Purpose: Clinical whole-body (WB) PET images can be compensated for respiratory motion using data-driven gating (DDG). However, PET DDG images may still exhibit motion artefacts at the diaphragm if the CT is acquired in a different respiratory phase than the PET image. This study evaluates the combined use of PET DDG and a deep-learning model (AIR-PETCT) for elastic registration of CT (WarpCT) to the non attenuation- and non scatter-corrected PET image (PET NAC), enabling improved PET reconstruction.
View Article and Find Full Text PDFThe outstanding capabilities of modern Positron Emission Tomography (PET) to highlight small tumor lesions and provide pathological function assessment are at peril from image quality degradation caused by respiratory and cardiac motion. However, the advent of the long axial field-of-view (LAFOV) scanners with increased sensitivity, alongside the precise time-of-flight (TOF) of modern PET systems, enables the acquisition of ultrafast time resolution images, which can be used for estimating and correcting the cyclic motion. 0.
View Article and Find Full Text PDFRespiratory gating is the standard to prevent respiration effects from degrading image quality in PET. Data-driven gating (DDG) using signals derived from PET raw data is a promising alternative to gating approaches requiring additional hardware (e.g.
View Article and Find Full Text PDFBackground: PET-MRI is under investigation as a new strategy for quantitative myocardial perfusion imaging. Consideration is required as to the maximum scanner count rate in order to limit dead-time losses resulting from administered activity in the scanner field of view during the first pass of the radiotracer.
Results: We performed a decaying-source experiment to investigate the high count-rate performance of a PET-MR system (Siemens mMR) over the expected range of activities during a clinical study.