Výsledky vyhledávání

Report

Deep unrolled primal dual network for TOF-PET list-mode image reconstruction

Autor: Hu, Rui, Li, Chenxu, Tian, Kun, Cui, Jianan, Chen, Yunmei, Liu, Huafeng

Time-of-flight (TOF) information provides more accurate location data for annihilation photons, thereby enhancing the quality of PET reconstruction images and reducing noise. List-mode reconstruction has a significant advantage in handling TOF inform

Externí odkaz: http://arxiv.org/abs/2410.11148

Zobrazit plný text záznamu

Report

Anatomy-guided fiber trajectory distribution estimation for cranial nerves tractography

Autor: Xie, Lei, Zeng, Qingrun, Zhou, Huajun, Xie, Guoqiang, Li, Mingchu, Huang, Jiahao, Cui, Jianan, Chen, Hao, Feng, Yuanjing

Diffusion MRI tractography is an important tool for identifying and analyzing the intracranial course of cranial nerves (CNs). However, the complex environment of the skull base leads to ambiguous spatial correspondence between diffusion directions a

Externí odkaz: http://arxiv.org/abs/2402.18856

Zobrazit plný text záznamu

Report

STPDnet: Spatial-temporal convolutional primal dual network for dynamic PET image reconstruction

Autor: Hu, Rui, Cui, Jianan, Yu, Chengjin, Chen, Yunmei, Liu, Huafeng

Dynamic positron emission tomography (dPET) image reconstruction is extremely challenging due to the limited counts received in individual frame. In this paper, we propose a spatial-temporal convolutional primal dual network (STPDnet) for dynamic PET

Externí odkaz: http://arxiv.org/abs/2303.04667

Zobrazit plný text záznamu

Report

LMPDNet: TOF-PET list-mode image reconstruction using model-based deep learning method

Autor: Li, Chenxu, Hu, Rui, Cui, Jianan, Liu, Huafeng

The integration of Time-of-Flight (TOF) information in the reconstruction process of Positron Emission Tomography (PET) yields improved image properties. However, implementing the cutting-edge model-based deep learning methods for TOF-PET reconstruct

Externí odkaz: http://arxiv.org/abs/2302.10481

Zobrazit plný text záznamu

Report

A Noise-level-aware Framework for PET Image Denoising

Autor: Li, Ye, Cui, Jianan, Chen, Junyu, Zeng, Guodong, Wollenweber, Scott, Jansen, Floris, Jang, Se-In, Kim, Kyungsang, Gong, Kuang, Li, Quanzheng

In PET, the amount of relative (signal-dependent) noise present in different body regions can be significantly different and is inherently related to the number of counts present in that region. The number of counts in a region depends, in principle

Externí odkaz: http://arxiv.org/abs/2203.08034

Zobrazit plný text záznamu

Report

Super Resolution of Arterial Spin Labeling MR Imaging Using Unsupervised Multi-Scale Generative Adversarial Network

Autor: Cui, Jianan, Gong, Kuang, Han, Paul, Liu, Huafeng, Li, Quanzheng

Arterial spin labeling (ASL) magnetic resonance imaging (MRI) is a powerful imaging technology that can measure cerebral blood flow (CBF) quantitatively. However, since only a small portion of blood is labeled compared to the whole tissue volume, con

Externí odkaz: http://arxiv.org/abs/2009.06129

Zobrazit plný text záznamu

Report

Clinically Translatable Direct Patlak Reconstruction from Dynamic PET with Motion Correction Using Convolutional Neural Network

Autor: Xie, Nuobei, Gong, Kuang, Guo, Ning, Qin, Zhixing, Cui, Jianan, Wu, Zhifang, Liu, Huafeng, Li, Quanzheng

Patlak model is widely used in 18F-FDG dynamic positron emission tomography (PET) imaging, where the estimated parametric images reveal important biochemical and physiology information. Because of better noise modeling and more information extracted

Externí odkaz: http://arxiv.org/abs/2009.05901

Zobrazit plný text záznamu