Diffusion rpca #232
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Diffusion rpca #232
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added 11 commits
November 17, 2025 17:03
nathanpainchaud
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Nov 18, 2025
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Il faudrait ignorer les changements fait au Gemfile.lock. @juliapg si tu hésites comment faire ça avec Git, n'hésites pas à venir me voir.
Celia-Gjt
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Nov 19, 2025
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Celia-Gjt
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Celia-Gjt
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Thank you for your great review, I've just corrected some typos.
| - **Harmonic imaging** receives echoes at frequencies that are multiples of the original frequency. It produces higher quality images, as multipath scatterers have less energy and therefore generate fewer harmonics. However, it results in reduced penetration depth. | ||
| - **Clutter filtering methods.** | ||
| - Block-matching and 3D filtering algorithm (BM3D) works by grouping similar patches of the image and then stacking and filtering them. It needs assumptions on the noise distribution. | ||
| - Temporal decompositions (PCA, SVD) allow to separate data correspoding to rapidly moving events (tissue) from data corresponding to stationary events (clutter). This assumption is not always true, leading to mistakes. |
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Typo: correspoding = corresponding
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| - Nuclear diffusion acheives a better contrast while better preserving tissue distribution. |
nathanpainchaud
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Nov 20, 2025
| - *L* is low-rank because of the spatial coherence of background signal | ||
| - *X* can be considered sparse because tissue is not everywhere in the sector | ||
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| The decomposition of a matrix in low-rank and sparse components is called **Robust Principal Component Analysis** (RPCA) [[1]](https://arxiv.org/abs/0912.3599) and *L* and *S* can be found solving a convex minimization problem. |
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À corriger (selon ton commentaire lors de la présentation):
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| The decomposition of a matrix in low-rank and sparse components is called **Robust Principal Component Analysis** (RPCA) [[1]](https://arxiv.org/abs/0912.3599) and *L* and *S* can be found solving a convex minimization problem. | |
| The decomposition of a matrix in low-rank and sparse components is called **Robust Principal Component Analysis** (RPCA) [[1]](https://arxiv.org/abs/0912.3599) and *L* and *X* can be found solving a convex minimization problem. |
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