http://www.cnr.it/ontology/cnr/individuo/prodotto/ID185322
Automatic partial volume correction in cardiac PET without anatomical images: a preliminary study (Abstract/Comunicazione in rivista)
- Type
- Label
- Automatic partial volume correction in cardiac PET without anatomical images: a preliminary study (Abstract/Comunicazione in rivista) (literal)
- Anno
- 2009-01-01T00:00:00+01:00 (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
- 10.1007/s00259-009-1234-6 (literal)
- Alternative label
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
- Panetta Daniele; Pisani Patrizia; Sorace Oreste; Salvadori Piero A. (literal)
- Pagina inizio
- Pagina fine
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
- Rivista
- Note
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
- Istituto di Fisiologia Clinica del CNR, Pisa (literal)
- Titolo
- Automatic partial volume correction in cardiac PET without anatomical images: a preliminary study (literal)
- Abstract
- Aim: The partial volume effect (PVE) is one of the main sources of error in quantification of cardiac PET
imaging. Most of the algorithms for partial volume correction (PVC) are based on recovery coefficients (RC)
derived from phantom measurements and applied to well defined regions-of-interest (ROIs). Such methods
require the precise knowledge of the thickness of the structure to be corrected, and this is usually obtained by
anatomical imaging (MR, CT or US). However, anatomical images may not be always justified or available for
the specific patient. Here, we describe a novel method for automatic PVC of ECG-gated PET cardiac images,
which does not require anatomical images. The presented method simply requires a curve describing RC that
is obtained, for the specific scanner and reconstruction protocol in use, with a phantom of known geometry.
Materials and methods: The PVC algorithm consists in four steps: 1) 3D segmentation of the myocardial
walls on the gated PET image, 2) transformation of the segmented image in a 3D thickness map using a local
thickness transform (LTT), 3) transformation of the LTT image in a 3D correction mask using the phantom-based RC curve, and 4) application of the correction mask to the original image. PET cardiac image is
segmented by a thresholding method used in conjunction with an iterative deblurring method. RCs are
measured in a static phantom, simulating cardiac left ventricle (LV), made of two off-centered and
independently fillable cavities that reproduced a variable wall thickness (2 mm to 19 mm max.). The LV walls
in the phantom were filled with an initial activity concentration of 133 kBq/mL of 18F, whereas all other
structures were filled with water. Images were acquired with a PET/CT (GE Healthcare Discovery VCT Rx)
and an RC curve was derived and fitted with a 4th degree polynomial curve.
Results: The PVC method has been preliminary used on PET images. The corrected activity concentration
resulted accurate within 5% and 10% for wall thicknesses > 9 mm and 4 mm respectively. Thinner walls,
having a thickness comparable to the voxel size, cannot be recovered because of the limitations of the
segmentation method in use.
Conclusions: The proposed PVC method has an acceptable accuracy and does not necessitate any
additional information by anatomical imaging modalities. Furthermore, it can be implemented in an automatic
analysis workflow, thus avoiding manual ROI definition and/or contouring, which are both time-demanding and
operator dependent. (literal)
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