Literature DB >> 29653974

Development of a Dedicated Rebinner with Rigid Motion Correction for the mMR PET/MR Scanner, and Validation in a Large Cohort of 11C-PIB Scans.

Anthonin Reilhac1,2, Inés Merida2, Zacharie Irace2, Mary C Stephenson3, Ashley A Weekes3, Christopher Chen4,5, John J Totman3, David W Townsend3, Hadi Fayad6,7, Nicolas Costes2.   

Abstract

Head motion occurring during brain PET studies leads to image blurring and to bias in measured local quantities. The objective of this work was to implement a correction method for PET data acquired with the mMR synchronous PET/MR scanner.
Methods: A list-mode-based motion-correction approach has been designed. The developed rebinner chronologically reads the recorded events from the Siemens list-mode file, applies the estimated geometric transformations, and frames the detected counts into sinograms. The rigid-body motion parameters were estimated from an initial dynamic reconstruction of the PET data. We then optimized the correction for 11C-Pittsburgh compound B (11C-PIB) scans using simulated and actual data with well-controlled motion.
Results: An efficient list-mode-based motion correction approach has been implemented, fully optimized, and validated using simulated and actual PET data. The average spatial resolution loss induced by inaccuracies in motion parameter estimates and by the rebinning process was estimated to correspond to a 1-mm increase in full width at half maximum with motion parameters estimated directly from the PET data with a temporal frequency of 20 s. The results show that the rebinner can be safely applied to the 11C-PIB scans, allowing almost complete removal of motion-induced artifacts. The application of the correction method to a large cohort of 11C-PIB scans led to the following observations: first, that more than 21% of the scans were affected by motion greater than 10 mm (39% for subjects with Mini-Mental State Examination scores below 20), and second, that the correction led to quantitative changes in Alzheimer-specific cortical regions of up to 30%.
Conclusion: The rebinner allows accurate motion correction at a cost of minimal resolution reduction. Application of the correction to a large cohort of 11C-PIB scans confirmed the necessity of systematically correcting for motion to obtain quantitative results.
© 2018 by the Society of Nuclear Medicine and Molecular Imaging.

Entities:  

Keywords:  11C-PIB; PET; list-mode rebinner; rigid motion correction

Mesh:

Substances:

Year:  2018        PMID: 29653974     DOI: 10.2967/jnumed.117.206375

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


  11 in total

1.  Comparison of Three Automated Approaches for Classification of Amyloid-PET Images.

Authors:  Ying-Hwey Nai; Yee-Hsin Tay; Tomotaka Tanaka; Christopher P Chen; Edward G Robins; Anthonin Reilhac
Journal:  Neuroinformatics       Date:  2022-05-27

2.  Improved quantification of amyloid burden and associated biomarker cut-off points: results from the first amyloid Singaporean cohort with overlapping cerebrovascular disease.

Authors:  Tomotaka Tanaka; Mary C Stephenson; Ying-Hwey Nai; Damian Khor; Francis N Saridin; Saima Hilal; Steven Villaraza; Bibek Gyanwali; Masafumi Ihara; Henri Vrooman; Ashley A Weekes; John J Totman; Edward G Robins; Christopher P Chen; Anthonin Reilhac
Journal:  Eur J Nucl Med Mol Imaging       Date:  2019-12-20       Impact factor: 9.236

3.  Patient motion correction for dynamic cardiac PET: Current status and challenges.

Authors:  Yihuan Lu; Chi Liu
Journal:  J Nucl Cardiol       Date:  2018-11-12       Impact factor: 5.952

4.  Uncertainty analysis of MR-PET image registration for precision neuro-PET imaging.

Authors:  Pawel J Markiewicz; Julian C Matthews; John Ashburner; David M Cash; David L Thomas; Enrico De Vita; Anna Barnes; M Jorge Cardoso; Marc Modat; Richard Brown; Kris Thielemans; Casper da Costa-Luis; Isadora Lopes Alves; Juan Domingo Gispert; Mark E Schmidt; Paul Marsden; Alexander Hammers; Sebastien Ourselin; Frederik Barkhof
Journal:  Neuroimage       Date:  2021-02-12       Impact factor: 6.556

Review 5.  Synergistic motion compensation strategies for positron emission tomography when acquired simultaneously with magnetic resonance imaging.

Authors:  Irene Polycarpou; Georgios Soultanidis; Charalampos Tsoumpas
Journal:  Philos Trans A Math Phys Eng Sci       Date:  2021-07-05       Impact factor: 4.226

6.  Data-Driven Motion Detection and Event-by-Event Correction for Brain PET: Comparison with Vicra.

Authors:  Yihuan Lu; Mika Naganawa; Takuya Toyonaga; Jean-Dominique Gallezot; Kathryn Fontaine; Silin Ren; Enette Mae Revilla; Tim Mulnix; Richard E Carson
Journal:  J Nucl Med       Date:  2020-01-31       Impact factor: 11.082

7.  Assessment of motion and model bias on the detection of dopamine response to behavioral challenge.

Authors:  Michael A Levine; Joseph B Mandeville; Finnegan Calabro; David Izquierdo-Garcia; Daniel B Chonde; Kevin T Chen; Inki Hong; Julie C Price; Beatriz Luna; Ciprian Catana
Journal:  J Cereb Blood Flow Metab       Date:  2022-02-04       Impact factor: 6.960

Review 8.  MRI-Driven PET Image Optimization for Neurological Applications.

Authors:  Yuankai Zhu; Xiaohua Zhu
Journal:  Front Neurosci       Date:  2019-07-31       Impact factor: 4.677

9.  Improved amyloid burden quantification with nonspecific estimates using deep learning.

Authors:  Haohui Liu; Ying-Hwey Nai; Francis Saridin; Tomotaka Tanaka; Jim O' Doherty; Saima Hilal; Bibek Gyanwali; Christopher P Chen; Edward G Robins; Anthonin Reilhac
Journal:  Eur J Nucl Med Mol Imaging       Date:  2021-01-07       Impact factor: 9.236

10.  Longitudinal trajectory of Amyloid-related hippocampal subfield atrophy in nondemented elderly.

Authors:  Liwen Zhang; Elijah Mak; Anthonin Reilhac; Hee Y Shim; Kwun K Ng; Marcus Q W Ong; Fang Ji; Eddie J Y Chong; Xin Xu; Zi X Wong; Mary C Stephenson; Narayanaswamy Venketasubramanian; Boon Y Tan; John T O'Brien; Juan H Zhou; Christopher L H Chen
Journal:  Hum Brain Mapp       Date:  2020-01-14       Impact factor: 5.038

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