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Titlebook: Medical Image Computing and Computer-Assisted Intervention - MICCAI 2011; 14th International C Gabor Fichtinger,Anne Martel,Terry Peters Co

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樓主: Opiate
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發(fā)表于 2025-3-25 05:55:22 | 只看該作者
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發(fā)表于 2025-3-25 08:51:28 | 只看該作者
Thomas Schultzre developed and introduced into medical practice. The authors,both of whom have considerable experience in the performance and interpretation of PET-CT studies with FDG, have made an important contri- tion tha978-1-4419-1927-4978-0-387-27044-9
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發(fā)表于 2025-3-25 13:55:19 | 只看該作者
Karim Lekadir,Babak Ghafaryasl,Emma Mu?oz-Moreno,Constantine Butakoff,Corné Hoogendoorn,Alejandro F.
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發(fā)表于 2025-3-25 20:37:54 | 只看該作者
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發(fā)表于 2025-3-26 01:43:03 | 只看該作者
Carolyn B. Lauzon,Andrew J. Asman,Ciprian Crainiceanu,Brian C. Caffo,Bennett A. Landmanitioners, radiologists, and residents, as well as referring clinicians interested in learning more about how this imaging modality can be applied in their patient populations.?.?.Peter S. Conti is a Professor of Radiology and the Director of the PET Imaging Science Center at the University of Southe
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發(fā)表于 2025-3-26 04:22:07 | 只看該作者
28#
發(fā)表于 2025-3-26 11:34:14 | 只看該作者
Benoit Scherrer,Ali Gholipour,Simon K. Warfieldtood the test of time for more than a hundred years and is still valid as new modalities are developed and introduced into medical practice. The authors,both of whom have considerable experience in the performance and interpretation of PET-CT studies with FDG, have made an important contri- tion tha
29#
發(fā)表于 2025-3-26 14:42:59 | 只看該作者
Bao Ge,Lei Guo,Jinglei Lv,Xintao Hu,Junwei Han,Tuo Zhang,Tianming Liutood the test of time for more than a hundred years and is still valid as new modalities are developed and introduced into medical practice. The authors,both of whom have considerable experience in the performance and interpretation of PET-CT studies with FDG, have made an important contri- tion tha
30#
發(fā)表于 2025-3-26 19:39:24 | 只看該作者
Fiber Modeling and Clustering Based on Neuroanatomical Featuresrs, we model the fibers as observations sampled from multivariate Gaussian mixtures in the feature space. An expectation-maximization clustering approach is then employed to group the fibers into 16 major bundles. Experimental results indicate that the proposed method groups the fibers into anatomic
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