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Liver PDFF estimation using a multi-decoder water-fat separation neural network with a reduced number of echoes.
Meneses, Juan Pablo; Arrieta, Cristobal; Della Maggiora, Gabriel; Besa, Cecilia; Urbina, Jesús; Arrese, Marco; Gana, Juan Cristóbal; Galgani, Jose E; Tejos, Cristian; Uribe, Sergio.
Afiliação
  • Meneses JP; Biomedical Imaging Center, Pontificia Universidad Católica de Chile, Santiago, Chile.
  • Arrieta C; Millennium Institute for Intelligent Healthcare Engineering iHEALTH, Santiago, Chile.
  • Della Maggiora G; Department of Electrical Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile.
  • Besa C; Millennium Institute for Intelligent Healthcare Engineering iHEALTH, Santiago, Chile.
  • Urbina J; Faculty of Engineering, Universidad Alberto Hurtado, Santiago, Chile.
  • Arrese M; Biomedical Imaging Center, Pontificia Universidad Católica de Chile, Santiago, Chile.
  • Gana JC; Millennium Institute for Intelligent Healthcare Engineering iHEALTH, Santiago, Chile.
  • Galgani JE; Department of Radiology, School of Medicine, Pontificia Universidad Catolica de Chile, Santiago, Chile.
  • Tejos C; Biomedical Imaging Center, Pontificia Universidad Católica de Chile, Santiago, Chile.
  • Uribe S; Millennium Institute for Intelligent Healthcare Engineering iHEALTH, Santiago, Chile.
Eur Radiol ; 33(9): 6557-6568, 2023 Sep.
Article em En | MEDLINE | ID: mdl-37014405
OBJECTIVE: To accurately estimate liver PDFF from chemical shift-encoded (CSE) MRI using a deep learning (DL)-based Multi-Decoder Water-Fat separation Network (MDWF-Net), that operates over complex-valued CSE-MR images with only 3 echoes. METHODS: The proposed MDWF-Net and a U-Net model were independently trained using the first 3 echoes of MRI data from 134 subjects, acquired with conventional 6-echoes abdomen protocol at 1.5 T. Resulting models were then evaluated using unseen CSE-MR images obtained from 14 subjects that were acquired with a 3-echoes CSE-MR pulse sequence with a shorter duration compared to the standard protocol. Resulting PDFF maps were qualitatively assessed by two radiologists, and quantitatively assessed at two corresponding liver ROIs, using Bland Altman and regression analysis for mean values, and ANOVA testing for standard deviation (STD) (significance level: .05). A 6-echo graph cut was considered ground truth. RESULTS: Assessment of radiologists demonstrated that, unlike U-Net, MDWF-Net had a similar quality to the ground truth, despite it considered half of the information. Regarding PDFF mean values at ROIs, MDWF-Net showed a better agreement with ground truth (regression slope = 0.94, R2 = 0.97) than U-Net (regression slope = 0.86, R2 = 0.93). Moreover, ANOVA post hoc analysis of STDs showed a statistical difference between graph cuts and U-Net (p < .05), unlike MDWF-Net (p = .53). CONCLUSION: MDWF-Net showed a liver PDFF accuracy comparable to the reference graph cut method, using only 3 echoes and thus allowing a reduction in the acquisition times. CLINICAL RELEVANCE STATEMENT: We have prospectively validated that the use of a multi-decoder convolutional neural network to estimate liver proton density fat fraction allows a significant reduction in MR scan time by reducing the number of echoes required by 50%. KEY POINTS: • Novel water-fat separation neural network allows for liver PDFF estimation by using multi-echo MR images with a reduced number of echoes. • Prospective single-center validation demonstrated that echo reduction leads to a significant shortening of the scan time, compared to standard 6-echo acquisition. • Qualitative and quantitative performance of the proposed method showed no significant differences in PDFF estimation with respect to the reference technique.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Água / Fígado Tipo de estudo: Guideline / Observational_studies / Prognostic_studies / Qualitative_research Limite: Humans Idioma: En Revista: Eur Radiol Assunto da revista: RADIOLOGIA Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Chile

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Água / Fígado Tipo de estudo: Guideline / Observational_studies / Prognostic_studies / Qualitative_research Limite: Humans Idioma: En Revista: Eur Radiol Assunto da revista: RADIOLOGIA Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Chile