Literature DB >> 26422177

Region specific optimization of continuous linear attenuation coefficients based on UTE (RESOLUTE): application to PET/MR brain imaging.

Claes N Ladefoged1, Didier Benoit, Ian Law, Søren Holm, Andreas Kjær, Liselotte Højgaard, Adam E Hansen, Flemming L Andersen.   

Abstract

The reconstruction of PET brain data in a PET/MR hybrid scanner is challenging in the absence of transmission sources, where MR images are used for MR-based attenuation correction (MR-AC). The main challenge of MR-AC is to separate bone and air, as neither have a signal in traditional MR images, and to assign the correct linear attenuation coefficient to bone. The ultra-short echo time (UTE) MR sequence was proposed as a basis for MR-AC as this sequence shows a small signal in bone. The purpose of this study was to develop a new clinically feasible MR-AC method with patient specific continuous-valued linear attenuation coefficients in bone that provides accurate reconstructed PET image data. A total of 164 [(18)F]FDG PET/MR patients were included in this study, of which 10 were used for training. MR-AC was based on either standard CT (reference), UTE or our method (RESOLUTE). The reconstructed PET images were evaluated in the whole brain, as well as regionally in the brain using a ROI-based analysis. Our method segments air, brain, cerebral spinal fluid, and soft tissue voxels on the unprocessed UTE TE images, and uses a mapping of R(*)2 values to CT Hounsfield Units (HU) to measure the density in bone voxels. The average error of our method in the brain was 0.1% and less than 1.2% in any region of the brain. On average 95% of the brain was within  ±10% of PETCT, compared to 72% when using UTE. The proposed method is clinically feasible, reducing both the global and local errors on the reconstructed PET images, as well as limiting the number and extent of the outliers.

Entities:  

Mesh:

Substances:

Year:  2015        PMID: 26422177     DOI: 10.1088/0031-9155/60/20/8047

Source DB:  PubMed          Journal:  Phys Med Biol        ISSN: 0031-9155            Impact factor:   3.609


  43 in total

1.  Zero-Echo-Time and Dixon Deep Pseudo-CT (ZeDD CT): Direct Generation of Pseudo-CT Images for Pelvic PET/MRI Attenuation Correction Using Deep Convolutional Neural Networks with Multiparametric MRI.

Authors:  Andrew P Leynes; Jaewon Yang; Florian Wiesinger; Sandeep S Kaushik; Dattesh D Shanbhag; Youngho Seo; Thomas A Hope; Peder E Z Larson
Journal:  J Nucl Med       Date:  2017-10-30       Impact factor: 10.057

2.  Attenuation correction for brain PET imaging using deep neural network based on Dixon and ZTE MR images.

Authors:  Kuang Gong; Jaewon Yang; Kyungsang Kim; Georges El Fakhri; Youngho Seo; Quanzheng Li
Journal:  Phys Med Biol       Date:  2018-06-13       Impact factor: 3.609

3.  Evaluation of Sinus/Edge-Corrected Zero-Echo-Time-Based Attenuation Correction in Brain PET/MRI.

Authors:  Jaewon Yang; Florian Wiesinger; Sandeep Kaushik; Dattesh Shanbhag; Thomas A Hope; Peder E Z Larson; Youngho Seo
Journal:  J Nucl Med       Date:  2017-05-04       Impact factor: 10.057

Review 4.  From simultaneous to synergistic MR-PET brain imaging: A review of hybrid MR-PET imaging methodologies.

Authors:  Zhaolin Chen; Sharna D Jamadar; Shenpeng Li; Francesco Sforazzini; Jakub Baran; Nicholas Ferris; Nadim Jon Shah; Gary F Egan
Journal:  Hum Brain Mapp       Date:  2018-08-04       Impact factor: 5.038

5.  Magnetic resonance imaging-based pseudo computed tomography using anatomic signature and joint dictionary learning.

Authors:  Yang Lei; Hui-Kuo Shu; Sibo Tian; Jiwoong Jason Jeong; Tian Liu; Hyunsuk Shim; Hui Mao; Tonghe Wang; Ashesh B Jani; Walter J Curran; Xiaofeng Yang
Journal:  J Med Imaging (Bellingham)       Date:  2018-08-24

Review 6.  Applications of artificial intelligence in nuclear medicine image generation.

Authors:  Zhibiao Cheng; Junhai Wen; Gang Huang; Jianhua Yan
Journal:  Quant Imaging Med Surg       Date:  2021-06

7.  MR-based PET attenuation correction using a combined ultrashort echo time/multi-echo Dixon acquisition.

Authors:  Paul Kyu Han; Debra E Horng; Kuang Gong; Yoann Petibon; Kyungsang Kim; Quanzheng Li; Keith A Johnson; Georges El Fakhri; Jinsong Ouyang; Chao Ma
Journal:  Med Phys       Date:  2020-05-11       Impact factor: 4.071

8.  Deep Learning MR Imaging-based Attenuation Correction for PET/MR Imaging.

Authors:  Fang Liu; Hyungseok Jang; Richard Kijowski; Tyler Bradshaw; Alan B McMillan
Journal:  Radiology       Date:  2017-09-19       Impact factor: 11.105

9.  Generalization of deep learning models for ultra-low-count amyloid PET/MRI using transfer learning.

Authors:  Kevin T Chen; Matti Schürer; Jiahong Ouyang; Mary Ellen I Koran; Guido Davidzon; Elizabeth Mormino; Solveig Tiepolt; Karl-Titus Hoffmann; Osama Sabri; Greg Zaharchuk; Henryk Barthel
Journal:  Eur J Nucl Med Mol Imaging       Date:  2020-06-13       Impact factor: 9.236

10.  On the accuracy and reproducibility of a novel probabilistic atlas-based generation for calculation of head attenuation maps on integrated PET/MR scanners.

Authors:  Kevin T Chen; David Izquierdo-Garcia; Clare B Poynton; Daniel B Chonde; Ciprian Catana
Journal:  Eur J Nucl Med Mol Imaging       Date:  2016-08-29       Impact factor: 9.236

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.