Results of the 2023 ISBI challenge to reduce GABA-edited MRS acquisition time.
MAGMA
; 37(3): 449-463, 2024 Jul.
Article
em En
| MEDLINE
| ID: mdl-38613715
ABSTRACT
PURPOSE:
Use 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.METHODS:
There 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.RESULTS:
Three 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.CONCLUSION:
DL-based reconstruction pipelines have the promise to reduce the number of transients required for GABA-edited MRS.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Espectroscopia de Ressonância Magnética
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Razão Sinal-Ruído
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Aprendizado Profundo
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Ácido gama-Aminobutírico
Limite:
Humans
Idioma:
En
Revista:
MAGMA
Assunto da revista:
DIAGNOSTICO POR IMAGEM
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
Canadá