Literature DB >> 23744678

Incorporating anatomical side information into PET reconstruction using nonlocal regularization.

Van-Giang Nguyen1, Soo-Jin Lee.   

Abstract

With the introduction of combined positron emission tomography (PET)/computed tomography (CT) or PET/magnetic resonance imaging (MRI) scanners, there is an increasing emphasis on reconstructing PET images with the aid of the anatomical side information obtained from X-ray CT or MRI scanners. In this paper, we propose a new approach to incorporating prior anatomical information into PET reconstruction using the nonlocal regularization method. The nonlocal regularizer developed for this application is designed to selectively consider the anatomical information only when it is reliable. As our proposed nonlocal regularization method does not directly use anatomical edges or boundaries which are often used in conventional methods, it is not only free from additional processes to extract anatomical boundaries or segmented regions, but also more robust to the signal mismatch problem that is caused by the indirect relationship between the PET image and the anatomical image. We perform simulations with digital phantoms. According to our experimental results, compared to the conventional method based on the traditional local regularization method, our nonlocal regularization method performs well even with the imperfect prior anatomical information or in the presence of signal mismatch between the PET image and the anatomical image.

Mesh:

Year:  2013        PMID: 23744678     DOI: 10.1109/TIP.2013.2265881

Source DB:  PubMed          Journal:  IEEE Trans Image Process        ISSN: 1057-7149            Impact factor:   10.856


  12 in total

1.  Anatomy-guided brain PET imaging incorporating a joint prior model.

Authors:  Lijun Lu; Jianhua Ma; Qianjin Feng; Wufan Chen; Arman Rahmim
Journal:  Phys Med Biol       Date:  2015-02-16       Impact factor: 3.609

2.  Alternating direction method of multiplier for tomography with nonlocal regularizers.

Authors:  Se Young Chun; Yuni K Dewaraja; Jeffrey A Fessler
Journal:  IEEE Trans Med Imaging       Date:  2014-10       Impact factor: 10.048

3.  Enhancement of Partial Volume Correction in MR-Guided PET Image Reconstruction by Using MRI Voxel Sizes.

Authors:  Martin A Belzunce; Abolfazl Mehranian; Andrew J Reader
Journal:  IEEE Trans Radiat Plasma Med Sci       Date:  2018-11-15

4.  Deep Auto-context Convolutional Neural Networks for Standard-Dose PET Image Estimation from Low-Dose PET/MRI.

Authors:  Lei Xiang; Yu Qiao; Dong Nie; Le An; Qian Wang; Dinggang Shen
Journal:  Neurocomputing       Date:  2017-06-29       Impact factor: 5.719

5.  PET image reconstruction using kernel method.

Authors:  Guobao Wang; Jinyi Qi
Journal:  IEEE Trans Med Imaging       Date:  2014-07-30       Impact factor: 10.048

6.  Multi-Level Canonical Correlation Analysis for Standard-Dose PET Image Estimation.

Authors:  Ehsan Adeli; David S Lalush
Journal:  IEEE Trans Image Process       Date:  2016-05-11       Impact factor: 10.856

7.  Anatomically-aided PET reconstruction using the kernel method.

Authors:  Will Hutchcroft; Guobao Wang; Kevin T Chen; Ciprian Catana; Jinyi Qi
Journal:  Phys Med Biol       Date:  2016-08-19       Impact factor: 3.609

8.  Direct Patlak Reconstruction From Dynamic PET Data Using the Kernel Method With MRI Information Based on Structural Similarity.

Authors:  Kuang Gong; Jinxiu Cheng-Liao; Guobao Wang; Kevin T Chen; Ciprian Catana; Jinyi Qi
Journal:  IEEE Trans Med Imaging       Date:  2018-04       Impact factor: 10.048

9.  Multi-Tracer Guided PET Image Reconstruction.

Authors:  Sam Ellis; Andrew Mallia; Colm J McGinnity; Gary J R Cook; Andrew J Reader
Journal:  IEEE Trans Radiat Plasma Med Sci       Date:  2018-07-23

10.  Reconstruction of Positron Emission Tomography Images Using Gaussian Curvature.

Authors:  Jose Mejia; Boris Mederos; Jie Zhao; Leticia Ortega; Nelly Gordillo
Journal:  J Healthc Eng       Date:  2018-11-15       Impact factor: 2.682

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