Regional deconvolution method for partial volume correction in brain PET

2001 ◽  
Author(s):  
Henry Rusinek ◽  
Wai-Hon Tsui ◽  
Mony J. de Leon
Keyword(s):  
Partial Volume Correction ◽  
Deconvolution Method ◽  
Partial Volume ◽  
Volume Correction ◽  
Brain Pet

MRI-guided brain PET image filtering and partial volume correction

2015 ◽  
Vol 60 (3) ◽  
pp. 961-976 ◽  
Author(s):  
Jianhua Yan ◽  
Jason Chu-Shern Lim ◽  
David W Townsend
Keyword(s):  
Image Filtering ◽  
Partial Volume Correction ◽  
Partial Volume ◽  
Volume Correction ◽  
Brain Pet ◽  
Mri Guided

scholarly journals Establishment of an open database of realistic simulated data for evaluation of partial volume correction techniques in brain PET/MR

2015 ◽  
Vol 2 (S1) ◽  
Author(s):  
Ana Mota ◽  
Vesna Cuplov ◽  
Jonathan Schott ◽  
Brian Hutton ◽  
Kris Thielemans ◽  
...  
Keyword(s):  
Simulated Data ◽  
Partial Volume Correction ◽  
Partial Volume ◽  
Volume Correction ◽  
Brain Pet

scholarly journals Erratum to: Establishment of an open database of realistic simulated data for evaluation of partial volume correction techniques in brain PET/MR

2015 ◽  
Vol 2 (1) ◽  
Author(s):  
Ana Mota ◽  
Vesna Cuplov ◽  
Ivana Drobnjak ◽  
John Dickson ◽  
Julien Bert ◽  
...  
Keyword(s):  
Simulated Data ◽  
Partial Volume Correction ◽  
Partial Volume ◽  
Volume Correction ◽  
Brain Pet

A blind deconvolution method incorporated with anatomical-based filtering for partial volume correction: Validations with 123 I-mIBG cardiac SPECT/CT

2017 ◽  
Vol 44 (12) ◽  
pp. 6435-6446 ◽  
Author(s):  
Jing Wu ◽  
Hui Liu ◽  
Taraneh Hashemi Zonouz ◽  
Veronica M. Sandoval ◽  
Hassan Mohy-ud-Din ◽  
...  
Keyword(s):  
Blind Deconvolution ◽  
Partial Volume Correction ◽  
Deconvolution Method ◽  
Cardiac Spect ◽  
Partial Volume ◽  
Volume Correction

Performance evaluation of PMOD integrated partial volume correction method for brain PET data

NeuroImage ◽  
2010 ◽  
Vol 52 ◽  
pp. S10 ◽  
Author(s):  
Olivier Barret ◽  
Pawel Mularczyk ◽  
Krzysztof Mikolajczyk ◽  
Cyrill Burger
Keyword(s):  
Performance Evaluation ◽  
Correction Method ◽  
Partial Volume Correction ◽  
Partial Volume ◽  
Volume Correction ◽  
Brain Pet

scholarly journals Error propagation analysis of seven partial volume correction algorithms for [18F]THK-5351 brain PET imaging

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Senri Oyama ◽  
Ayumu Hosoi ◽  
Masanobu Ibaraki ◽  
Colm J. McGinnity ◽  
Keisuke Matsubara ◽  
...  
Keyword(s):  
Image Registration ◽  
Pet Imaging ◽  
Error Propagation ◽  
Volume Effect ◽  
Clinical Applications ◽  
Partial Volume Correction ◽  
Error Sources ◽  
Partial Volume ◽  
Volume Correction ◽  
Brain Pet

Abstract Background Novel partial volume correction (PVC) algorithms have been validated by assuming ideal conditions of image processing; however, in real clinical PET studies, the input datasets include error sources which cause error propagation to the corrected outcome. Methods We aimed to evaluate error propagations of seven PVCs algorithms for brain PET imaging with [18F]THK-5351 and to discuss the reliability of those algorithms for clinical applications. In order to mimic brain PET imaging of [18F]THK-5351, pseudo-observed SUVR images for one healthy adult and one adult with Alzheimer’s disease were simulated from individual PET and MR images. The partial volume effect of pseudo-observed PET images were corrected by using Müller-Gärtner (MG), the geometric transfer matrix (GTM), Labbé (LABBE), regional voxel-based (RBV), iterative Yang (IY), structural functional synergy for resolution recovery (SFS-RR), and modified SFS-RR algorithms with incorporation of error sources in the datasets for PVC processing. Assumed error sources were mismatched FWHM, inaccurate image-registration, and incorrectly segmented anatomical volume. The degree of error propagations in ROI values was evaluated by percent differences (%diff) of PV-corrected SUVR against true SUVR. Results Uncorrected SUVRs were underestimated against true SUVRs (− 15.7 and − 53.7% in hippocampus for HC and AD conditions), and application of each PVC algorithm reduced the %diff. Larger FWHM mismatch led to larger %diff of PVC-SUVRs against true SUVRs for all algorithms. Inaccurate image registration showed systematic propagation for most algorithms except for SFS-RR and modified SFS-RR. Incorrect segmentation of the anatomical volume only resulted in error propagations in limited local regions. Conclusions We demonstrated error propagation by numerical simulation of THK-PET imaging. Error propagations of 7 PVC algorithms for brain PET imaging with [18F]THK-5351 were significant. Robust algorithms for clinical applications must be carefully selected according to the study design of clinical PET data.

Functional and structural synergy for resolution recovery and partial volume correction in brain PET

NeuroImage ◽  
2009 ◽  
Vol 44 (2) ◽  
pp. 340-348 ◽  
Author(s):  
M SHIDAHARA ◽  
C TSOUMPAS ◽  
A HAMMERS ◽  
N BOUSSION ◽  
D VISVIKIS ◽  
...  
Keyword(s):  
Partial Volume Correction ◽  
Resolution Recovery ◽  
Partial Volume ◽  
Volume Correction ◽  
Brain Pet

scholarly journals Partial volume correction of brain PET studies using iterative deconvolution in combination with HYPR denoising

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Sandeep S. V. Golla ◽  
Mark Lubberink ◽  
Bart N. M. van Berckel ◽  
Adriaan A. Lammertsma ◽  
Ronald Boellaard
Keyword(s):  
Partial Volume Correction ◽  
Partial Volume ◽  
Volume Correction ◽  
Brain Pet ◽  
Iterative Deconvolution

Segmentation-Based Partial Volume Correction for Volume Estimation of Solid Lesions in CT

2014 ◽  
Vol 33 (2) ◽  
pp. 462-480 ◽  
Author(s):  
Frank Heckel ◽  
Hans Meine ◽  
Jan H. Moltz ◽  
Jan-Martin Kuhnigk ◽  
Johannes T. Heverhagen ◽  
...  
Keyword(s):  
Volume Estimation ◽  
Partial Volume Correction ◽  
Partial Volume ◽  
Volume Correction ◽  
Solid Lesions
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