Quantification of [ 11 C]FLB 457 binding in the human brain with PET before and after PVE correction Judit Sóvágó Karolinska Institutet Department of Clinical.

Slides:



Advertisements
Similar presentations
Two aims: 1.Take stock of the dMRI literature on TBI. 2.Make a case for patient specific identification of dMRI abnormalities.
Advertisements

Variability of PET-PIB retention measurements due to different scanner performance in multi-site trials Jean-Claude Rwigema Chet Mathis Charles Laymon.
The BRAINs Orientation
H Jeremy Bockholt Ronald Pierson Vincent Magnotta Nancy C Andreasen The BRAINS2 Morphometry pipeline in action BRAINS2/Slicer Workshop.
Realigning and Unwarping MfD
ANALYSIS OF PET STUDIES PET Basics Course 2006 Turku PET Centre Vesa Oikonen
fMRI data analysis at CCBI
Applications of wavelets in PET modelling - a literature survey.
PVEOut project, EU IBB, 2004 Cortical GABA-A CBZR and rCBF in Alzheimer`s disease: SPET studies with Partial Volume Effect Correction Mario Quarantelli.
T (B+F)/F B/F ‘late’ or ‘early’ image? whose brain is this? why a CT scan first? why increasing contrast w/ time? why increasing noise w/time?
ANALYSIS OF PET STUDIES Turku PET Centre V Oikonen PET Raw Data (sinogram) Results Parametric Sinogram PET Image Parametric Image Regional TACs.
The structural organization of the Brain Gray matter: nerve cell bodies (neurons), glial cells, capillaries, and short nerve cell extensions (axons and.
Imaging Drugs in the Brain ENAS 880 / NSCI 523 Fall 2010 Morris/Cosgrove kelly
1/20 Document Segmentation for Image Compression 27/10/2005 Emma Jonasson Supervisor: Dr. Peter Tischer.
VISUALIZING ALL THE FITS: Evaluating The Quality And Precision Of Parametric Images Created From Direct Reconstruction Of PET Sinogram Data Evan D. Morris.
Edward Kaye 1,2, Kie Honjo 3,4, Ana Petrovic-Poljak 5, Robert van Reekum 6, David Streiner 1,6,7,8,9, Alan Wilson 6,10, Sandra Black 3,4,11, Morris Freedman.
MNTP Trainee: Georgina Vinyes Junque, Chi Hun Kim Prof. James T. Becker Cyrus Raji, Leonid Teverovskiy, and Robert Tamburo.
Positron Emission Tomography: Tool to Facilitate Drug Development and to Study Pharmacokinetics Robert B. Innis, MD, PhD Molecular Imaging Branch National.
Dinggang Shen Development and Dissemination of Robust Brain MRI Measurement Tools ( 1R01EB ) Department of Radiology and BRIC UNC-Chapel Hill IDEA.
Figure 2 shows between-group connectivity when the seed is the amygdala. Ayahuasca users show greater connectivity based from the amygdala. The connectivity.
May 19-20, Quantitation, both single point and longitudinal*, of tumor metabolism via FDG-PET/CT that can be used practically and efficiently as.
Registration of functional PET and structural MR images PVEOut satellite meeting Budapest, June 11 th 2004 Peter Willendrup & Claus Svarer Neurobiology.
ASSESSMENT OF INTERNAL BRUISE VOLUME OF SELECTED FRUITS USING MR IMAGING Ta-Te Lin, Yu-Che Cheng, Jen-Fang Yu Department of Bio-Industrial Mechatronics.
Spatio-Temporal Free-Form Registration of Cardiac MR Image Sequences Antonios Perperidis s /02/2006.
Department of Chemistry Seminar Announcement Date/Time/VenueTitle/Speaker 12 Jan (Wed) 11am – S8 Level 3 Executive Classroom PET in Neuroscience.
Conclusions Simulated fMRI phantoms with real motion and realistic susceptibility artifacts have been generated and tested using SPM2. Image distortion.
NA-MIC National Alliance for Medical Image Computing Evaluating Brain Tissue Classifiers S. Bouix, M. Martin-Fernandez, L. Ungar, M.
A.B.Madhan Kumar Mentor: Dr. Charles M. Laymon Department of Radiology
Automatic pipeline for quantitative brain tissue segmentation and parcellation: Experience with a large longitudinal schizophrenia MRI study 1,2 G Gerig,
