Evidence map›Paper›PMID 40872000›Full record

ArticleSensors (Basel, Switzerland)2025

Sparse Transform and Compressed Sensing Methods to Improve Efficiency and Quality in Magnetic Resonance Medical Imaging.

Santiago Villota, Esteban Inga

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2025. 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

2 authors.

Santiago VillotaBiomedical Engineering Program, Universidad Politécnica Salesiana, Quito EC170525, Ecuador.ORCID 0009-0001-4593-2210
Esteban IngaMaster in ICT for Education, Smart Grid Research Group (GIREI), Universidad Politécnica Salesiana, Quito EC170525, Ecuador.ORCID 0000-0002-0837-0642

Funding

GIREIUniversidad Politécnica Salesiana
6 · The paper itself

Abstract

This paper explores the application of transform-domain sparsification and compressed sensing (CS) techniques to improve the efficiency and quality of magnetic resonance imaging (MRI). We implement and evaluate three sparsifying methods-discrete wavelet transform (DWT), fast Fourier transform (FFT), and discrete cosine transform (DCT)-which are used to simulate subsampled reconstruction via inverse transforms. Additionally, one accurate CS reconstruction algorithm, basis pursuit (BP), using the L

Indexed as

Data CompressionImage Processing, Computer-AssistedMagnetic Resonance ImagingAlgorithmsFourier AnalysisHumansSignal-To-Noise RatioWavelet Analysisbasis pursuitcompressed sensingmagnetic resonance imagingoptimizationPSNRRMSEsignal reconstruction

Identifiers

PMID40872000
PMCPMC12389978

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.