Measuring sparse temporal-variation for accurate registration of dynamic contrast-enhanced breast MR images
• Propose new techniques for robust registration of DCE-MR breast image. • Treat the temporal variation of DCE-MR breast images as sparse “corruptions”. • Model DCE-MR breast image similarity with a L 1 norm and a Lorentzian. • Propose an iteratively reweighted least squares for optimization. • Prop...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Zheng, Yuanjie [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2015 |
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| Schlagwörter: |
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| Umfang: |
8 |
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| Übergeordnetes Werk: |
Enthalten in: Synthesis of terraced and spherical MgO nanoparticles using flame metal combustion - Chae, Sukbyung ELSEVIER, 2017transfer abstract, the international journal on imaging and image-archiving in all medical specialties : the official journal of the Computerized Medical Image Society, Amsterdam [u.a.] |
|---|---|
| Übergeordnetes Werk: |
volume:46 ; year:2015 ; pages:73-80 ; extent:8 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.compmedimag.2015.05.004 |
|---|
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ELV034649387 |
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Measuring sparse temporal-variation for accurate registration of dynamic contrast-enhanced breast MR images |
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• Propose new techniques for robust registration of DCE-MR breast image. • Treat the temporal variation of DCE-MR breast images as sparse “corruptions”. • Model DCE-MR breast image similarity with a L 1 norm and a Lorentzian. • Propose an iteratively reweighted least squares for optimization. • Propose a linear programming based technique for optimization. |
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• Propose new techniques for robust registration of DCE-MR breast image. • Treat the temporal variation of DCE-MR breast images as sparse “corruptions”. • Model DCE-MR breast image similarity with a L 1 norm and a Lorentzian. • Propose an iteratively reweighted least squares for optimization. • Propose a linear programming based technique for optimization. |
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• Propose new techniques for robust registration of DCE-MR breast image. • Treat the temporal variation of DCE-MR breast images as sparse “corruptions”. • Model DCE-MR breast image similarity with a L 1 norm and a Lorentzian. • Propose an iteratively reweighted least squares for optimization. • Propose a linear programming based technique for optimization. |
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