Literature DB >> 27339875

Clinical Evaluation of Zero-Echo-Time Attenuation Correction for Brain 18F-FDG PET/MRI: Comparison with Atlas Attenuation Correction.

Tetsuro Sekine1,2, Edwin E G W Ter Voert3, Geoffrey Warnock3,4,5, Alfred Buck3, Martin Huellner3,6, Patrick Veit-Haibach3,7, Gaspar Delso8.   

Abstract

Accurate attenuation correction (AC) on PET/MR is still challenging. The purpose of this study was to evaluate the clinical feasibility of AC based on fast zero-echo-time (ZTE) MRI by comparing it with the default atlas-based AC on a clinical PET/MR scanner.
METHODS: We recruited 10 patients with malignant diseases not located on the brain. In all patients, a clinically indicated whole-body 18F-FDG PET/CT scan was acquired. In addition, a head PET/MR scan was obtained voluntarily. For each patient, 2 AC maps were generated from the MR images. One was atlas-AC, derived from T1-weighted liver acquisition with volume acceleration flex images (clinical standard). The other was ZTE-AC, derived from proton-density-weighted ZTE images by applying tissue segmentation and assigning continuous attenuation values to the bone. The AC map generated by PET/CT was used as a silver standard. On the basis of each AC map, PET images were reconstructed from identical raw data on the PET/MR scanner. All PET images were normalized to the SPM5 PET template. After that, these images were qualified visually and quantified in 67 volumes of interest (VOIs; automated anatomic labeling, atlas). Relative differences and absolute relative differences between PET images based on each AC were calculated. 18F-FDG uptake in all 670 VOIs and generalized merged VOIs were compared using a paired t test.
RESULTS: Qualitative analysis shows that ZTE-AC was robust to patient variability. Nevertheless, misclassification of air and bone in mastoid and nasal areas led to the overestimation of PET in the temporal lobe and cerebellum (%diff of ZTE-AC, 2.46% ± 1.19% and 3.31% ± 1.70%, respectively). The |%diff| of all 670 VOIs on ZTE was improved by approximately 25% compared with atlas-AC (ZTE-AC vs. atlas-AC, 1.77% ± 1.41% vs. 2.44% ± 1.63%, P < 0.01). In 2 of 7 generalized VOIs, |%diff| on ZTE-AC was significantly smaller than atlas-AC (ZTE-AC vs. atlas-AC: insula and cingulate, 1.06% ± 0.67% vs. 2.22% ± 1.10%, P < 0.01; central structure, 1.03% ± 0.99% vs. 2.54% ± 1.20%, P < 0.05).
CONCLUSION: The ZTE-AC could provide more accurate AC than clinical atlas-AC by improving the estimation of head-skull attenuation. The misclassification in mastoid and nasal areas must be addressed to prevent the overestimation of PET in regions near the skull base.
© 2016 by the Society of Nuclear Medicine and Molecular Imaging, Inc.

Entities:  

Keywords:  18F-FDG; PET/MR; ZTE; atlas-based; attenuation correction; brain

Mesh:

Substances:

Year:  2016        PMID: 27339875     DOI: 10.2967/jnumed.116.175398

Source DB:  PubMed          Journal:  J Nucl Med        ISSN: 0161-5505            Impact factor:   10.057


  32 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.  mDixon-Based Synthetic CT Generation for PET Attenuation Correction on Abdomen and Pelvis Jointly Using Transfer Fuzzy Clustering and Active Learning-Based Classification.

Authors:  Pengjiang Qian; Yangyang Chen; Jung-Wen Kuo; Yu-Dong Zhang; Yizhang Jiang; Kaifa Zhao; Rose Al Helo; Harry Friel; Atallah Baydoun; Feifei Zhou; Jin Uk Heo; Norbert Avril; Karin Herrmann; Rodney Ellis; Bryan Traughber; Robert S Jones; Shitong Wang; Kuan-Hao Su; Raymond F Muzic
Journal:  IEEE Trans Med Imaging       Date:  2019-08-16       Impact factor: 10.048

3.  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

4.  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

5.  A Quantitative Evaluation of Joint Activity and Attenuation Reconstruction in TOF PET/MR Brain Imaging.

Authors:  Ahmadreza Rezaei; Georg Schramm; Stefanie M A Willekens; Gaspar Delso; Koen Van Laere; Johan Nuyts
Journal:  J Nucl Med       Date:  2019-04-12       Impact factor: 10.057

6.  Clinical evaluation of TOF versus non-TOF on PET artifacts in simultaneous PET/MR: a dual centre experience.

Authors:  Edwin E G W Ter Voert; Patrick Veit-Haibach; Sangtae Ahn; Florian Wiesinger; M Mehdi Khalighi; Craig S Levin; Andrei H Iagaru; Greg Zaharchuk; Martin Huellner; Gaspar Delso
Journal:  Eur J Nucl Med Mol Imaging       Date:  2017-01-26       Impact factor: 9.236

7.  Impact of Tissue Classification in MRI-Guided Attenuation Correction on Whole-Body Patlak PET/MRI.

Authors:  Mingzan Zhuang; Nicolas A Karakatsanis; Rudi A J O Dierckx; Habib Zaidi
Journal:  Mol Imaging Biol       Date:  2019-12       Impact factor: 3.488

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.  ZTE MR-based attenuation correction in brain FDG-PET/MR: performance in patients with cognitive impairment.

Authors:  Brian Sgard; Maya Khalifé; Arthur Bouchut; Brice Fernandez; Marine Soret; Alain Giron; Clara Zaslavsky; Gaspar Delso; Marie-Odile Habert; Aurélie Kas
Journal:  Eur Radiol       Date:  2019-11-20       Impact factor: 5.315

10.  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

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