scispace - formally typeset
P

Pascal Bailly

Researcher at University of Picardie Jules Verne

Publications -  27
Citations -  354

Pascal Bailly is an academic researcher from University of Picardie Jules Verne. The author has contributed to research in topics: Imaging phantom & Positron emission tomography. The author has an hindex of 9, co-authored 26 publications receiving 322 citations.

Papers
More filters
Journal ArticleDOI

Management of respiratory motion in PET/computed tomography: the state of the art

TL;DR: An overview of how motion is managed to overcome respiratory motion in PET/CT images and correction techniques that take account of all the counting statistics and integrate motion information before, during, or after the reconstruction process are provided.
Journal ArticleDOI

Initial clinical results for breath-hold CT-based processing of respiratory-gated PET acquisitions

TL;DR: A CT-based RG-PET processing method can be implemented in clinical practice with a small increase in radiation exposure and improves PET-CT co-registration of lung lesions and should lead to more accurate attenuation correction and thus SUV measurement.
Journal ArticleDOI

Respiratory-Gated Positron Emission Tomography and Breath-Hold Computed Tomography Coupling to Reduce the Influence of Respiratory Motion: Methodology and Feasibility

TL;DR: Respiratory-gated PET acquisition reduces the blurring effect and increases image contrast, however, Freq-based and Amp-based volumes are still influenced by inappropriate attenuation correction and misregistration of mobile lesions on CT images.
Journal ArticleDOI

Respiratory-gated 18F-FDG PET imaging in lung cancer: effects on sensitivity and specificity.

TL;DR: The CT-based method increased sensitivity and did not diminish specificity, compared with the ungated method for imaging the lower lobes and smallest lesions, which are most affected by respiratory motion.
Journal ArticleDOI

Improved attenuation correction via appropriate selection of respiratory-correlated PET data

TL;DR: The application of a BH-CT-based method decreases motion bias in PET images and increases lesion detectability and resolves issues related to both PET-to-CT misregistration and erroneous attenuation correction.