Evidence map›Paper›PMID 38282253›Full record

ArticleMagnetic resonance in medicine2024

Model-based reconstruction for looping-star MRI.

Haowei Xiang, Jeffrey A Fessler, Douglas C Noll

Open access · hybridAbstract read
In one paragraph

Article in Magnetic resonance in medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.6field-weighted citation impact, top 18% of its field
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

3 citing papers in PubMed, 4 citations in OpenAlex.

  1. Article
  2. Article
  3. Spatiotemporal Maps for Dynamic MRI Reconstruction.IEEE transactions on computational imaging · 2026
    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

3 authors at 1 institution in 1 country.

Haowei XiangEECS, University of Michigan, Ann Arbor, Michigan, USA.ORCID 0000-0001-7598-9480
Jeffrey A FesslerEECS, University of Michigan, Ann Arbor, Michigan, USA.ORCID 0000-0001-9998-3315
Douglas C NollBiomedical Engineering, University of Michigan, Ann Arbor, Michigan, USA.ORCID 0000-0002-0983-3805
University of Michigan · US

Funding

Upgrade for MRI Instrument for Functional Brain ImagingS10OD026738 · OD · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI NOLL, DOUGLAS C · 2019 to 2019
$1.8M
Fast Functional MRI with Sparse Sampling and Model-Based ReconstructionR01EB023618 · NIBIB · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI FESSLER, JEFFREY A, NOLL, DOUGLAS C · 2017 to 2020
$1.5M
NIBIB NIH HHS R01 EB023618NIH HHS R01 EB023618NIH HHS S10 OD026738
6 · The paper itself

Abstract

purposeThe aim of this study was to develop a reconstruction method that more fully models the signals and reconstructs gradient echo (GRE) images without sacrificing the signal to noise ratio and spatial resolution, compared to conventional gridding and model-based image reconstruction method.

methodsBy modeling the trajectories for every spoke and simplifying the scenario to only echo-in and echo-out mixture, the approach explicitly models the overlapping echoes. After modeling the overlapping echoes with two system matrices, we use the conjugate gradient algorithm (CG-SENSE) with the nonuniform FFT (NUFFT) to optimize the image reconstruction cost function.

resultsThe proposed method is demonstrated in phantoms and in-vivo volunteer experiments for three-dimensional, high-resolution T2*-weighted imaging and functional MRI tasks. Compared to the gridding method, the high resolution protocol exhibits improved spatial resolution and reduced signal loss as a result of less intra-voxel dephasing. The fMRI task shows that the proposed model-based method produced images with reduced artifacts and blurring as well as more stable and prominent time courses.

conclusionThe proposed model-based reconstruction results shows improved spatial resolution and reduced artifacts. The fMRI task shows improved time series and activation map due to the reduced overlapping echoes and under-sampling artifacts.

Indexed as

AlgorithmsImage Processing, Computer-AssistedArtifactsHumansMagnetic Resonance ImagingPhantoms, Imagingmodel-based reconstructionsilent MRIZTE

Identifiers

PMID38282253
PMCPMC10950512
OpenAlexW4391320283

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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.