Literature DB >> 28234560

Quantitative Evaluation of Atlas-based Attenuation Correction for Brain PET in an Integrated Time-of-Flight PET/MR Imaging System.

Jaewon Yang1, Yiqiang Jian1, Nathaniel Jenkins1, Spencer C Behr1, Thomas A Hope1, Peder E Z Larson1, Daniel Vigneron1, Youngho Seo1.   

Abstract

Purpose To assess the patient-dependent accuracy of atlas-based attenuation correction (ATAC) for brain positron emission tomography (PET) in an integrated time-of-flight (TOF) PET/magnetic resonance (MR) imaging system. Materials and Methods Thirty recruited patients provided informed consent in this institutional review board-approved study. All patients underwent whole-body fluorodeoxyglucose PET/computed tomography (CT) followed by TOF PET/MR imaging. With use of TOF PET data, PET images were reconstructed with four different attenuation correction (AC) methods: PET with patient CT-based AC (CTAC), PET with ATAC (air and bone from an atlas), PET with ATACpatientBone (air and tissue from the atlas with patient bone), and PET with ATACboneless (air and tissue from the atlas without bone). For quantitative evaluation, PET mean activity concentration values were measured in 14 1-mL volumes of interest (VOIs) distributed throughout the brain and statistical significance was tested with a paired t test. Results The mean overall difference (±standard deviation) of PET with ATAC compared with PET with CTAC was -0.69 kBq/mL ± 0.60 (-4.0% ± 3.2) (P < .001). The results were patient dependent (range, -9.3% to 0.57%) and VOI dependent (range, -5.9 to -2.2). In addition, when bone was not included for AC, the overall difference of PET with ATACboneless (-9.4% ± 3.7) was significantly worse than that of PET with ATAC (-4.0% ± 3.2) (P < .001). Finally, when patient bone was used for AC instead of atlas bone, the overall difference of PET with ATACpatientBone (-1.5% ± 1.5) improved over that of PET with ATAC (-4.0% ± 3.2) (P < .001). Conclusion ATAC in PET/MR imaging achieves similar quantification accuracy to that from CTAC by means of atlas-based bone compensation. However, patient-specific anatomic differences from the atlas causes bone attenuation differences and misclassified sinuses, which result in patient-dependent performance variation of ATAC. © RSNA, 2017 Online supplemental material is available for this article.

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Year:  2017        PMID: 28234560     DOI: 10.1148/radiol.2017161603

Source DB:  PubMed          Journal:  Radiology        ISSN: 0033-8419            Impact factor:   11.105


  9 in total

1.  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

2.  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

3.  Joint correction of attenuation and scatter in image space using deep convolutional neural networks for dedicated brain 18F-FDG PET.

Authors:  Jaewon Yang; Dookun Park; Grant T Gullberg; Youngho Seo
Journal:  Phys Med Biol       Date:  2019-04-04       Impact factor: 3.609

4.  Generation of PET Attenuation Map for Whole-Body Time-of-Flight 18F-FDG PET/MRI Using a Deep Neural Network Trained with Simultaneously Reconstructed Activity and Attenuation Maps.

Authors:  Donghwi Hwang; Seung Kwan Kang; Kyeong Yun Kim; Seongho Seo; Jin Chul Paeng; Dong Soo Lee; Jae Sung Lee
Journal:  J Nucl Med       Date:  2019-01-25       Impact factor: 10.057

5.  Targeting iron metabolism in high-grade glioma with 68Ga-citrate PET/MR.

Authors:  Spencer C Behr; Javier E Villanueva-Meyer; Yan Li; Yung-Hua Wang; Junnian Wei; Anna Moroz; Julia Kl Lee; Jeffrey C Hsiao; Kenneth T Gao; Wendy Ma; Soonmee Cha; David M Wilson; Youngho Seo; Sarah J Nelson; Susan M Chang; Michael J Evans
Journal:  JCI Insight       Date:  2018-11-02

6.  MR-based Attenuation Correction for Brain PET Using 3D Cycle-Consistent Adversarial Network.

Authors:  Kuang Gong; Jaewon Yang; Peder E Z Larson; Spencer C Behr; Thomas A Hope; Youngho Seo; Quanzheng Li
Journal:  IEEE Trans Radiat Plasma Med Sci       Date:  2020-07-03

Review 7.  EANM/EARL harmonization strategies in PET quantification: from daily practice to multicentre oncological studies.

Authors:  Nicolas Aide; Charline Lasnon; Patrick Veit-Haibach; Terez Sera; Bernhard Sattler; Ronald Boellaard
Journal:  Eur J Nucl Med Mol Imaging       Date:  2017-06-16       Impact factor: 9.236

8.  Zero Echo Time MRAC on FDG-PET/MR Maintains Diagnostic Accuracy for Alzheimer's Disease; A Simulation Study Combining ADNI-Data.

Authors:  Takahiro Ando; Bradley Kemp; Geoffrey Warnock; Tetsuro Sekine; Sandeep Kaushik; Florian Wiesinger; Gaspar Delso
Journal:  Front Neurosci       Date:  2020-11-26       Impact factor: 4.677

9.  Evaluation of three methods for delineation and attenuation estimation of the sinus region in MR-based attenuation correction for brain PET-MR imaging.

Authors:  Jani Lindén; Jarmo Teuho; Mika Teräs; Riku Klén
Journal:  BMC Med Imaging       Date:  2022-03-17       Impact factor: 1.930

  9 in total

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