Long-axial field-of-view PET/CT improves radiomics feature reliability.

Purpose: To assess the influence of long-axial field-of-view (LAFOV) PET/CT systems on radiomics feature reliability, to assess the suitability for short-duration or low-activity acquisitions for textural feature analysis and to investigate the influence of acceptance angle.

Methods: 34 patients were analysed: twelve patients underwent oncological 2-[18F]-FDG PET/CT, fourteen [18F]PSMA-1007 and eight [68Ga]Ga-DOTATOC. Data were obtained using a 106 cm LAFOV system for 10 min.

Phantom study for Y liver radioembolization dosimetry with a long axial field-of-view PET/CT.

Purpose: The physical properties of yttrium-90 (Y) allow for imaging with positron emission tomography/computed tomography (PET/CT). The increased sensitivity of long axial field-of-view (LAFOV) PET/CT scanners possibly allows to overcome the small branching ratio for positron production from Y decays and to improve for the post-treatment dosimetry of Y of selective internal radiation therapy.

Methods: For the challenging case of an image quality body phantom, we compare a full Monte Carlo (MC) dose calculation with the results from the two commercial software packages Simplicit90Y and Hermes.

Phantom-based evaluation of yttrium-90 datasets using biograph vision quadra.

Purpose: The image quality characteristics of two NEMA phantoms with yttrium-90 (Y) were evaluated on a long axial field-of-view (AFOV) PET/CT. The purpose was to identify the optimized reconstruction setup for the imaging of patients with hepatocellular carcinoma after Y radioembolization.

Methods: Two NEMA phantoms were used, where one had a 1:10 sphere to background activity concentration ratio and the second had cold background.

Using domain knowledge for robust and generalizable deep learning-based CT-free PET attenuation and scatter correction.

Despite the potential of deep learning (DL)-based methods in substituting CT-based PET attenuation and scatter correction for CT-free PET imaging, a critical bottleneck is their limited capability in handling large heterogeneity of tracers and scanners of PET imaging. This study employs a simple way to integrate domain knowledge in DL for CT-free PET imaging. In contrast to conventional direct DL methods, we simplify the complex problem by a domain decomposition so that the learning of anatomy-dependent attenuation correction can be achieved robustly in a low-frequency domain while the original anatomy-independent high-frequency texture can be preserved during the processing.