NEMA NU 2-2018 performance evaluation of a new generation digital 32-cm axial field-of-view Omni Legend PET-CT

TL;DR

This study evaluates the performance of GE's new digital Omni Legend PET-CT with extended field-of-view, demonstrating superior imaging metrics compared to previous models, and introduces neural network corrections to enhance non-Time-of-Flight imaging quality.

Contribution

First clinical digital PET-CT with extended FOV evaluated using NEMA NU2-2018, introducing neural network corrections to improve non-ToF image quality.

Findings

Superior spatial resolution and sensitivity compared to previous models

Neural network corrections improve image quality without ToF capabilities

Performance surpasses other GE scanners in most NEMA tests

Abstract

A NEMA performance evaluation was conducted on the new General Electric (GE) digital Omni Legend PET-CT system with 32-cm extended field-of-view. This study marks the introduction of the first-ever commercially available clinical digital bismuth germanate technology. Testing was performed in accordance with the NEMA NU2-2018 standard. A comparison was made with the performance of two other commercial GE scanners with extended fields-of-view. A digital lutetium yttrium orthosilicate system (Discovery MI - 6 ring) and a non-digital bismuth germanate system (Discovery IQ). For the Omni assessment, the tangential, radial, and axial spatial resolutions at 1 cm radial offset were measured as 3.76 mm, 3.73 mm, and 4.25 mm FWHM. The total system sensitivity to a line source at the center was 44.36 cps/kBq. The peak NECR was 501 kcps at 17.8 kBq/mL. The scatter fraction at NECR peak was 35.48%, and the maximum count-rate error at and below NEC peak was 5.5%. Sphere contrast recovery coefficients were from 52% (10 mm) to 93% (37 mm). The system does not use time of flight; thus, no assessment of timing resolution was made. The PET-CT co-registration accuracy was 2.4 mm. The performance of the Omni Legend surpassed that of the Discovery MI on all NEMA tests, except for assessments of background variability (image noise). Time of flight is associated with inherent improvements in signal-to-noise ratio. In lieu of time of flight capabilities, the Omni provides software corrections in the form of a pre-trained neural network (trained on non-ToF to ToF). With such corrections, average performance is competitive when compared to ToF systems. Further validation is required to optimize clinical imaging protocols and hyperparameters associated with such software corrections and to examine the effect of non-linear corrections with varying target size, particularly for real world, clinical scans.