Literature DB >> 28504644

Direct reconstruction of parametric images for brain PET with event-by-event motion correction: evaluation in two tracers across count levels.

Mary Germino1, Jean-Dominque Gallezot, Jianhua Yan, Richard E Carson.   

Abstract

Parametric images for dynamic positron emission tomography (PET) are typically generated by an indirect method, i.e. reconstructing a time series of emission images, then fitting a kinetic model to each voxel time activity curve. Alternatively, 'direct reconstruction', incorporates the kinetic model into the reconstruction algorithm itself, directly producing parametric images from projection data. Direct reconstruction has been shown to achieve parametric images with lower standard error than the indirect method. Here, we present direct reconstruction for brain PET using event-by-event motion correction of list-mode data, applied to two tracers. Event-by-event motion correction was implemented for direct reconstruction in the Parametric Motion-compensation OSEM List-mode Algorithm for Resolution-recovery reconstruction. The direct implementation was tested on simulated and human datasets with tracers [11C]AFM (serotonin transporter) and [11C]UCB-J (synaptic density), which follow the 1-tissue compartment model. Rigid head motion was tracked with the Vicra system. Parametric images of K 1 and distribution volume (V T  =  K 1/k 2) were compared to those generated by the indirect method by regional coefficient of variation (CoV). Performance across count levels was assessed using sub-sampled datasets. For simulated and real datasets at high counts, the two methods estimated K 1 and V T with comparable accuracy. At lower count levels, the direct method was substantially more robust to outliers than the indirect method. Compared to the indirect method, direct reconstruction reduced regional K 1 CoV by 35-48% (simulated dataset), 39-43% ([11C]AFM dataset) and 30-36% ([11C]UCB-J dataset) across count levels (averaged over regions at matched iteration); V T CoV was reduced by 51-58%, 54-60% and 30-46%, respectively. Motion correction played an important role in the dataset with larger motion: correction increased regional V T by 51% on average in the [11C]UCB-J dataset. Direct reconstruction of dynamic brain PET with event-by-event motion correction is achievable and dramatically more robust to noise in V T images than the indirect method.

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Mesh:

Year:  2017        PMID: 28504644      PMCID: PMC5783541          DOI: 10.1088/1361-6560/aa731f

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


  24 in total

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Authors:  John M Floberg; Charles A Mistretta; Jamey P Weichert; Lance T Hall; James E Holden; Bradley T Christian
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2.  Quantitative myocardial perfusion PET parametric imaging at the voxel-level.

Authors:  Hassan Mohy-Ud-Din; Martin A Lodge; Arman Rahmim
Journal:  Phys Med Biol       Date:  2015-07-28       Impact factor: 3.609

Review 3.  Consensus nomenclature for in vivo imaging of reversibly binding radioligands.

Authors:  Robert B Innis; Vincent J Cunningham; Jacques Delforge; Masahiro Fujita; Albert Gjedde; Roger N Gunn; James Holden; Sylvain Houle; Sung-Cheng Huang; Masanori Ichise; Hidehiro Iida; Hiroshi Ito; Yuichi Kimura; Robert A Koeppe; Gitte M Knudsen; Juhani Knuuti; Adriaan A Lammertsma; Marc Laruelle; Jean Logan; Ralph Paul Maguire; Mark A Mintun; Evan D Morris; Ramin Parsey; Julie C Price; Mark Slifstein; Vesna Sossi; Tetsuya Suhara; John R Votaw; Dean F Wong; Richard E Carson
Journal:  J Cereb Blood Flow Metab       Date:  2007-05-09       Impact factor: 6.200

Review 4.  PET kinetic analysis: wavelet denoising of dynamic PET data with application to parametric imaging.

Authors:  Miho Shidahara; Yoko Ikoma; Jeff Kershaw; Yuichi Kimura; Mika Naganawa; Hiroshi Watabe
Journal:  Ann Nucl Med       Date:  2007-09-25       Impact factor: 2.668

Review 5.  Four-dimensional (4D) image reconstruction strategies in dynamic PET: beyond conventional independent frame reconstruction.

Authors:  Arman Rahmim; Jing Tang; Habib Zaidi
Journal:  Med Phys       Date:  2009-08       Impact factor: 4.071

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Journal:  Phys Med Biol       Date:  2015-05-20       Impact factor: 3.609

7.  EM reconstruction algorithms for emission and transmission tomography.

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Journal:  J Comput Assist Tomogr       Date:  1984-04       Impact factor: 1.826

8.  Parametric images of blood flow in oncology PET studies using [15O]water.

Authors:  M A Lodge; R E Carson; J A Carrasquillo; M Whatley; S K Libutti; S L Bacharach
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Authors:  Sjoerd J Finnema; Nabeel B Nabulsi; Tore Eid; Kamil Detyniecki; Shu-Fei Lin; Ming-Kai Chen; Roni Dhaher; David Matuskey; Evan Baum; Daniel Holden; Dennis D Spencer; Joël Mercier; Jonas Hannestad; Yiyun Huang; Richard E Carson
Journal:  Sci Transl Med       Date:  2016-07-20       Impact factor: 17.956

10.  Direct Parametric Reconstruction With Joint Motion Estimation/Correction for Dynamic Brain PET Data.

Authors:  Jieqing Jiao; Alexandre Bousse; Kris Thielemans; Ninon Burgos; Philip S J Weston; Jonathan M Schott; David Atkinson; Simon R Arridge; Brian F Hutton; Pawel Markiewicz; Sebastien Ourselin
Journal:  IEEE Trans Med Imaging       Date:  2016-08-24       Impact factor: 10.048

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  6 in total

1.  Cardiac-gated parametric images from 82 Rb PET from dynamic frames and direct 4D reconstruction.

Authors:  Mary Germino; Richard E Carson
Journal:  Med Phys       Date:  2017-12-30       Impact factor: 4.071

2.  Simultaneous Denoising of Dynamic PET Images Based on Deep Image Prior.

Authors:  Cheng-Hsun Yang; Hsuan-Ming Huang
Journal:  J Digit Imaging       Date:  2022-03-03       Impact factor: 4.903

3.  NRM 2021 Abstract Booklet.

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Journal:  J Cereb Blood Flow Metab       Date:  2021-12       Impact factor: 6.960

4.  Direct List Mode Parametric Reconstruction for Dynamic Cardiac SPECT.

Authors:  Luyao Shi; Yihuan Lu; Jing Wu; Jean-Dominique Gallezot; Nabil Boutagy; Stephanie Thorn; Albert J Sinusas; Richard E Carson; Chi Liu
Journal:  IEEE Trans Med Imaging       Date:  2019-06-10       Impact factor: 10.048

5.  Adaptive data-driven motion detection and optimized correction for brain PET.

Authors:  Enette Mae Revilla; Jean-Dominique Gallezot; Mika Naganawa; Takuya Toyonaga; Kathryn Fontaine; Tim Mulnix; John A Onofrey; Richard E Carson; Yihuan Lu
Journal:  Neuroimage       Date:  2022-03-04       Impact factor: 7.400

Review 6.  MRI-Driven PET Image Optimization for Neurological Applications.

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

  6 in total

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