title: Image-based Reconstruction of 3D Myocardial Infarct Geometry for Patient Specific Applications.
abstract: Accurate reconstruction of the three-dimensional (3D) geometry of a myocardial infarct from two-dimensional (2D) multi-slice image sequences has important applications in the clinical evaluation and treatment of patients with ischemic cardiomyopathy. However, this reconstruction is challenging because the resolution of common clinical scans used to acquire infarct structure, such as short-axis, late-gadolinium enhanced cardiac magnetic resonance (LGE-CMR) images, is low, especially in the out-of-plane direction. In this study, we propose a novel technique to reconstruct the 3D infarct geometry from low resolution clinical images. Our methodology is based on a function called logarithm of odds (LogOdds), which allows the broader class of linear combinations in the LogOdds vector space as opposed to being limited to only a convex combination in the binary label space. To assess the efficacy of the method, we used high-resolution LGE-CMR images of 36 human heartsin vivo, and 3 canine heartsex vivo. The infarct was manually segmented in each slice of the acquired images, and the manually segmented data were downsampled to clinical resolution. The developed method was then applied to the downsampled image slices, and the resulting reconstructions were compared with the manually segmented data. Several existing reconstruction techniques were also implemented, and compared with the proposed method. The results show that the LogOdds method significantly outperforms all the other tested methods in terms of region overlap.
authors.affiliation: Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, United States ; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States.
authors.forename: Eranga
authors.initials: E
authors.lastname: Ukwatta
authors.affiliation: Department of Computing, Imperial College London, London, United Kingdom.
authors.forename: Martin
authors.initials: M
authors.lastname: Rajchl
authors.affiliation: Stephenson Cardiovascular MR Centre, University of Calgary, Calgary, AB, Canada.
authors.forename: James
authors.initials: J
authors.lastname: White
authors.affiliation: Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, United States ; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States.
authors.forename: Farhad
authors.initials: F
authors.lastname: Pashakhanloo
authors.affiliation: Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States.
authors.forename: Daniel A
authors.initials: DA
authors.lastname: Herzka
authors.affiliation: Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States.
authors.forename: Elliot
authors.initials: E
authors.lastname: McVeigh
authors.affiliation: Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States ; School of Medicine, Johns Hopkins University, Baltimore, MD, United States.
authors.forename: Albert C
authors.initials: AC
authors.lastname: Lardo
authors.affiliation: Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, United States ; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States ; School of Medicine, Johns Hopkins University, Baltimore, MD, United States.
authors.forename: Natalia
authors.initials: N
authors.lastname: Trayanova
authors.affiliation: Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, United States ; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States.
authors.forename: Fijoy
authors.initials: F
authors.lastname: Vadakkumpadan
keywords: Image-based Reconstruction; Interpolation; Logarithm of Odds; Myocardial Infarct