Dr. Ali Saad modified from Dr. Carlos Davila Southe. metho univ 1 EEG Brain signal measurement and analysis 414BMT Dr Ali Saad, College of Applied medical.
Comparison of methodologies for the assessment of dopamine receptor binding in subregions of the striatum Functional Neuroimaging Lab School of Psychology.
GENERATION OF PARAMETRIC IMAGES PROSPECTS PROBLEMS Vesa Oikonen Turku PET Centre
PVEOut project, EU IBB, 2004 Issues in voxel-based analysis of PV-corrected data Mario Quarantelli Biostructure and Bioimaging Institute – CNR Naples -
Kamran M 1, Deuerling-Zheng 2, Mueller-Allissat B 2, Grunwald IQ 1, Byrne JV 1 1. Oxford Neurovascular and Neuroradiology Research Unit, University of.
National Alliance for Medical Image Computing Volumetric Studies.
THE ROLE OF QEEG IN COMPREHENSIVE CLASSIFICATION OF ADHD CHILDREN OF ADHD CHILDREN Zorcec Tatjana¹, Pop-Jordanova Nada¹, Mueller Andreas², Biljana Gjoneska³.
Federico E. Turkheimer, Paul Edison, Nicola Pavese, Federico Roncaroli, Alexander Hammers, Alex Anderson, Alexander Gerhard, Rainer Hinz, Yen F. Tai, David.
Segmentation of 3D microPET Images of the Rat Brain by Hybrid GMM and KDE Tai-Been Chen Department of Medical Imaging and Radiological Science,
Date of download: 7/9/2016 Copyright © 2016 SPIE. All rights reserved. An example of fMRI data acquired from a healthy fetus in the coronal plane. It includes.
Pilot study of [64Cu]-histidine2 PET imaging
Hybrid Head motion correction in PET/MR Brian Imaging
Discussion & Conclusions
Manuela Tondelli, Gordon K. Wilcock, Paolo Nichelli, Celeste A
Thursday Case of the Day
DATA-DRIVEN TAU-PET COVARIANCE NETWORKS ENHANCE PREDICTION OF RETROSPECTIVE COGNITIVE CHANGE IN ALZHEIMER’S DISEASE  Jacob W. Vogel, Niklas Mattsson,
20 Years of FreeSurfer PETsurfer: MRI-PET Integration using FreeSurfer Nov 17, 2017 Douglas N. Greve, Ph.D. Martinos Center for Biomedical Imaging Massachusetts.
Signal fluctuations in 2D and 3D fMRI at 7 Tesla
MR images of cytoarchitectural dysplasia
Model Part – Yiming Weng
Kirsten Tillisch, Jennifer S. Labus  Gastroenterology 
Fig. 1. [11C]Martinostat images of all subjects show high cortical binding and distinct gray-white matter differences. [11C]Martinostat images of all subjects.
Spherical harmonic representation of anatomical boundary
Goals Significance Results Uncorrected MAR Corrected
Volume 77, Issue 2, Pages (January 2013)
Left middle frontal cortex, caudate nuclei
Network hubs in the human brain
Normalized and averaged images of rGMC and I-123 iomazenil BP
SYSTEMATIC REVIEW OF COMPUTATIONAL MODELS FOR BRAIN PARCELLATION
Botond K. Szabó * Peter Aspelin ** Maria Kristoffersen-Wiberg **
Walter H. Kaye, Christina E. Wierenga, Ursula F. Bailer, Alan N
Panoramic Image Reconstruction: Problem
MultiModality Registration using Hilbert-Schmidt Estimators
Will Penny Wellcome Trust Centre for Neuroimaging,
NAMIC AHM, Jan 7-11, 2008 – Salt Lake City, Utah
The influence of biological and technical factors on quantitative analysis of amyloid PET: Points to consider and recommendations for controlling variability.
Axial MR image (TR/TE, 10,002/142) obtained when the patient was aged 5 days shows extensive areas of abnormal signal intensity, which suggest edema involving.
Common Prefrontal Regions Coactivate with Dissociable Posterior Regions during Controlled Semantic and Phonological Tasks  Brian T Gold, Randy L Buckner 
Fig. 1. Examples of the response evaluation using MR-CAD, DWI and PET/CT. (A) DCE-MRI analysis using MR-CAD provided information regarding the size, volume.
Given that {image} {image} Evaluate the limit: {image} Choose the correct answer from the following:
Presentation transcript:

