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StrainNet: Improved Myocardial Strain Analysis of Cine MRI by Deep Learning from DENSE.
Wang, Yu; Sun, Changyu; Ghadimi, Sona; Auger, Daniel C; Croisille, Pierre; Viallon, Magalie; Mangion, Kenneth; Berry, Colin; Haggerty, Christopher M; Jing, Linyuan; Fornwalt, Brandon K; Cao, J Jane; Cheng, Joshua; Scott, Andrew D; Ferreira, Pedro F; Oshinski, John N; Ennis, Daniel B; Bilchick, Kenneth C; Epstein, Frederick H.
Afiliação
  • Wang Y; From the Department of Biomedical Engineering, University of Virginia, Biomedical Engineering and Medical Science Building, Room 2013, MR5, Charlottesville, VA 22903 (Y.W., C.S., S.G., D.C.A., F.H.E.); Department of Biomedical, Biological and Chemical Engineering and Department of Radiology, Univers
  • Sun C; From the Department of Biomedical Engineering, University of Virginia, Biomedical Engineering and Medical Science Building, Room 2013, MR5, Charlottesville, VA 22903 (Y.W., C.S., S.G., D.C.A., F.H.E.); Department of Biomedical, Biological and Chemical Engineering and Department of Radiology, Univers
  • Ghadimi S; From the Department of Biomedical Engineering, University of Virginia, Biomedical Engineering and Medical Science Building, Room 2013, MR5, Charlottesville, VA 22903 (Y.W., C.S., S.G., D.C.A., F.H.E.); Department of Biomedical, Biological and Chemical Engineering and Department of Radiology, Univers
  • Auger DC; From the Department of Biomedical Engineering, University of Virginia, Biomedical Engineering and Medical Science Building, Room 2013, MR5, Charlottesville, VA 22903 (Y.W., C.S., S.G., D.C.A., F.H.E.); Department of Biomedical, Biological and Chemical Engineering and Department of Radiology, Univers
  • Croisille P; From the Department of Biomedical Engineering, University of Virginia, Biomedical Engineering and Medical Science Building, Room 2013, MR5, Charlottesville, VA 22903 (Y.W., C.S., S.G., D.C.A., F.H.E.); Department of Biomedical, Biological and Chemical Engineering and Department of Radiology, Univers
  • Viallon M; From the Department of Biomedical Engineering, University of Virginia, Biomedical Engineering and Medical Science Building, Room 2013, MR5, Charlottesville, VA 22903 (Y.W., C.S., S.G., D.C.A., F.H.E.); Department of Biomedical, Biological and Chemical Engineering and Department of Radiology, Univers
  • Mangion K; From the Department of Biomedical Engineering, University of Virginia, Biomedical Engineering and Medical Science Building, Room 2013, MR5, Charlottesville, VA 22903 (Y.W., C.S., S.G., D.C.A., F.H.E.); Department of Biomedical, Biological and Chemical Engineering and Department of Radiology, Univers
  • Berry C; From the Department of Biomedical Engineering, University of Virginia, Biomedical Engineering and Medical Science Building, Room 2013, MR5, Charlottesville, VA 22903 (Y.W., C.S., S.G., D.C.A., F.H.E.); Department of Biomedical, Biological and Chemical Engineering and Department of Radiology, Univers
  • Haggerty CM; From the Department of Biomedical Engineering, University of Virginia, Biomedical Engineering and Medical Science Building, Room 2013, MR5, Charlottesville, VA 22903 (Y.W., C.S., S.G., D.C.A., F.H.E.); Department of Biomedical, Biological and Chemical Engineering and Department of Radiology, Univers
  • Jing L; From the Department of Biomedical Engineering, University of Virginia, Biomedical Engineering and Medical Science Building, Room 2013, MR5, Charlottesville, VA 22903 (Y.W., C.S., S.G., D.C.A., F.H.E.); Department of Biomedical, Biological and Chemical Engineering and Department of Radiology, Univers
  • Fornwalt BK; From the Department of Biomedical Engineering, University of Virginia, Biomedical Engineering and Medical Science Building, Room 2013, MR5, Charlottesville, VA 22903 (Y.W., C.S., S.G., D.C.A., F.H.E.); Department of Biomedical, Biological and Chemical Engineering and Department of Radiology, Univers
  • Cao JJ; From the Department of Biomedical Engineering, University of Virginia, Biomedical Engineering and Medical Science Building, Room 2013, MR5, Charlottesville, VA 22903 (Y.W., C.S., S.G., D.C.A., F.H.E.); Department of Biomedical, Biological and Chemical Engineering and Department of Radiology, Univers
  • Cheng J; From the Department of Biomedical Engineering, University of Virginia, Biomedical Engineering and Medical Science Building, Room 2013, MR5, Charlottesville, VA 22903 (Y.W., C.S., S.G., D.C.A., F.H.E.); Department of Biomedical, Biological and Chemical Engineering and Department of Radiology, Univers
  • Scott AD; From the Department of Biomedical Engineering, University of Virginia, Biomedical Engineering and Medical Science Building, Room 2013, MR5, Charlottesville, VA 22903 (Y.W., C.S., S.G., D.C.A., F.H.E.); Department of Biomedical, Biological and Chemical Engineering and Department of Radiology, Univers
  • Ferreira PF; From the Department of Biomedical Engineering, University of Virginia, Biomedical Engineering and Medical Science Building, Room 2013, MR5, Charlottesville, VA 22903 (Y.W., C.S., S.G., D.C.A., F.H.E.); Department of Biomedical, Biological and Chemical Engineering and Department of Radiology, Univers
  • Oshinski JN; From the Department of Biomedical Engineering, University of Virginia, Biomedical Engineering and Medical Science Building, Room 2013, MR5, Charlottesville, VA 22903 (Y.W., C.S., S.G., D.C.A., F.H.E.); Department of Biomedical, Biological and Chemical Engineering and Department of Radiology, Univers
  • Ennis DB; From the Department of Biomedical Engineering, University of Virginia, Biomedical Engineering and Medical Science Building, Room 2013, MR5, Charlottesville, VA 22903 (Y.W., C.S., S.G., D.C.A., F.H.E.); Department of Biomedical, Biological and Chemical Engineering and Department of Radiology, Univers
  • Bilchick KC; From the Department of Biomedical Engineering, University of Virginia, Biomedical Engineering and Medical Science Building, Room 2013, MR5, Charlottesville, VA 22903 (Y.W., C.S., S.G., D.C.A., F.H.E.); Department of Biomedical, Biological and Chemical Engineering and Department of Radiology, Univers
  • Epstein FH; From the Department of Biomedical Engineering, University of Virginia, Biomedical Engineering and Medical Science Building, Room 2013, MR5, Charlottesville, VA 22903 (Y.W., C.S., S.G., D.C.A., F.H.E.); Department of Biomedical, Biological and Chemical Engineering and Department of Radiology, Univers
Radiol Cardiothorac Imaging ; 5(3): e220196, 2023 Jun.
Article em En | MEDLINE | ID: mdl-37404792
ABSTRACT

