals. In contrast to the conventional approach of acquiring a full diffusion-weighted (DW) volume per diffusion wavevector, SIDE acquires in each repetition time (TR) a volume that consists of interleaved slice groups, each group corresponding to a different diffusion wavevector. This strategy allows SIDE to rapidly acquire data covering a large number of wavevectors within a short period of time. The proposed reconstruction method uses a diffusion spectrum model and multi-dimensional total variation to recover full DW images from DW volumes that are slice-undersampled due to unacquired SIDE volumes. We formulate an inverse problem that can be solved efficiently using the alternating direction method of multipliers (ADMM). Experiment results demonstrate that DW images can be reconstructed with high fidelity even when the acquisition is accelerated by 25 folds.

Indexed as

BrainDiffusion Magnetic Resonance ImagingAgedChildHumansImage Processing, Computer-AssistedMagnetic Resonance ImagingDiffusion MRIFast MRISlice-Interleaved Diffusion Encoding (SIDE)

Identifiers

PMID35917693
PMCPMC9988327

What OpenQuestion holds

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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.