Quantification of [ 11 C]FLB 457 binding in the human brain with PET before and after PVE correction Judit Sóvágó Karolinska Institutet Department of Clinical Neuroscience HBM2004 Satellite meeting

The influence of PVE on estimates of radioligand uptake BP 5-HT 2A receptors increased with 4-16% after PVC Interregional differences of 5-HT 2A density detected only after PVC Estimates of influx and metabolism of 18 F-DOPA altered in region specific manner after PVC Signal under- or overestimation due to PVE

To estimate the influence of PVE on the measured regional uptake of [ 11 C]FLB 457 Study aims To evaluate the performance of different PVE correction algorithms in PET studies with [ 11 C]FLB 457: Meltzer Müller-Gärtner Rousset

Image analysis 1.Image acquisition: 6 subjects, T2w. MR, [ 11 C]FLB 457 PET 2.Segmentation: automated and manual 3.Coregistration: manual, image overlay method (MARS) 4.PVE correction: PVE developed within project QLG3-CT ROI definition: automated and manual

Quantification of ROI data Standard 3 compartment model CPCP CFCF CBCB K1K1 k3k3 k2k2 k4k4 BP = k 3 /k 4 DV (tot) = (K 1 /k 2 )(1+k 3 /k 4 )

[ 11 C]FLB 457 uptake in the brain time (min) ROI mean (nCi/ml) thalamusputamen caudateamygdala parietal c.occipital c. temporal c.frontal c. white matter cerebellum Caud.Put.Thal.Amyg.Temp.Front.Pariet.Occ.Cb. K k k k

[ 11 C]FLB 457 uptake in the brain after PVE correction according to Meltzer time (min) ROI mean (nCi/ml) thalamusputamen caudateamygdala parietal c.occipital c. temporal c.frontal c. white matter cerebellum Caud.Put.Thal.Amyg.Temp.Front.Pariet.Occip. K1+14.2*+1.0*+7.2*+11.1*+17.4*+24.9*+32.3*+18.5* k * *+3.1 k k * time (min) change in ROI mean (% of uncorrected values) * p < 0.05, * p < 0.005

[ 11 C]FLB 457 uptake in the brain after PVE correction according to Müller-Gärtner time (min) Caud.Put.Thal.Amyg.Temp.Front.Pariet.Occip. K1+28.3*+15.3*+15.8*+14.3*+39.1*+50.1*+70.5*+54.7* k *+18.3*+13.0*+16.2*+9.3* k * k time (min) * p < 0.05, * p < thalamusputamen caudateamygdala parietal c.occipital c. temporal c.frontal c. white matter cerebellum ROI mean (nCi/ml) change in ROI mean (% of uncorrected values)

[ 11 C]FLB 457 uptake in the brain after PVE correction according to Rousset time (min) Caud.Put.Thal.Amyg.Temp.Front.Pariet.Occip. K * *+55.3*+73.8*+58.1* k *+13.0*+12.4*+10.7* k *+33.5* k * time (min) * p < 0.05, * p < thalamusputamen caudateamygdala parietal c.occipital c. temporal c.frontal c. white matter cerebellum ROI mean (nCi/ml) change in ROI mean (% of uncorrected values)

PutamenCaudateThalamusAmygdalaTemp. c.Front. c.Pariet. c.Occipit. c. Total distribution volume (DV (tot) ) VD(tot) PET Meltzer Müller-Gärtner Rousset * p < 0.05, * p < * * * * ** * ** * ** * ** * * *

Binding potential (BP) BP * * * * * * ** PET Meltzer Müller-Gärtner Rousset * p < 0.05, * p < 0.005

Conclusions [ 11 C]FLB 457 binding is underestimated to the highest degree in the temporal cortex and the amygdala. DV tot is more susceptible to PVE than BP. The applied PVE correction algorithms recovered the regional activity loss with different efficacy: –Meltzer: least efficient –Müller-Gärtner: increased DV tot and BP –Rousset: increased DV tot, enhanced differences in BP (Comparison with receptor density obtained with in vitro methods?)