Evidence map›Paper›PMID 27332141›Full record

ArticleNMR in biomedicine2017

Single-step quantitative susceptibility mapping with variational penalties.

Itthi Chatnuntawech, Patrick McDaniel, Stephen F Cauley, Borjan A Gagoski, Christian Langkammer, Adrian Martin, P Ellen Grant, Lawrence L Wald, Kawin Setsompop, Elfar Adalsteinsson and 1 more

Abstract readEvaluation Study
In one paragraph

Article in NMR in biomedicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing 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

23 citing papers in PubMed.

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  16. Improvement of Signal Inhomogeneity Induced by Radio-frequency Transmit-related Phase Error for Single-step Quantitative Susceptibility Mapping Reconstruction.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2019
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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

11 authors.

Itthi ChatnuntawechDepartment of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA.
Patrick McDanielDepartment of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA.
Stephen F CauleyA. A. Martinos Center for Biomedical Imaging, Department of Radiology; Massachusetts General Hospital, Charlestown, MA, USA.
Borjan A GagoskiHarvard Medical School, Boston, MA, USA.
Christian LangkammerDepartment of Neurology, Medical University of Graz, Graz, Austria.
Adrian MartinApplied Mathematics, Universidad Rey Juan Carlos, Mostoles, Madrid, Spain.
P Ellen GrantHarvard Medical School, Boston, MA, USA.
Lawrence L WaldA. A. Martinos Center for Biomedical Imaging, Department of Radiology; Massachusetts General Hospital, Charlestown, MA, USA.
Kawin SetsompopA. A. Martinos Center for Biomedical Imaging, Department of Radiology; Massachusetts General Hospital, Charlestown, MA, USA.
Elfar AdalsteinssonDepartment of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA.
Berkin BilgicA. A. Martinos Center for Biomedical Imaging, Department of Radiology; Massachusetts General Hospital, Charlestown, MA, USA.

Funding

The Human Connectome Project (HCP)U01MH093765 · NIMH · MASSACHUSETTS GENERAL HOSPITAL · PI ROSEN, BRUCE R · 2010 to 2014
$12.1M
Project 4P41EB015896 · NIBIB · MASSACHUSETTS GENERAL HOSPITAL · PI ROSEN, BRUCE R · 2012 to 2018
$9.8M
Advanced Fetal ImagingR01EB017337 · NIBIB · BOSTON CHILDREN'S HOSPITAL · PI ADALSTEINSSON, ELFAR, GRANT, PATRICIA ELLEN · 2014 to 2018
$6.2M
Mouse Neurodevelopmental Behavior CoreU54HD090255 · NICHD · BOSTON CHILDREN'S HOSPITAL · PI SAHIN, MUSTAFA · 2016 to 2020
$5.2M
Rapid MRI acquisition for pediatric low-grade gliomasR01EB020613 · NIBIB · STANFORD UNIVERSITY · PI SETSOMPOP, KAWIN · 2016 to 2019
$2.5M
Magnetic Resonance Fingerprinting (MRF) for Improved High Field MRR01EB017219 · NIBIB · CASE WESTERN RESERVE UNIVERSITY · PI GRISWOLD, MARK · 2013 to 2016
$2.5M
MRI Corticography (MRCoG): Micro-scale Human Cortical ImagingR24MH106096 · NIMH · UNIVERSITY OF CALIFORNIA BERKELEY · PI FEINBERG, DAVID ALAN, LIU, CHUNLEI · 2014 to 2016
$1.4M
NIBIB NIH HHS P41 EB015896NIBIB NIH HHS R01 EB017219NIBIB NIH HHS R01 EB017337NIBIB NIH HHS R01 EB020613NICHD NIH HHS U54 HD090255NIMH NIH HHS R24 MH106096NIMH NIH HHS U01 MH093765
6 · The paper itself

Abstract

Quantitative susceptibility mapping (QSM) estimates the underlying tissue magnetic susceptibility from the gradient echo (GRE) phase signal through background phase removal and dipole inversion steps. Each of these steps typically requires the solution of an ill-posed inverse problem and thus necessitates additional regularization. Recently developed single-step QSM algorithms directly relate the unprocessed GRE phase to the unknown susceptibility distribution, thereby requiring the solution of a single inverse problem. In this work, we show that such a holistic approach provides susceptibility estimation with artifact mitigation and develop efficient algorithms that involve simple analytical solutions for all of the optimization steps. Our methods employ total variation (TV) and total generalized variation (TGV) to jointly perform the background removal and dipole inversion in a single step. Using multiple spherical mean value (SMV) kernels of varying radii permits high-fidelity background removal whilst retaining the phase information in the cortex. Using numerical simulations, we demonstrate that the proposed single-step methods reduce the reconstruction error by up to 66% relative to the multi-step methods that involve SMV background filtering with the same number of SMV kernels, followed by TV- or TGV-regularized dipole inversion. In vivo single-step experiments demonstrate a dramatic reduction in dipole streaking artifacts and improved homogeneity of image contrast. These acquisitions employ the rapid three-dimensional echo planar imaging (3D EPI) and Wave-CAIPI (controlled aliasing in parallel imaging) trajectories for signal-to-noise ratio-efficient whole-brain imaging. Herein, we also demonstrate the multi-echo capability of the Wave-CAIPI sequence for the first time, and introduce an automated, phase-sensitive coil sensitivity estimation scheme based on a 4-s calibration acquisition. Copyright © 2016 John Wiley & Sons, Ltd.

Indexed as

AlgorithmsBrainDiffusion Magnetic Resonance ImagingEcho-Planar ImagingHumansImage EnhancementImage Interpretation, Computer-AssistedImaging, Three-DimensionalPhantoms, ImagingReproducibility of ResultsSensitivity and Specificityalternating direction method of multipliers (ADMM)quantitative susceptibility mapping (QSM)total generalized variation (TGV)total variation (TV)Wave-CAIPI

Identifiers

PMID27332141
PMCPMC5179325

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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.