Evidence map›Paper›PMID 38725132›Full record

ArticleMagnetic resonance in medicine2024

Diffusion tensor brain imaging at 0.55T: A feasibility study.

Hao-Ting Kung, Sophia X Cui, Jonas T Kaplan, Anand A Joshi, Richard M Leahy, Krishna S Nayak, Justin P Haldar

Abstract read
PubMed Full text at PMC Publisher
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
–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

3 citing papers in PubMed.

  1. Magnetic resonance imaging.Science advances · 2026
    Review
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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

7 authors.

Hao-Ting KungSignal and Image Processing Institute, Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California, USA.ORCID 0009-0006-0941-7613
Sophia X CuiSiemens Medical Solutions USA, Los Angeles, California, USA.ORCID 0000-0002-5133-4903
Jonas T KaplanBrain and Creativity Institute, University of Southern California, Los Angeles, California, USA.
Anand A JoshiSignal and Image Processing Institute, Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California, USA.
Richard M LeahySignal and Image Processing Institute, Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California, USA.
Krishna S NayakSignal and Image Processing Institute, Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California, USA.ORCID 0000-0001-5735-3550
Justin P HaldarSignal and Image Processing Institute, Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California, USA.ORCID 0000-0002-1838-0211

Funding

Next generation in-vivo diffusion imaging at submillimeter resolutionR01MH116173 · NIMH · STANFORD UNIVERSITY · PI Yogesh Rathi, Kawin Setsompop · 2018 to 2026
$6.2M
Interactive software for semiautomated analysis of structural brain imagesR01NS074980 · NINDS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI LEAHY, RICHARD M, SHATTUCK, DAVID W · 2011 to 2021
$5.0M
Multicomponent Modeling of High-Dimensional Multiparametric MRI DataR56EB034349 · NIBIB · UNIVERSITY OF SOUTHERN CALIFORNIA · PI HALDAR, JUSTIN PRITAM · 2023 to 2023
$836k
Ming Hsieh Institute for Research on Engineering-Medicine for CancerNational Science Foundation CNS-1828736NIBIB NIH HHS R56 EB034349NIH HHS R01-MH116173NIH HHS R01-NS074980NIH HHS R56- EB034349NIMH NIH HHS R01 MH116173NINDS NIH HHS R01 NS074980USC Provost's Strategic Directions for Research AwardUSC Viterbi Graduate Fellowship
6 · The paper itself

Abstract

purposeTo investigate the feasibility of diffusion tensor brain imaging at 0.55T with comparisons against 3T.

methodsDiffusion tensor imaging data with 2 mm isotropic resolution was acquired on a cohort of five healthy subjects using both 0.55T and 3T scanners. The signal-to-noise ratio (SNR) of the 0.55T data was improved using a previous SNR-enhancing joint reconstruction method that jointly reconstructs the entire set of diffusion weighted images from k-space using shared-edge constraints. Quantitative diffusion tensor parameters were estimated and compared across field strengths. We also performed a test-retest assessment of repeatability at each field strength.

resultsAfter applying SNR-enhancing joint reconstruction, the diffusion tensor parameters obtained from 0.55T data were strongly correlated (

conclusionHigh-resolution in vivo diffusion MRI of the human brain is feasible at 0.55T when appropriate noise-mitigation strategies are applied.

Indexed as

BrainDiffusion Tensor ImagingFeasibility StudiesImage Processing, Computer-AssistedSignal-To-Noise RatioAdultAlgorithmsFemaleHealthy VolunteersHumansMaleReproducibility of Resultsconstrained reconstructiondenoisingdiffusion MRIlow‐field MRI

Identifiers

PMID38725132
PMCPMC12732710

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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.