Evidence map›Paper›PMID 38613715›Full record

ArticleMagma (New York, N.Y.)2024

Results of the 2023 ISBI challenge to reduce GABA-edited MRS acquisition time.

Rodrigo Pommot Berto, Hanna Bugler, Gabriel Dias, Mateus Oliveira, Lucas Ueda, Sergio Dertkigil, Paula D P Costa, Leticia Rittner, Julian P Merkofer, Dennis M J van de Sande and 11 more

Abstract read
PubMed Publisher
In one paragraph

Article in Magma (New York, N.Y.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

21 authors.

Rodrigo Pommot Berto *Department of Biomedical Engineering, University of Calgary, Calgary, Canada.
Hanna Bugler *Department of Biomedical Engineering, University of Calgary, Calgary, Canada. hanna.bugler@ucalgary.ca.ORCID http://orcid.org/0009-0003-1331-3233
Gabriel DiasSchool of Electrical and Computer Engineering, University of Campinas, Campinas, Brazil.
Mateus OliveiraSchool of Electrical and Computer Engineering, University of Campinas, Campinas, Brazil.
Lucas UedaSchool of Electrical and Computer Engineering, University of Campinas, Campinas, Brazil.
Sergio DertkigilSchool of Medical Sciences, University of Campinas, Campinas, Brazil.
Paula D P CostaSchool of Electrical and Computer Engineering, University of Campinas, Campinas, Brazil.
Leticia RittnerSchool of Electrical and Computer Engineering, University of Campinas, Campinas, Brazil.
Julian P MerkoferDepartment of Electrical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands.
Dennis M J van de SandeDepartment of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands.
Sina AmirrajabDepartment of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands.
Gerhard S DrenthenDepartment of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, Netherlands.
Mitko VetaDepartment of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands.
Jacobus F A JansenDepartment of Electrical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands.
Marcel BreeuwerDepartment of Electrical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands.
Ruud J G van SlounDepartment of Electrical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands.
Abdul QayyumNational Heart & Lung Institute, Imperial College London, London, UK.
Cristobal RoderoNational Heart & Lung Institute, Imperial College London, London, UK.
Steven NiedererNational Heart & Lung Institute, Imperial College London, London, UK.
Roberto SouzaHotchkiss Brain Institute, University of Calgary, Calgary, Canada.
Ashley D HarrisDepartment of Radiology, University of Calgary, Calgary, Canada.

Funding

British Heart Foundation RG/20/4/34 803Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) 001EUREKA IA Call ITEA4 project 20209National Council for Scientific and Technological Development (CNPq) process #313598/2020-7Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grant RGPIN-2017-03875Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grant RGPIN-2021-02867São Paulo Research Foundation (FAPESP) - BI0S Brazilian Institute of Data Science 2020/09838-0São Paulo Research Foundation (FAPESP) - BRAINN - Brazilian Institute of Neuroscience and Neurotechnology 2013/07559-3Technology Missions Fund EPSRC Grant EP/X03870X/1
6 · The paper itself

Abstract

purposeUse a conference challenge format to compare machine learning-based gamma-aminobutyric acid (GABA)-edited magnetic resonance spectroscopy (MRS) reconstruction models using one-quarter of the transients typically acquired during a complete scan.

methodsThere were three tracks: Track 1: simulated data, Track 2: identical acquisition parameters with in vivo data, and Track 3: different acquisition parameters with in vivo data. The mean squared error, signal-to-noise ratio, linewidth, and a proposed shape score metric were used to quantify model performance. Challenge organizers provided open access to a baseline model, simulated noise-free data, guides for adding synthetic noise, and in vivo data.

resultsThree submissions were compared. A covariance matrix convolutional neural network model was most successful for Track 1. A vision transformer model operating on a spectrogram data representation was most successful for Tracks 2 and 3. Deep learning (DL) reconstructions with 80 transients achieved equivalent or better SNR, linewidth and fit error compared to conventional 320 transient reconstructions. However, some DL models optimized linewidth and SNR without actually improving overall spectral quality, indicating a need for more robust metrics.

conclusionDL-based reconstruction pipelines have the promise to reduce the number of transients required for GABA-edited MRS.

Indexed as

Deep Learninggamma-Aminobutyric AcidMagnetic Resonance SpectroscopySignal-To-Noise RatioAlgorithmsBrainComputer SimulationHumansImage Processing, Computer-AssistedMachine LearningNeural Networks, Computergamma-Aminobutyric AcidBenchmarkingComputerDeep learningMagnetic resonance spectroscopyNeural networks

Identifiers

PMID38613715

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.