Deep learning-based PET image denoising and reconstruction: a review
This review focuses on positron emission tomography (PET) imaging algorithms and traces
the evolution of PET image reconstruction methods. First, we provide an overview of …
the evolution of PET image reconstruction methods. First, we provide an overview of …
Self-supervised pre-training for deep image prior-based robust pet image denoising
Deep image prior (DIP) has been successfully applied to positron emission tomography
(PET) image restoration, enabling represent implicit prior using only convolutional neural …
(PET) image restoration, enabling represent implicit prior using only convolutional neural …
ReconU-Net: a direct PET image reconstruction using U-Net architecture with back projection-induced skip connection
Objective. This study aims to introduce a novel back projection-induced U-Net-shaped
architecture, called ReconU-Net, based on the original U-Net architecture for deep learning …
architecture, called ReconU-Net, based on the original U-Net architecture for deep learning …
Enhanced PET imaging using progressive conditional deep image prior
J Li, C **, H Dai, J Wang, Y Lv, P Zhang… - Physics in Medicine & …, 2023 - iopscience.iop.org
Objective. Unsupervised learning-based methods have been proven to be an effective way
to improve the image quality of positron emission tomography (PET) images when a large …
to improve the image quality of positron emission tomography (PET) images when a large …
[HTML][HTML] A hybrid deep image prior and compressed sensing reconstruction method for highly accelerated 3D coronary magnetic resonance angiography
Z Xue, S Zhu, F Yang, J Gao, H Peng, C Zou… - Frontiers in …, 2024 - frontiersin.org
Introduction High-resolution whole-heart coronary magnetic resonance angiography
(CMRA) often suffers from unreasonably long scan times, rendering imaging acceleration …
(CMRA) often suffers from unreasonably long scan times, rendering imaging acceleration …
Two-step optimization for accelerating deep image prior-based PET image reconstruction
Deep learning, particularly convolutional neural networks (CNNs), has advanced positron
emission tomography (PET) image reconstruction. However, it requires extensive, high …
emission tomography (PET) image reconstruction. However, it requires extensive, high …
Deep Volume Reconstruction from Multi-focus Microscopic Images
Reconstructing 3D volumes from optical microscopic images is useful in important areas
such as cellular analysis, cancer research, and drug development. However, existing …
such as cellular analysis, cancer research, and drug development. However, existing …
Deep learning PET 画像再構成への招待
橋本二三生， 大西佑弥， 大手希望 - Medical Imaging Technology, 2023 - jstage.jst.go.jp
Deep learning PET 画像再構成への招待 Page 1 162 MEDICAL IMAGING TECHNOLOGY Vol.
41 No. 4-5 September-November 2023 サーベイ論文 Deep learning PET 画像再構成への招待 …
41 No. 4-5 September-November 2023 サーベイ論文 Deep learning PET 画像再構成への招待 …
3D PET-DIP reconstruction with relative difference prior using a SIRF-based objective
Deep Image Prior (DIP) is an unsupervised deep learning technique that does not require
ground truth images. For the first time, 3D PET reconstruction with DIP is cast as a single …
ground truth images. For the first time, 3D PET reconstruction with DIP is cast as a single …
A New MLEM Reconstruction Algorithm for Ultra-low Dose PET
R Cierniak - International Conference on Computational Collective …, 2024 - Springer
This study introduces a novel ML-EM estimation method for reconstructing images in
positron emission tomography. The concept proposed here utilizes a continuous-to …
positron emission tomography. The concept proposed here utilizes a continuous-to …