MR image reconstruction via guided filter.

Heyan Huang, Hang Yang, Kang Wang

Abstract read

In one paragraph

Article in Medical & biological engineering & computing, 2018. 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

0.5field-weighted citation impact, top 36% of its field

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

1 citing paper in PubMed, 5 citations in OpenAlex.

Orthogonal tensor dictionary learning for accelerated dynamic MRI.Medical & biological engineering & computing · 2019
Article

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

3 authors at 3 institutions in 1 country.

Heyan HuangSchool of Sciences, Changchun University, Changchun, 130012, China. huanghy10@mails.jlu.edu.cn.

Hang YangChangchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, 130033, China.

Kang WangChina FAW Group Corporation R&D Center, Changchun, 130011, China.

Changchun Institute of Optics, Fine Mechanics and Physics · CNChangchun University · CNFirst Automotive Works (China) · CN

Funding

National Natural Science Foundation of China 61401425

6 · The paper itself

Abstract

Magnetic resonance imaging (MRI) reconstruction from the smallest possible set of Fourier samples has been a difficult problem in medical imaging field. In our paper, we present a new approach based on a guided filter for efficient MRI recovery algorithm. The guided filter is an edge-preserving smoothing operator and has better behaviors near edges than the bilateral filter. Our reconstruction method is consist of two steps. First, we propose two cost functions which could be computed efficiently and thus obtain two different images. Second, the guided filter is used with these two obtained images for efficient edge-preserving filtering, and one image is used as the guidance image, the other one is used as a filtered image in the guided filter. In our reconstruction algorithm, we can obtain more details by introducing guided filter. We compare our reconstruction algorithm with some competitive MRI reconstruction techniques in terms of PSNR and visual quality. Simulation results are given to show the performance of our new method.

Indexed as

AlgorithmsBrainHumansImage Processing, Computer-AssistedLegMagnetic Resonance ImagingCompressive sensingGuided filterImage reconstructionMagnetic resonance imaging

Identifiers

PMID28840445

DOI10.1007/s11517-017-1709-8

OpenAlexW2747186032

What OpenQuestion holds

Textmetadata

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.