Conveners
Image reconstruction for PET/MR and TB-PET
- Marco Aiello
MRI-guided PET reconstruction can potentially reduce noise and increase spatial resolution. However, the balance between measured data and prior information usually requires manual tuning depending on the measured statistics and desired image quality. This work presents an adaptive method of MRI-guided PET reconstruction which does not require manual tuning, and is robust against a wide range...
The extended number of detectors in large-field-of-view (LFOV) PET provides the possibility to increase the number of lines-of-response (LORs), as axial acceptance angles (α) become significantly larger when compared with conventional PET. Considering a small region inside the LFOV, however, its corresponding LORs become a very small fraction of the total detected signal and, hence, their...
This work presents the study of the performance of the TRIMAGE brain PET scanner obtained through experimental detectors characterization, simulated phantom acquisitions and image reconstruction optimization. The TRIMAGE scanner uses dual-layer staggered LYSO:Ce crystal matrices coupled to silicon photomultipliers (SiPM). The dual layer architecture provides depth of interaction (DOI)...
Introduction: Breathing related patient motion during PET scans causes image artifacts, notably spatially variant blurring and degradation of contrast recovery. Dealing with these artifacts commonly involves respiratory gating, i.e. splitting the acquisition data into several temporal bins ("gates") depending on the respiratory cycle. With registration of the different gated images to one...
Introduction: The possibility of low-dose positron emission tomography (PET) imaging using high sensitivity long axial field of view (FOV) PET/computed tomography (CT) scanners makes CT a critical radiation burden in clinical applications. Artificial intelligence has shown the potential to generate PET images from non-corrected PET images. Our aim in this work is to develop a CT-free...
