Evidence map›Paper›PMID 36366046›Full record

ArticleSensors (Basel, Switzerland)2022

Ultrasound Ultrafast Power Doppler Imaging with High Signal-to-Noise Ratio by Temporal Multiply-and-Sum (TMAS) Autocorrelation.

Che-Chou Shen, Feng-Ting Guo

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 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

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.

  1. High-Resolution Power Doppler Using Null Subtraction Imaging.IEEE transactions on medical imaging · 2024
    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

2 authors.

Che-Chou ShenDepartment of Electrical Engineering, National Taiwan University of Science and Technology, Taipei 106335, Taiwan.ORCID 0000-0002-4542-4195
Feng-Ting GuoDepartment of Electrical Engineering, National Taiwan University of Science and Technology, Taipei 106335, Taiwan.ORCID 0000-0001-6152-114X

Funding

Ministry of Science and Technology, Taiwan 108-2221-E-011-071-MY3Ministry of Science and Technology, Taiwan 108-2221-E-011-072-MY3
6 · The paper itself

Abstract

Coherent plane wave compounding (CPWC) reconstructs transmit focusing by coherently summing several low-resolution plane-wave (PW) images from different transmit angles to improve its image resolution and quality. The high frame rate of CPWC imaging enables a much larger number of Doppler ensembles such that the Doppler estimation of blood flow becomes more reliable. Due to the unfocused PW transmission, however, one major limitation of the Doppler estimation in CPWC imaging is the relatively low signal-to-noise ratio (SNR). Conventionally, the Doppler power is estimated by a zero-lag autocorrelation which reduces the noise variance, but not the noise level. A higher-lag autocorrelation method such as the first-lag (R(1)) power Doppler image has been developed to take advantage of the signal coherence in the temporal direction for suppressing uncorrelated random noises. In this paper, we propose a novel Temporal Multiply-and-Sum (TMAS) power Doppler detection method to further improve the noise suppression of the higher-lag method by modulating the signal coherence among the temporal correlation pairs in the higher-lag autocorrelation with a tunable

Indexed as

AngiographyUltrasonography, DopplerImage Processing, Computer-AssistedPhantoms, ImagingSignal-To-Noise RatioUltrasonographycoherent plane-wave compounding (CPWC)higher-lag autocorrelationplane-wave (PW) imagingpower Doppler detectionsignal coherencesignal decorrelationtemporal multiply-and-sum (TMAS)

Identifiers

PMID36366046
PMCPMC9655537

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.