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Anatomically and metabolically informed diffusion for unified denoising and segmentation in low-count PET imaging

delete2025-10-06
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PRE
AI
M
Menghua Xia
K
Kuan‐Yin Ko
D
Der-Shiun Wang
M
Ming-Kai Chen
Q
Qiong Liu
H
Huidong Xie
L
Liang Guo
W
Wei Ji
J
Jinsong Ouyang
R
Reimund Bayerlein
B
Benjamin A. Spencer
Q
Quanzheng Li
R
Ramsey D. Badawi
G
Georges El Fakhri
C
Chi Liu *
DOI:10.1016/j.media.2025.103831delete
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Abstract

Abstract

En 中文
• Developing an anatomically and metabolically informed diffusion model for low-count PET analysis, enabling clinical metric quantification directly from low-count inputs. • Exploring the synergies between denoising and lesion/organ segmentation in PET imaging. • Conducting extensive experiments on multi-vendor, multi-center, and multi-noise-level datasets.

Journal

Medical Image Analysis cover
Medical Image Analysis
IF:
11.8
Papers:
3.8K
Citations:
2.4W

Organization

T
Tri-Service General Hospital
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3.7K
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Y
Yale University
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6.5W
Papers: 6.0W
Citations: 10.0W
U
university of california davis
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3.3W
Papers: 2.6W
Citations: 45
Y
Yale University School of Medicine
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1.1K
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N
national taiwan university cancer center
Scholars:
64
Papers: 44
Citations: 0
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