Modelization of extended axial field-of-view PET scanners to analyze the performance improvement

Abstract

Positron Emission Tomography (PET) offers non-invasive insights into physiological processes. However, in clinical practice conventional PET scanners face limitations in comprehensive imaging due to their restricted axial field of view (FOV).This study explores the benefits and challenges of extending PET scanner AFOV using Monte Carlo simulation with GATE 9.1. The Biograph Vision Quadra scanner is a novel system based on the Digital Biograph Vision 600 scanner. Both devices use silicon photomultipliers and 3.2 x 3.2 × 20 mm Lutetium Oxyorthosilicate crystals (LSO). However, the 32 detector rings of the Quadra scanner enable a four-fold expansion in the Axial Field Of View (FOV) compared to Vision. As a result, higher sensitivity, and an enhancement of noise equivalent count rate and image quality are achieved. The purpose of this study is to analyze the effect of increasing the number of detector rings and the axial FOV on the scanner performance. To accomplish this, four different geometries are modeled using GATE (GEANT4 Application for Tomographic Emission). Based on the Biograph Vision 600 model, the length and the number of detector rings are gradually extended to reach the length of the Quadra scanner. Thus, four geometries with 8 (25.6 cm), 16 (51.2 cm), 24 (76.8 cm) and 32 (102.4 cm) detector rings are simulated, keeping the crystal configuration and the dimensions in the transaxial plane. The study evaluates sensitivity, Noise Equivalent Count Rate (NECR), and image quality parameters. Results indicate a significant increase in sensitivity and NECR with FOV extension, underscoring enhanced performance. However, artifact occurrence, particularly concentric circular patterns and background homogeneity changes, accentuates with extended AFOV. Normalization algorithms, particularly component-based normalization, mitigate these artifacts. This study contributes to the understanding of the advantages and limitations of extending PET scanner AFOV, offering insights into the effectiveness of component-based normalization for improving PET image quality.