Improving Brain Metabolite Detection with a Combined Low-Rank Approximation and Denoising Diffusion Probabilistic Model Approach.

Yeong-Jae Jeon, Kyung Min Nam, Shin-Eui Park, Hyeon-Man Baek

Abstract read

In one paragraph

Article in Bioengineering (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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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

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Yeong-Jae JeonDepartment of Health Sciences and Technology, Gachon Advanced Institute for Health Sciences and Technology, Gachon University, Incheon 21999, Republic of Korea.

Kyung Min NamHigh Field MR Research Group, Center for Image Sciences, University Medical Centre Utrecht, Heidelberglaan 100, P.O. Box 85500, 3584 CX Utrecht, The Netherlands.ORCID 0000-0002-9185-3986

Shin-Eui ParkDepartment of Biomedical Science, Lee Gil Ya Cancer and Diabetes Institute, Gachon University, Incheon 21999, Republic of Korea.

Hyeon-Man BaekDepartment of Health Sciences and Technology, Gachon Advanced Institute for Health Sciences and Technology, Gachon University, Incheon 21999, Republic of Korea.

Funding

the Basic Science Research Capacity Enhancement Project through the Republic of Korea Basic Science Institute (National Research Facilities and Equipment Center) 2021R1A6C101A432the Bio and Medical Technology Development Program of the National Research Foundation funded by the Korean government (MSIT) 2020M3A9E4104384the European Union's Horizon 2020 Research and Innovation Program under the Marie Sklodowska-Curie 813120

6 · The paper itself

Abstract

In vivo proton magnetic resonance spectroscopy (MRS) is a noninvasive technique for monitoring brain metabolites. However, it is challenged by a low signal-to-noise ratio (SNR), often necessitating extended scan times to compensate. One of the conventional techniques for noise reduction is signal averaging, which is inherently time-consuming and can lead to participant discomfort, thus posing limitations in clinical settings. This study aimed to develop a hybrid denoising strategy that integrates low-rank approximation and denoising diffusion probabilistic model (DDPM) to enhance MRS data quality and shorten scan times. Using publicly available

Indexed as

1H MRSanterior cingulate cortex (ACC)CSVDdenoisingdenoising diffusion probabilistic model (DDPM)functional MRSlow-rank approximationpain

Identifiers

PMID39593829

DOI10.3390/bioengineering11111170

PMCPMC11592133

What Socratic holds

Textmetadata

LicenceCC BY

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Registered trials

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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 Socratic graph.