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Titlebook: Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2012; 15th International C Nicholas Ayache,Hervé Delingette,Kensaku Mo

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發(fā)表于 2025-3-23 13:39:38 | 只看該作者
Ender Konukoglu,Ben Glocker,Darko Zikic,Antonio Criminisit photoreceptors from their bacterial ancestors to algae, fePlants as sessile organisms have evolved fascinating capacities to adapt to changes in their natural environment. Arguably, light is by far the most important and variable environmental factor. The quality, quantity, direction and duration
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發(fā)表于 2025-3-23 16:43:03 | 只看該作者
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發(fā)表于 2025-3-23 21:35:03 | 只看該作者
Accelerated Diffusion Spectrum Imaging with Compressed Sensing Using Adaptive Dictionariesg imaging times (~1 hour). It is possible to accelerate DSI by sub-Nyquist sampling of the .-space followed by nonlinear reconstruction to estimate the diffusion probability density functions (pdfs). Recent work by Menzel et al. imposed sparsity constraints on the pdfs under wavelet and Total Variat
14#
發(fā)表于 2025-3-23 22:53:48 | 只看該作者
Parametric Dictionary Learning for Modeling EAP and ODF in Diffusion MRIfrom a limited number of samples while efficiently recovering important diffusion features such as the Ensemble Average Propagator (EAP) and the Orientation Distribution Function (ODF). Some attempts to sparsely represent the diffusion signal have already been performed. However and contrarly to wha
15#
發(fā)表于 2025-3-24 03:27:01 | 只看該作者
Resolution Enhancement of Diffusion-Weighted Images by Local Fiber Profilingnal-to-noise ratio (SNR). This paper describes an algorithm that will increase the resolution of DW images beyond the scan resolution, allowing for a closer investigation of fiber structures and more accurate assessment of brain connectivity. The algorithm is capable of generating a dense vector-val
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發(fā)表于 2025-3-24 06:42:28 | 只看該作者
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發(fā)表于 2025-3-24 12:36:58 | 只看該作者
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發(fā)表于 2025-3-24 18:39:49 | 只看該作者
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發(fā)表于 2025-3-24 20:47:37 | 只看該作者
Incorporating Parameter Uncertainty in Bayesian Segmentation Models: Application to Hippocampal Subfion. However, these methods typically have many free parameters that are estimated to obtain point estimates only, whereas a faithful Bayesian analysis would also consider all possible alternate values these parameters may take. In this paper, we propose to incorporate the uncertainty of the free pa
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發(fā)表于 2025-3-24 23:57:39 | 只看該作者
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