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200x Low-dose PET Reconstruction using Deep Learning

200x Low-dose PET Reconstruction using Deep Learning

12 December 2017
Junshen Xu
Enhao Gong
John M. Pauly
Greg Zaharchuk
    MedIm
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Papers citing "200x Low-dose PET Reconstruction using Deep Learning"

6 / 6 papers shown
Title
Contrastive Diffusion Model with Auxiliary Guidance for Coarse-to-Fine
  PET Reconstruction
Contrastive Diffusion Model with Auxiliary Guidance for Coarse-to-Fine PET Reconstruction
Zeyu Han
Yuhan Wang
Luping Zhou
Peng Wang
Binyu Yan
Jiliu Zhou
Yan Wang
Dinggang Shen
DiffM
19
22
0
20 Aug 2023
Deformed2Self: Self-Supervised Denoising for Dynamic Medical Imaging
Deformed2Self: Self-Supervised Denoising for Dynamic Medical Imaging
Junshen Xu
E. Adalsteinsson
MedIm
11
30
0
23 Jun 2021
A review of deep learning in medical imaging: Imaging traits, technology
  trends, case studies with progress highlights, and future promises
A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises
S. Kevin Zhou
H. Greenspan
Christos Davatzikos
James S. Duncan
Bram van Ginneken
A. Madabhushi
Jerry L. Prince
Daniel Rueckert
Ronald M. Summers
38
624
0
02 Aug 2020
Penalized-likelihood PET Image Reconstruction Using 3D Structural
  Convolutional Sparse Coding
Penalized-likelihood PET Image Reconstruction Using 3D Structural Convolutional Sparse Coding
Nuobei Xie
Kuang Gong
Ning Guo
ZhiXing Qin
Zhifang Wu
Huafeng Liu
Quanzheng Li
3DV
3DPC
MedIm
19
20
0
16 Dec 2019
DirectPET: Full Size Neural Network PET Reconstruction from Sinogram
  Data
DirectPET: Full Size Neural Network PET Reconstruction from Sinogram Data
W. Whiteley
W. K. Luk
J. Gregor
3DV
AI4TS
13
54
0
19 Aug 2019
Joint Correction of Attenuation and Scatter Using Deep Convolutional
  Neural Networks (DCNN) for Time-of-Flight PET
Joint Correction of Attenuation and Scatter Using Deep Convolutional Neural Networks (DCNN) for Time-of-Flight PET
Jaewon Yang
Dookun Park
J. Sohn
Z. J. Wang
G. Gullberg
Youngho Seo
13
68
0
28 Nov 2018
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