Evidence map›Paper›PMID 28543266›Full record

ArticleMedical physics2017

Technical Note: Evaluation of pre-reconstruction interpolation methods for iterative reconstruction of radial k-space data.

Ye Tian, Kay Condie Erb, Ganesh Adluru, Devavrat Likhite, Apoorva Pedgaonkar, Michael Blatt, Srikant Kamesh Iyer, John Roberts, Edward DiBella

Abstract read
In one paragraph

Article in Medical physics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

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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

9 authors.

Ye TianDepartment of Physics and Astronomy, University of Utah, Salt Lake City, UT, 84112, USA.
Kay Condie ErbDepartment of Physics and Astronomy, University of Utah, Salt Lake City, UT, 84112, USA.
Ganesh AdluruDepartment of Radiology and Imaging Sciences, University of Utah, Salt Lake City, UT, 84108, USA.
Devavrat LikhiteDepartment of Radiology and Imaging Sciences, University of Utah, Salt Lake City, UT, 84108, USA.
Apoorva PedgaonkarDepartment of Radiology and Imaging Sciences, University of Utah, Salt Lake City, UT, 84108, USA.
Michael BlattDepartment of Bioengineering, University of Utah, Salt Lake City, UT, 84108, USA.
Srikant Kamesh IyerDepartment of Radiology and Imaging Sciences, University of Utah, Salt Lake City, UT, 84108, USA.
John RobertsDepartment of Radiology and Imaging Sciences, University of Utah, Salt Lake City, UT, 84108, USA.
Edward DiBellaDepartment of Radiology and Imaging Sciences, University of Utah, Salt Lake City, UT, 84108, USA.

Funding

Quantitative MRI for characterizing heart failure with preserved ejection fractionR01HL135328 · NHLBI · UNIVERSITY OF UTAH · PI DIBELLA, EDWARD VR · 2017 to 2020
$2.6M
Quantitative cardiac MRI perfusion for longitudinal studiesR01HL113224 · NHLBI · UNIVERSITY OF UTAH · PI DIBELLA, EDWARD VR · 2012 to 2015
$2.0M
Rapid high order diffusion imaging for strokeR01NS083761 · NINDS · UNIVERSITY OF UTAH · PI DIBELLA, EDWARD VR · 2013 to 2016
$1.4M
NHLBI NIH HHS R01 HL113224NHLBI NIH HHS R01 HL135328NINDS NIH HHS R01 NS083761
6 · The paper itself

Abstract

purposeTo evaluate the use of three different pre-reconstruction interpolation methods to convert non-Cartesian k-space data to Cartesian samples such that iterative reconstructions can be performed more simply and more rapidly.

methodsPhantom as well as cardiac perfusion radial datasets were reconstructed by four different methods. Three of the methods used pre-reconstruction interpolation once followed by a fast Fourier transform (FFT) at each iteration. The methods were: bilinear interpolation of nearest-neighbor points (BINN), 3-point interpolation, and a multi-coil interpolator called GRAPPA Operator Gridding (GROG). The fourth method performed a full non-Uniform FFT (NUFFT) at each iteration. An iterative reconstruction with spatiotemporal total variation constraints was used with each method. Differences in the images were quantified and compared.

resultsThe GROG multicoil interpolation, the 3-point interpolation, and the NUFFT-at-each-iteration approaches produced high quality images compared to BINN, with the GROG-derived images having the fewest streaks among the three preinterpolation approaches. However, all reconstruction methods produced approximately equal results when applied to perfusion quantitation tasks. Pre-reconstruction interpolation gave approximately an 83% reduction in reconstruction time.

conclusionImage quality suffers little from using a pre-reconstruction interpolation approach compared to the more accurate NUFFT-based approach. GROG-based pre-reconstruction interpolation appears to offer the best compromise by using multicoil information to perform the interpolation to Cartesian sample points prior to image reconstruction. Speed gains depend on the implementation and relatively standard optimizations on a MATLAB platform result in preinterpolation speedups of ~ 6 compared to using NUFFT at every iteration, reducing the reconstruction time from around 42 min to 7 min.

Indexed as

AlgorithmsImage Processing, Computer-AssistedMagnetic Resonance ImagingFourier AnalysisHumansPhantoms, ImagingRadionuclide Imagingconstrained reconstructionGROGinterpolationiterativeMRInon-CartesianNUFFT

Identifiers

PMID28543266
PMCPMC5862392

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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.