Evidence map›Paper›PMID 35666824›Full record

ArticleMagnetic resonance in medicine2022

Gradient-echo-train-based sub-millisecond periodic event encoded dynamic imaging with random (k, t)-space undersampling: k-t get-SPEEDI.

Qingfei Luo, Zheng Zhong, Kaibao Sun, Alessandro Scotti, Xiaohong Joe Zhou

Abstract read
In one paragraph

Article in Magnetic resonance in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Qingfei LuoCenter for Magnetic Resonance Research, University of Illinois at Chicago, Chicago, Illinois, USA.ORCID 0000-0001-5574-817X
Zheng ZhongDepartment of Radiology, Stanford University, Stanford, California, USA.ORCID 0000-0002-0162-5964
Kaibao SunCenter for Magnetic Resonance Research, University of Illinois at Chicago, Chicago, Illinois, USA.ORCID 0000-0001-6923-5914
Alessandro ScottiCenter for Magnetic Resonance Research, University of Illinois at Chicago, Chicago, Illinois, USA.ORCID 0000-0002-8892-881X
Xiaohong Joe ZhouCenter for Magnetic Resonance Research, University of Illinois at Chicago, Chicago, Illinois, USA.ORCID 0000-0003-0793-4925

Funding

A High-End 3 Tesla Human MRI Scanner Dedicated to ResearchS10RR028898 · NCRR · UNIVERSITY OF ILLINOIS AT CHICAGO · PI ZHOU, XIAOHONG JOE · 2010 to 2010
$2.9M
Probing Intra-voxel Tissue Heterogeneity Using MRIR01EB026716 · NIBIB · UNIVERSITY OF ILLINOIS AT CHICAGO · PI ZHOU, XIAOHONG JOE · 2018 to 2022
$1.7M
NCRR NIH HHS S10 RR028898NIBIB NIH HHS R01 EB026716
6 · The paper itself

Abstract

purposeThe gradient-echo-train-based Sub-millisecond Periodic Event Encoded Dynamic Imaging (get-SPEEDI) technique provides ultrahigh temporal resolutions (∼0.6 ms) for detecting rapid physiological activities, but its practical adoption can be hampered by long scan times. This study aimed at developing a more efficient variant of get-SPEEDI for reducing the scan time without degrading temporal resolution or image quality.

methodsThe proposed pulse sequence, named k-t get-SPEEDI, accelerated get-SPEEDI acquisition by undersampling the k-space phase-encoding lines semi-randomly. At each time frame, k-space was fully sampled in the central region whereas randomly undersampled in the outer regions. A time-series of images was reconstructed using an algorithm based on the joint partial separability and sparsity constraints. To demonstrate the performance of k-t get-SPEEDI, images of human aortic valve opening and closing were acquired with 0.6-ms temporal resolution and compared with those from conventional get-SPEEDI.

resultsk-t get-SPEEDI achieved a 2-fold scan time reduction over the conventional get-SPEEDI (from ∼6 to ∼3 min), while achieving comparable SNRs and contrast-to-noise ratio (CNRs) for visualizing the dynamic process of aortic valve: SNR/CNR

conclusionsThe k-t get-SPEEDI pulse sequence was able to half the scan time without compromising the image quality and ultrahigh temporal resolution. Additional scan time reduction may also be possible, facilitating in vivo adoptions of SPEEDI techniques.

Indexed as

Image Interpretation, Computer-AssistedMagnetic Resonance ImagingAlgorithmsHumansImage Processing, Computer-AssistedImaging, Three-Dimensionaljoint partial separability and sparsityrandom undersamplingSPEEDIsub-millisecondultrahigh temporal resolution

Identifiers

PMID35666824
PMCPMC9339512

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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.