Combined tensor fitting and TV regularization in diffusion tensor imaging based on a Riemannian manifold approach
In this paper, we consider combined TV denoising and diffusion tensor fitting in DTI using the affine-invariant Riemannian metric on the space of diffusion tensors. Instead of first fitting the diffusion tensors, and then denoising them, we define a suitable TV type energy functional which incorpora...
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| Hauptverfasser: | , , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
April 27, 2016
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| In: |
IEEE transactions on medical imaging
Year: 2016, Jahrgang: 35, Heft: 8, Pages: 1972-1989 |
| ISSN: | 1558-254X |
| DOI: | 10.1109/TMI.2016.2528820 |
| Online-Zugang: | Verlag, Volltext: https://doi.org/10.1109/TMI.2016.2528820
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| Verfasserangaben: | Maximilian Baust, Andreas Weinmann, Matthias Wieczorek, Tobias Lasser, Martin Storath, and Nassir Navab |
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| 100 | 1 | |a Baust, Maximilian |e VerfasserIn |0 (DE-588)1208233858 |0 (DE-627)1694461211 |4 aut | |
| 245 | 1 | 0 | |a Combined tensor fitting and TV regularization in diffusion tensor imaging based on a Riemannian manifold approach |c Maximilian Baust, Andreas Weinmann, Matthias Wieczorek, Tobias Lasser, Martin Storath, and Nassir Navab |
| 264 | 1 | |c April 27, 2016 | |
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| 520 | |a In this paper, we consider combined TV denoising and diffusion tensor fitting in DTI using the affine-invariant Riemannian metric on the space of diffusion tensors. Instead of first fitting the diffusion tensors, and then denoising them, we define a suitable TV type energy functional which incorporates the measured DWIs (using an inverse problem setup) and which measures the nearness of neighboring tensors in the manifold. To approach this functional, we propose generalized forward- backward splitting algorithms which combine an explicit and several implicit steps performed on a decomposition of the functional. We validate the performance of the derived algorithms on synthetic and real DTI data. In particular, we work on real 3D data. To our knowledge, the present paper describes the first approach to TV regularization in a combined manifold and inverse problem setup. | ||
| 650 | 4 | |a affine-invariant Riemannian metric | |
| 650 | 4 | |a Algorithms | |
| 650 | 4 | |a biodiffusion | |
| 650 | 4 | |a biomedical MRI | |
| 650 | 4 | |a Combined denoising and diffusion tensor fitting | |
| 650 | 4 | |a combined tensor fitting | |
| 650 | 4 | |a Diffusion | |
| 650 | 4 | |a Diffusion Magnetic Resonance Imaging | |
| 650 | 4 | |a diffusion tensor imaging | |
| 650 | 4 | |a Diffusion tensor imaging | |
| 650 | 4 | |a Diffusion Tensor Imaging | |
| 650 | 4 | |a diffusion tensors | |
| 650 | 4 | |a DTI | |
| 650 | 4 | |a generalized forward- backward splitting algorithms | |
| 650 | 4 | |a generalized forward-backward algorithm | |
| 650 | 4 | |a Image reconstruction | |
| 650 | 4 | |a Imaging, Three-Dimensional | |
| 650 | 4 | |a inverse problem setup | |
| 650 | 4 | |a inverse problems | |
| 650 | 4 | |a manifold-valued data | |
| 650 | 4 | |a Manifolds | |
| 650 | 4 | |a Measurement | |
| 650 | 4 | |a medical image processing | |
| 650 | 4 | |a Noise reduction | |
| 650 | 4 | |a Riemannian manifold approach | |
| 650 | 4 | |a Tensile stress | |
| 650 | 4 | |a tensors | |
| 650 | 4 | |a total variation minimization | |
| 650 | 4 | |a TV | |
| 650 | 4 | |a TV denoising | |
| 650 | 4 | |a TV regularization | |
| 700 | 1 | |a Weinmann, Andreas |e VerfasserIn |0 (DE-588)1023236079 |0 (DE-627)717725316 |0 (DE-576)366549634 |4 aut | |
| 700 | 1 | |a Wieczorek, Matthias Valentin |d 1986- |e VerfasserIn |0 (DE-588)1124177604 |0 (DE-627)877874131 |0 (DE-576)482349115 |4 aut | |
| 700 | 1 | |a Lasser, Tobias |e VerfasserIn |4 aut | |
| 700 | 1 | |a Storath, Martin |e VerfasserIn |0 (DE-588)1036903818 |0 (DE-627)751410578 |0 (DE-576)389559830 |4 aut | |
| 700 | 1 | |a Navab, Nassir |d 1960- |e VerfasserIn |0 (DE-588)1018102558 |0 (DE-627)690516274 |0 (DE-576)354995502 |4 aut | |
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