Evidence map›Paper›PMID 25809847›Full record

ArticleMagnetic resonance in medicine2016

XD-GRASP: Golden-angle radial MRI with reconstruction of extra motion-state dimensions using compressed sensing.

Li Feng, Leon Axel, Hersh Chandarana, Kai Tobias Block, Daniel K Sodickson, Ricardo Otazo

Abstract read
In one paragraph

Article in Magnetic resonance in medicine, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 356 papers.

0numbers the graph read from it
0cells of the map it votes in
356citing 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

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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

356 citing papers in PubMed.

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296 more citing papers are in PubMed but not listed here.

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

6 authors.

Li FengCenter for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University School of Medicine, New York, New York, USA.
Leon AxelCenter for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University School of Medicine, New York, New York, USA.
Hersh ChandaranaCenter for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University School of Medicine, New York, New York, USA.
Kai Tobias BlockCenter for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University School of Medicine, New York, New York, USA.
Daniel K SodicksonCenter for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University School of Medicine, New York, New York, USA.
Ricardo OtazoCenter for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University School of Medicine, New York, New York, USA.

Funding

TR&D 4: Revealing Microstructure: Biophysical modeling and validation for discovery and clinical careP41EB017183 · NIBIB · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI Dmitry S Novikov · 2014 to 2026
$19.3M
Parallel MR Imaging: New Techniques and TechnologiesR01EB000447 · NIBIB · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI SODICKSON, DANIEL K · 2002 to 2012
$5.1M
High-Performance High-Field Parallel MRI.R01EB002568 · NIBIB · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI SODICKSON, DANIEL K · 2003 to 2014
$4.8M
NIBIB NIH HHS P41 EB017183NIBIB NIH HHS R01 EB000447NIBIB NIH HHS R01 EB002568
6 · The paper itself

Abstract

purposeTo develop a novel framework for free-breathing MRI called XD-GRASP, which sorts dynamic data into extra motion-state dimensions using the self-navigation properties of radial imaging and reconstructs the multidimensional dataset using compressed sensing.

methodsRadial k-space data are continuously acquired using the golden-angle sampling scheme and sorted into multiple motion-states based on respiratory and/or cardiac motion signals derived directly from the data. The resulting undersampled multidimensional dataset is reconstructed using a compressed sensing approach that exploits sparsity along the new dynamic dimensions. The performance of XD-GRASP is demonstrated for free-breathing three-dimensional (3D) abdominal imaging, two-dimensional (2D) cardiac cine imaging and 3D dynamic contrast-enhanced (DCE) MRI of the liver, comparing against reconstructions without motion sorting in both healthy volunteers and patients.

resultsXD-GRASP separates respiratory motion from cardiac motion in cardiac imaging, and respiratory motion from contrast enhancement in liver DCE-MRI, which improves image quality and reduces motion-blurring artifacts.

conclusionXD-GRASP represents a new use of sparsity for motion compensation and a novel way to handle motions in the context of a continuous acquisition paradigm. Instead of removing or correcting motion, extra motion-state dimensions are reconstructed, which improves image quality and also offers new physiological information of potential clinical value.

Indexed as

AbdomenAdultAtrioventricular BlockCardiac-Gated Imaging TechniquesContrast MediaData CompressionFemaleHealthy VolunteersHumansImage EnhancementImaging, Three-DimensionalMagnetic Resonance ImagingMagnetic Resonance Imaging, CineMaleMiddle AgedRespiratory-Gated Imaging TechniquesContrast Mediacompressed sensingfree-breathinggolden-anglemotion compensationradial sampling

Identifiers

PMID25809847
PMCPMC4583338

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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.