Evidence map›Paper›PMID 30273146›Full record

ArticleIEEE transactions on medical imaging2019

a-f BLAST: Non-Iterative Radial k-t BLAST Reconstruction for Real-Time Imaging.

Madison Kretzler, Jesse Hamilton, Mark Griswold, Nicole Seiberlich

Abstract read
In one paragraph

Article in IEEE transactions on medical imaging, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

4 authors.

Madison Kretzler
Jesse Hamilton
Mark Griswold
Nicole Seiberlich

Funding

Improved Cardiac and Vascular MRI Using Parallel Imaging and Compressed SensingR01HL094557 · NHLBI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI GRISWOLD, MARK, SEIBERLICH, NICOLE · 2010 to 2018
$4.5M
Comprehensive quantitative ultrafast 3D liver MRIR01DK098503 · NIDDK · CASE WESTERN RESERVE UNIVERSITY · PI GULANI, VIKAS, SEIBERLICH, NICOLE · 2013 to 2017
$2.6M
BIOMEDICAL ENGINEERING RESEARCH FACILITIESC06RR012463 · NCRR · CASE WESTERN RESERVE UNIVERSITY · PI KUTINA, KENNETH L · 1997 to 1997
–
NCRR NIH HHS C06 RR012463NHLBI NIH HHS R01 HL094557NIDDK NIH HHS R01 DK098503
6 · The paper itself

Abstract

Non-Cartesian trajectories are advantageous in the collection and reconstruction of dynamic MR images. Like many other fast imaging methods, the reconstruction technique k-t broad-use linear acquisition speed-up technique (BLAST) has been extended to non-Cartesian k-t BLAST through a conjugate gradient-based approach. However, it is necessary to transform and grid the data to a Cartesian domain as part of the reconstruction, a process which can be time consuming and computationally intensive. In this paper, a new non-iterative reconstruction, a-f BLAST, is proposed to extend k-t BLAST to radial sampling. The a-f BLAST reconstruction is performed in a previously unexplored domain termed the angular frequency-temporal frequency (a-f) space and uses similar steps to Cartesian k-t BLAST. The performance of this method was demonstrated on retrospectively undersampled numerical phantoms and compared with k-t BLAST, non-Cartesian k-t BLAST, and the sliding window technique. In addition, the reconstruction was tested on retrospectively and prospectively undersampled in vivo cardiac data. The a-f BLAST is shown to have a similar reconstruction time as k-t BLAST as well as outperform k-t BLAST in terms of root-mean-square error.

Indexed as

HeartHumansImage Processing, Computer-AssistedMagnetic Resonance ImagingPhantoms, ImagingRetrospective Studies

Identifiers

PMID30273146
PMCPMC6469684

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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.