A novel registration method of brain PET images and Talairach atlas

2017 ◽  
Author(s):  
Tan Wenjun ◽  
Kang Ying ◽  
Sun Xiaohan ◽  
Guo Chan
Keyword(s):  
Registration Method ◽  
Brain Pet ◽  
Talairach Atlas

scholarly journals F4-05-01: ASSOCIATIONS BETWEEN PLASMA NFL AND BRAIN PET IN ALZHEIMER'S DISEASE

2019 ◽  
Vol 15 ◽  
pp. P1224-P1225
Author(s):  
Andrea Lessa Benedet ◽  
Antoine Leuzy ◽  
Tharick A. Pascoal ◽  
Nicholas J. Ashton ◽  
Sulantha Mathotaarachchi ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Brain Pet

scholarly journals A High-Precision Registration Technology Based on Bundle Adjustment in Structured Light Scanning System

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Jianying Yuan ◽  
Qiong Wang ◽  
Xiaoliang Jiang ◽  
Bailin Li
Keyword(s):  
High Precision ◽  
Large Scale ◽  
Structured Light ◽  
Bundle Adjustment ◽  
Scanning System ◽  
Control Points ◽  
Registration Method ◽  
Global Control ◽  
3D Data ◽  
Structured Light Scanning

The multiview 3D data registration precision will decrease with the increasing number of registrations when measuring a large scale object using structured light scanning. In this paper, we propose a high-precision registration method based on multiple view geometry theory in order to solve this problem. First, a multiview network is constructed during the scanning process. The bundle adjustment method from digital close range photogrammetry is used to optimize the multiview network to obtain high-precision global control points. After that, the 3D data under each local coordinate of each scan are registered with the global control points. The method overcomes the error accumulation in the traditional registration process and reduces the time consumption of the following 3D data global optimization. The multiview 3D scan registration precision and efficiency are increased. Experiments verify the effectiveness of the proposed algorithm.

scholarly journals A Preprocess Method of External Disturbance Suppression for Carotid Wall Motion Estimation Using Local Phase and Orientation of B-Mode Ultrasound Sequences

2019 ◽  
Vol 2019 ◽  
pp. 1-15
Author(s):  
Qinghui Zhang ◽  
Junqiu Li ◽  
Zhenping Qiang ◽  
Libo He
Keyword(s):  
Motion Estimation ◽  
Wall Motion ◽  
Structural Information ◽  
External Disturbance ◽  
Estimation Accuracy ◽  
Ultrasound Images ◽  
Local Phase ◽  
Registration Method ◽  
Anisotropic Diffusion Filter ◽  
Disturbance Suppression

Estimating the motions of the common carotid artery wall plays a very important role in early diagnosis of the carotid atherosclerotic disease. However, the disturbances caused by either the instability of the probe operator or the breathing of subjects degrade the estimation accuracy of arterial wall motion when performing speckle tracking on the B-mode ultrasound images. In this paper, we propose a global registration method to suppress external disturbances before motion estimation. The local vector images, transformed from B-mode images, were used for registration. To take advantage of both the structural information from the local phase and the geometric information from the local orientation, we proposed a confidence coefficient to combine them two. Furthermore, we altered the speckle reducing anisotropic diffusion filter to improve the performance of disturbance suppression. We compared this method with schemes of extracting wall displacement directly from B-mode or phase images. The results show that this scheme can effectively suppress the disturbances and significantly improve the estimation accuracy.

scholarly journals Linear-Based Incremental Co-Registration of MLS and Photogrammetric Point Clouds

2021 ◽  
Vol 13 (11) ◽  
pp. 2195
Author(s):  
Shiming Li ◽  
Xuming Ge ◽  
Shengfu Li ◽  
Bo Xu ◽  
Zhendong Wang
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Point Clouds ◽  
Registration Method ◽  
Cloud Data ◽  
Linear Feature ◽  
Data Registration ◽  
Source Point ◽  
Urban Remote Sensing ◽  
Single Data

Today, mobile laser scanning and oblique photogrammetry are two standard urban remote sensing acquisition methods, and the cross-source point-cloud data obtained using these methods have significant differences and complementarity. Accurate co-registration can make up for the limitations of a single data source, but many existing registration methods face critical challenges. Therefore, in this paper, we propose a systematic incremental registration method that can successfully register MLS and photogrammetric point clouds in the presence of a large number of missing data, large variations in point density, and scale differences. The robustness of this method is due to its elimination of noise in the extracted linear features and its 2D incremental registration strategy. There are three main contributions of our work: (1) the development of an end-to-end automatic cross-source point-cloud registration method; (2) a way to effectively extract the linear feature and restore the scale; and (3) an incremental registration strategy that simplifies the complex registration process. The experimental results show that this method can successfully achieve cross-source data registration, while other methods have difficulty obtaining satisfactory registration results efficiently. Moreover, this method can be extended to more point-cloud sources.

