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Accelerating Cardiac Diffusion Tensor Imaging With a U-Net Based Model: Toward Single Breath-Hold.
Ferreira, Pedro F; Banerjee, Arjun; Scott, Andrew D; Khalique, Zohya; Yang, Guang; Rajakulasingam, Ramyah; Dwornik, Maria; De Silva, Ranil; Pennell, Dudley J; Firmin, David N; Nielles-Vallespin, Sonia.
Afiliação
  • Ferreira PF; Cardiovascular Magnetic Resonance Unit, Royal Brompton Hospital, London, UK.
  • Banerjee A; National Heart and Lung Institute, Imperial College, London, UK.
  • Scott AD; Department of Computing, Imperial College, London, UK.
  • Khalique Z; Cardiovascular Magnetic Resonance Unit, Royal Brompton Hospital, London, UK.
  • Yang G; National Heart and Lung Institute, Imperial College, London, UK.
  • Rajakulasingam R; Cardiovascular Magnetic Resonance Unit, Royal Brompton Hospital, London, UK.
  • Dwornik M; National Heart and Lung Institute, Imperial College, London, UK.
  • De Silva R; Cardiovascular Magnetic Resonance Unit, Royal Brompton Hospital, London, UK.
  • Pennell DJ; National Heart and Lung Institute, Imperial College, London, UK.
  • Firmin DN; Cardiovascular Magnetic Resonance Unit, Royal Brompton Hospital, London, UK.
  • Nielles-Vallespin S; National Heart and Lung Institute, Imperial College, London, UK.
J Magn Reson Imaging ; 56(6): 1691-1704, 2022 12.
Article em En | MEDLINE | ID: mdl-35460138
BACKGROUND: In vivo cardiac diffusion tensor imaging (cDTI) characterizes myocardial microstructure. Despite its potential clinical impact, considerable technical challenges exist due to the inherent low signal-to-noise ratio. PURPOSE: To reduce scan time toward one breath-hold by reconstructing diffusion tensors for in vivo cDTI with a fitting-free deep learning approach. STUDY TYPE: Retrospective. POPULATION: A total of 197 healthy controls, 547 cardiac patients. FIELD STRENGTH/SEQUENCE: A 3 T, diffusion-weighted stimulated echo acquisition mode single-shot echo-planar imaging sequence. ASSESSMENT: A U-Net was trained to reconstruct the diffusion tensor elements of the reference results from reduced datasets that could be acquired in 5, 3 or 1 breath-hold(s) (BH) per slice. Fractional anisotropy (FA), mean diffusivity (MD), helix angle (HA), and sheetlet angle (E2A) were calculated and compared to the same measures when using a conventional linear-least-square (LLS) tensor fit with the same reduced datasets. A conventional LLS tensor fit with all available data (12 ± 2.0 [mean ± sd] breath-holds) was used as the reference baseline. STATISTICAL TESTS: Wilcoxon signed rank/rank sum and Kruskal-Wallis tests. Statistical significance threshold was set at P = 0.05. Intersubject measures are quoted as median [interquartile range]. RESULTS: For global mean or median results, both the LLS and U-Net methods with reduced datasets present a bias for some of the results. For both LLS and U-Net, there is a small but significant difference from the reference results except for LLS: MD 5BH (P = 0.38) and MD 3BH (P = 0.09). When considering direct pixel-wise errors the U-Net model outperformed significantly the LLS tensor fit for reduced datasets that can be acquired in three or just one breath-hold for all parameters. DATA CONCLUSION: Diffusion tensor prediction with a trained U-Net is a promising approach to minimize the number of breath-holds needed in clinical cDTI studies. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY: Stage 1.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Imagem de Tensor de Difusão / Coração Tipo de estudo: Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Revista: J Magn Reson Imaging Assunto da revista: DIAGNOSTICO POR IMAGEM Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Imagem de Tensor de Difusão / Coração Tipo de estudo: Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Revista: J Magn Reson Imaging Assunto da revista: DIAGNOSTICO POR IMAGEM Ano de publicação: 2022 Tipo de documento: Article