Myocardial scar quanti cation using SLIC supervoxels - Parcellation based on tissue characteristic strains
Abnormal myocardial motion occurs in many cardiac pathologies, though in different ways, depending on the disease, some of which can result in negative clinical outcomes. Therefore, a better understanding of the contractile capability of the tissue is crucial in providing an improved and patient-spe...
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| Format: | Conference item |
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7th International Workshop on Statistical Atlases & Computational Modelling of the Heart
2017
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| _version_ | 1797073793538064384 |
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| author | Popescu, I Irving, B Borlotti, A Dall'Armellina, E Grau, V Popescu, I |
| author_facet | Popescu, I Irving, B Borlotti, A Dall'Armellina, E Grau, V Popescu, I |
| author_sort | Popescu, I |
| collection | OXFORD |
| description | Abnormal myocardial motion occurs in many cardiac pathologies, though in different ways, depending on the disease, some of which can result in negative clinical outcomes. Therefore, a better understanding of the contractile capability of the tissue is crucial in providing an improved and patient-specific clinical outcome [4]. Cardiovascular MagneticResonance Imaging (CMR) is considered the gold standard for the assessment of cardiac function and has the potential to also be used for routine tissue strain analysis because of its high availability in clinical practice. In this study we estimate the local strain in myocardial tissue over a cardiac cycle using cine MRI imaging to perform the analysis. To quantify the tissue displacement, we use the diffeomorphic demons registration algorithm [15] in a multi-step 3D registration, for the minimization of cumulative errors propagation. Using the displacement gradient of the deformation, individual voxel strain curves are computed. We present a novel method for parcellating the myocardium into regions based on the strain behaviour of clusters of voxels. We define the supervoxels using the Simple Linear Iterative Clustering (SLIC) algorithm [1] inside a predefined mask. The results are consistent with late gadolinium enhancement scar identification. |
| first_indexed | 2024-03-06T23:27:09Z |
| format | Conference item |
| id | oxford-uuid:6ac1644d-51ef-4e0e-a1b7-d768823af3e0 |
| institution | University of Oxford |
| last_indexed | 2024-03-06T23:27:09Z |
| publishDate | 2017 |
| publisher | 7th International Workshop on Statistical Atlases & Computational Modelling of the Heart |
| record_format | dspace |
| spelling | oxford-uuid:6ac1644d-51ef-4e0e-a1b7-d768823af3e02022-03-26T18:59:31ZMyocardial scar quanti cation using SLIC supervoxels - Parcellation based on tissue characteristic strainsConference itemhttp://purl.org/coar/resource_type/c_5794uuid:6ac1644d-51ef-4e0e-a1b7-d768823af3e0Symplectic Elements at Oxford7th International Workshop on Statistical Atlases & Computational Modelling of the Heart2017Popescu, IIrving, BBorlotti, ADall'Armellina, EGrau, VPopescu, IAbnormal myocardial motion occurs in many cardiac pathologies, though in different ways, depending on the disease, some of which can result in negative clinical outcomes. Therefore, a better understanding of the contractile capability of the tissue is crucial in providing an improved and patient-specific clinical outcome [4]. Cardiovascular MagneticResonance Imaging (CMR) is considered the gold standard for the assessment of cardiac function and has the potential to also be used for routine tissue strain analysis because of its high availability in clinical practice. In this study we estimate the local strain in myocardial tissue over a cardiac cycle using cine MRI imaging to perform the analysis. To quantify the tissue displacement, we use the diffeomorphic demons registration algorithm [15] in a multi-step 3D registration, for the minimization of cumulative errors propagation. Using the displacement gradient of the deformation, individual voxel strain curves are computed. We present a novel method for parcellating the myocardium into regions based on the strain behaviour of clusters of voxels. We define the supervoxels using the Simple Linear Iterative Clustering (SLIC) algorithm [1] inside a predefined mask. The results are consistent with late gadolinium enhancement scar identification. |
| spellingShingle | Popescu, I Irving, B Borlotti, A Dall'Armellina, E Grau, V Popescu, I Myocardial scar quanti cation using SLIC supervoxels - Parcellation based on tissue characteristic strains |
| title | Myocardial scar quanti cation using SLIC supervoxels - Parcellation based on tissue characteristic strains |
| title_full | Myocardial scar quanti cation using SLIC supervoxels - Parcellation based on tissue characteristic strains |
| title_fullStr | Myocardial scar quanti cation using SLIC supervoxels - Parcellation based on tissue characteristic strains |
| title_full_unstemmed | Myocardial scar quanti cation using SLIC supervoxels - Parcellation based on tissue characteristic strains |
| title_short | Myocardial scar quanti cation using SLIC supervoxels - Parcellation based on tissue characteristic strains |
| title_sort | myocardial scar quanti cation using slic supervoxels parcellation based on tissue characteristic strains |
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