Evidence map›Paper›PMID 36063492›Full record

ArticleMagnetic resonance in medicine2023

Multi-band multi-shot diffusion MRI reconstruction with joint usage of structured low-rank constraints and explicit phase mapping.

Erpeng Dai, Merry Mani, Jennifer A McNab

Open access · greenAbstract read
In one paragraph

Article in Magnetic resonance in medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
2.3field-weighted citation impact, top 12% 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

9 citing papers in PubMed, 17 citations in OpenAlex.

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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

3 authors at 2 institutions in 1 country.

Erpeng DaiDepartment of Radiology, Stanford University, Stanford, California, USA.ORCID 0000-0002-1032-5883
Merry ManiDepartment of Radiology, University of Iowa, Iowa City, Iowa, USA.
Jennifer A McNabDepartment of Radiology, Stanford University, Stanford, California, USA.
Stanford University · USUniversity of Iowa · US

Funding

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
NIBIB NIH HHS R01 EB031169
6 · The paper itself

Abstract

purposeTo develop a joint reconstruction method for multi-band multi-shot diffusion MRI. THEORY AND

methodsMulti-band multi-shot EPI acquisition is an effective approach for high-resolution diffusion MRI, but requires specific algorithms to correct the inter-shot phase variations. The phase correction can be done by first estimating the explicit phase map and then feeding it into the k-space signal formulation model. Alternatively, the phase information can be used indirectly as structured low-rank constraints in k-space. The 2 methods differ in reconstruction accuracy and efficiency. We aim to combine the 2 different approaches for improved image quality and reconstruction efficiency simultaneously, termed "joint usage of structured low-rank constraints and explicit phase mapping" (JULEP). The proposed JULEP reconstruction is tested on both single-band and multi-band, multi-shot diffusion data, with different resolutions and b values. The results of JULEP are compared with conventional methods with explicit phase mapping (i.e., multiplexed sensitivity-encoding [MUSE]) and structured low-rank constraints (i.e., MUSSELS), and another joint reconstruction method (i.e., network estimated artifacts for tempered reconstruction [NEATR]).

resultsJULEP improves the quality of the navigator and subsequently facilitates the reconstruction of final diffusion images. Compared with all 3 other methods (MUSE, MUSSELS, and NEATR), JULEP mitigates residual structural bias and improves temporal SNRs in the final diffusion image, particularly at high multi-band factors. Compared with MUSSELS, JULEP also improves computational efficiency.

conclusionThe proposed JULEP method significantly improves the image quality and reconstruction efficiency of multi-band multi-shot diffusion MRI, which can promote a broader application of high-resolution diffusion MRI.

Indexed as

AlprostadilBrainAlgorithmsArtifactsDiffusion Magnetic Resonance ImagingEcho-Planar ImagingImage Processing, Computer-AssistedAlprostadildiffusion MRIlow rankmulti-bandmulti-shotphase variation

Identifiers

PMID36063492
PMCPMC9887994
OpenAlexW4294647466

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.