Purpose:

To develop a three-dimensional (two dimensions + time) convolutional neural network trained with displacement encoding with stimulated echoes (DENSE) data for displacement and strain analysis of cine MRI. Materials and

Methods:

In this retrospective multicenter study, a deep learning model (StrainNet) was developed to predict intramyocardial displacement from contour motion. Patients with various heart diseases and healthy controls underwent cardiac MRI examinations with DENSE between August 2008 and January 2022. Network training inputs were a time series of myocardial contours from DENSE magnitude images, and ground truth data were DENSE displacement measurements. Model performance was evaluated using pixelwise end-point error (EPE). For testing, StrainNet was applied to contour motion from cine MRI. Global and segmental circumferential strain (Ecc) derived from commercial feature tracking (FT), StrainNet, and DENSE (reference) were compared using intraclass correlation coefficients (ICCs), Pearson correlations, Bland-Altman analyses, paired t tests, and linear mixed-effects models.

Results:

The study included 161 patients (110 men; mean age, 61 years ± 14 [SD]), 99 healthy adults (44 men; mean age, 35 years ± 15), and 45 healthy children and adolescents (21 males; mean age, 12 years ± 3). StrainNet showed good agreement with DENSE for intramyocardial displacement, with an average EPE of 0.75 mm ± 0.35. The ICCs between StrainNet and DENSE and FT and DENSE were 0.87 and 0.72, respectively, for global Ecc and 0.75 and 0.48, respectively, for segmental Ecc. Bland-Altman analysis showed that StrainNet had better agreement than FT with DENSE for global and segmental Ecc.

Conclusion:

StrainNet outperformed FT for global and segmental Ecc analysis of cine MRI.Keywords Image Postprocessing, MR Imaging, Cardiac, Heart, Pediatrics, Technical Aspects, Technology Assessment, Strain, Deep Learning, DENSE Supplemental material is available for this article. © RSNA, 2023.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Clinical_trials / Health_technology_assessment / Prognostic_studies Idioma: En Revista: Radiol Cardiothorac Imaging Ano de publicação: 2023 Tipo de documento: Article
Texto completo
Adicionar na Minha BVS
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Clinical_trials / Health_technology_assessment / Prognostic_studies Idioma: En Revista: Radiol Cardiothorac Imaging Ano de publicação: 2023 Tipo de documento: Article