A multiresolution image based approach for correction of partial volume effects in emission tomography

N Boussion, M Hatt, F Lamare, Y Bizais, A Turzo, C Cheze-Le Rest, D Visvikis
Phys. Med. Biol.. 2006-03-21; 51(7): 1857-1876
DOI: 10.1088/0031-9155/51/7/016

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1. Phys Med Biol. 2006 Apr 7;51(7):1857-76. Epub 2006 Mar 21.

A multiresolution image based approach for correction of partial volume effects
in emission tomography.

Boussion N(1), Hatt M, Lamare F, Bizais Y, Turzo A, Cheze-Le Rest C, Visvikis D.

Author information:
(1)INSERM U650, Laboratoire du Traitement de l’Information Médicale (LaTIM), CHU
Morvan, Brest, France.

Partial volume effects (PVEs) are consequences of the limited spatial resolution
in emission tomography. They lead to a loss of signal in tissues of size similar
to the point spread function and induce activity spillover between regions.
Although PVE can be corrected for by using algorithms that provide the correct
radioactivity concentration in a series of regions of interest (ROIs), so far
little attention has been given to the possibility of creating improved images as
a result of PVE correction. Potential advantages of PVE-corrected images include
the ability to accurately delineate functional volumes as well as improving
tumour-to-background ratio, resulting in an associated improvement in the
analysis of response to therapy studies and diagnostic examinations,
respectively. The objective of our study was therefore to develop a methodology
for PVE correction not only to enable the accurate recuperation of activity
concentrations, but also to generate PVE-corrected images. In the multiresolution
analysis that we define here, details of a high-resolution image H (MRI or CT)
are extracted, transformed and integrated in a low-resolution image L (PET or
SPECT). A discrete wavelet transform of both H and L images is performed by using
the « à trous » algorithm, which allows the spatial frequencies (details, edges,
textures) to be obtained easily at a level of resolution common to H and L. A
model is then inferred to build the lacking details of L from the high-frequency
details in H. The process was successfully tested on synthetic and simulated
data, proving the ability to obtain accurately corrected images. Quantitative PVE
correction was found to be comparable with a method considered as a reference but
limited to ROI analyses. Visual improvement and quantitative correction were also
obtained in two examples of clinical images, the first using a combined PET/CT
scanner with a lymphoma patient and the second using a FDG brain PET and
corresponding T1-weighted MRI in an epileptic patient.

DOI: 10.1088/0031-9155/51/7/016
PMID: 16552110 [Indexed for MEDLINE]

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