scholarly journals Accurate Transmission-Less Attenuation Correction Method for Amyloid-β Brain PET Using Deep Neural Network

Electronics ◽  
2021 ◽  
Vol 10 (15) ◽  
pp. 1836
Author(s):  
Bo-Hye Choi ◽  
Donghwi Hwang ◽  
Seung-Kwan Kang ◽  
Kyeong-Yun Kim ◽  
Hongyoon Choi ◽  
...  
Keyword(s):  
Neural Network ◽  
Attenuation Correction ◽  
Complex Structure ◽  
Scatter Correction ◽  
Amyloid Β ◽  
Correction Method ◽  
Percentage Error ◽  
Similarity Coefficients ◽  
Brain Pet ◽  
Positron Emission

The lack of physically measured attenuation maps (μ-maps) for attenuation and scatter correction is an important technical challenge in brain-dedicated stand-alone positron emission tomography (PET) scanners. The accuracy of the calculated attenuation correction is limited by the nonuniformity of tissue composition due to pathologic conditions and the complex structure of facial bones. The aim of this study is to develop an accurate transmission-less attenuation correction method for amyloid-β (Aβ) brain PET studies. We investigated the validity of a deep convolutional neural network trained to produce a CT-derived μ-map (μ-CT) from simultaneously reconstructed activity and attenuation maps using the MLAA (maximum likelihood reconstruction of activity and attenuation) algorithm for Aβ brain PET. The performance of three different structures of U-net models (2D, 2.5D, and 3D) were compared. The U-net models generated less noisy and more uniform μ-maps than MLAA μ-maps. Among the three different U-net models, the patch-based 3D U-net model reduced noise and cross-talk artifacts more effectively. The Dice similarity coefficients between the μ-map generated using 3D U-net and μ-CT in bone and air segments were 0.83 and 0.67. All three U-net models showed better voxel-wise correlation of the μ-maps compared to MLAA. The patch-based 3D U-net model was the best. While the uptake value of MLAA yielded a high percentage error of 20% or more, the uptake value of 3D U-nets yielded the lowest percentage error within 5%. The proposed deep learning approach that requires no transmission data, anatomic image, or atlas/template for PET attenuation correction remarkably enhanced the quantitative accuracy of the simultaneously estimated MLAA μ-maps from Aβ brain PET.

scholarly journals SPPD: A Novel Reassembly Method for 3D Terracotta Warrior Fragments Based on Fracture Surface Information

2021 ◽  
Vol 10 (8) ◽  
pp. 525
Author(s):  
Wenmin Yao ◽  
Tong Chu ◽  
Wenlong Tang ◽  
Jingyu Wang ◽  
Xin Cao ◽  
...  
Keyword(s):  
Fracture Surface ◽  
Principal Component ◽  
Point Clouds ◽  
Three Dimension ◽  
Registration Method ◽  
Series Of Experiments ◽  
Coarse To Fine ◽  
Fine Registration ◽  
Common Situation ◽  
Better Than

As one of China′s most precious cultural relics, the excavation and protection of the Terracotta Warriors pose significant challenges to archaeologists. A fairly common situation in the excavation is that the Terracotta Warriors are mostly found in the form of fragments, and manual reassembly among numerous fragments is laborious and time-consuming. This work presents a fracture-surface-based reassembling method, which is composed of SiamesePointNet, principal component analysis (PCA), and deep closest point (DCP), and is named SPPD. Firstly, SiamesePointNet is proposed to determine whether a pair of point clouds of 3D Terracotta Warrior fragments can be reassembled. Then, a coarse-to-fine registration method based on PCA and DCP is proposed to register the two fragments into a reassembled one. The above two steps iterate until the termination condition is met. A series of experiments on real-world examples are conducted, and the results demonstrate that the proposed method performs better than the conventional reassembling methods. We hope this work can provide a valuable tool for the virtual restoration of three-dimension cultural heritage artifacts.

scholarly journals Automated Data Quality Control in FDOPA brain PET Imaging using Deep Learning

2021 ◽  
pp. 106239
Author(s):  
Antonella D. Pontoriero ◽  
Giovanna Nordio ◽  
Rubaida Easmin ◽  
Alessio Giacomel ◽  
Barbara Santangelo ◽  
...  
Keyword(s):  
Quality Control ◽  
Deep Learning ◽  
Data Quality ◽  
Pet Imaging ◽  
Data Quality Control ◽  
Brain Pet

scholarly journals About the source and consequences of 18F-FDG brain PET hypometabolism in short and long COVID-19

2021 ◽  
Author(s):  
Igor C. Fontana ◽  
Débora Guerini Souza ◽  
Luc Pellerin ◽  
Diogo O. Souza ◽  
Eduardo R. Zimmer
Keyword(s):  
Brain Pet ◽  
18F Fdg
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