Traction
发表于 2025-3-21 16:13:47
书目名称Medical Image Computing and Computer Assisted Intervention – MICCAI 2023影响因子(影响力)<br> http://impactfactor.cn/if/?ISSN=BK0629220<br><br> <br><br>书目名称Medical Image Computing and Computer Assisted Intervention – MICCAI 2023影响因子(影响力)学科排名<br> http://impactfactor.cn/ifr/?ISSN=BK0629220<br><br> <br><br>书目名称Medical Image Computing and Computer Assisted Intervention – MICCAI 2023网络公开度<br> http://impactfactor.cn/at/?ISSN=BK0629220<br><br> <br><br>书目名称Medical Image Computing and Computer Assisted Intervention – MICCAI 2023网络公开度学科排名<br> http://impactfactor.cn/atr/?ISSN=BK0629220<br><br> <br><br>书目名称Medical Image Computing and Computer Assisted Intervention – MICCAI 2023被引频次<br> http://impactfactor.cn/tc/?ISSN=BK0629220<br><br> <br><br>书目名称Medical Image Computing and Computer Assisted Intervention – MICCAI 2023被引频次学科排名<br> http://impactfactor.cn/tcr/?ISSN=BK0629220<br><br> <br><br>书目名称Medical Image Computing and Computer Assisted Intervention – MICCAI 2023年度引用<br> http://impactfactor.cn/ii/?ISSN=BK0629220<br><br> <br><br>书目名称Medical Image Computing and Computer Assisted Intervention – MICCAI 2023年度引用学科排名<br> http://impactfactor.cn/iir/?ISSN=BK0629220<br><br> <br><br>书目名称Medical Image Computing and Computer Assisted Intervention – MICCAI 2023读者反馈<br> http://impactfactor.cn/5y/?ISSN=BK0629220<br><br> <br><br>书目名称Medical Image Computing and Computer Assisted Intervention – MICCAI 2023读者反馈学科排名<br> http://impactfactor.cn/5yr/?ISSN=BK0629220<br><br> <br><br>
Cerumen
发表于 2025-3-21 22:04:48
978-3-031-43998-8The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
NAUT
发表于 2025-3-22 02:54:34
Medical Image Computing and Computer Assisted Intervention – MICCAI 2023978-3-031-43999-5Series ISSN 0302-9743 Series E-ISSN 1611-3349
Canvas
发表于 2025-3-22 06:25:11
http://reply.papertrans.cn/63/6293/629220/629220_4.png
美学
发表于 2025-3-22 11:17:49
Learned Alternating Minimization Algorithm for Dual-Domain Sparse-View CT Reconstructioncture into the design of LAMA. We show that LAMA substantially reduces network complexity, improves memory efficiency and reconstruction accuracy, and is provably convergent for reliable reconstructions. Extensive numerical experiments demonstrate that LAMA outperforms existing methods by a wide margin on multiple benchmark CT datasets.
Cupping
发表于 2025-3-22 15:10:00
http://reply.papertrans.cn/63/6293/629220/629220_6.png
Bravura
发表于 2025-3-22 17:05:50
http://reply.papertrans.cn/63/6293/629220/629220_7.png
acolyte
发表于 2025-3-22 22:01:52
http://reply.papertrans.cn/63/6293/629220/629220_8.png
伴随而来
发表于 2025-3-23 03:46:26
CDiffMR: Can We Replace the Gaussian Noise with K-Space Undersampling for Fast MRI?can be reused for reconstruction tasks with different undersampling rates. We demonstrated, through extensive numerical and visual experiments, that the proposed CDiffMR can achieve comparable or even superior reconstruction results than state-of-the-art models. Compared to the diffusion model-based
HAUNT
发表于 2025-3-23 07:15:46
Learning Deep Intensity Field for Extremely Sparse-View CBCT Reconstructionated by a fusion module for intensity estimation. Notably, thousands of points can be processed in parallel to improve efficiency during training and testing. In practice, we collect a knee CBCT dataset to train and evaluate DIF-Net. Extensive experiments show that our approach can reconstruct CBCT