Assessing the deep learning based image quality enhancements for the BGO based GE omni legend PET/CT

Meysam Dadgar; Amaryllis Verstraete; Jens Maebe; Yves D'Asseler; Stefaan Vandenberghe

doi:10.1186/s40658-024-00688-2

Assessing the deep learning based image quality enhancements for the BGO based GE omni legend PET/CT

EJNMMI Phys. 2024 Oct 16;11(1):86. doi: 10.1186/s40658-024-00688-2.

Authors

Meysam Dadgar^#¹, Amaryllis Verstraete^#², Jens Maebe², Yves D'Asseler², Stefaan Vandenberghe²

Affiliations

¹ Department of Electronics and Information Systems, Medical Image and Signal Processing, Ghent University, C. Heymanslaan 10, Ghent, Belgium. meysam.dadgar@ugent.be.
² Department of Electronics and Information Systems, Medical Image and Signal Processing, Ghent University, C. Heymanslaan 10, Ghent, Belgium.

^# Contributed equally.

Abstract

Background: This study investigates the integration of Artificial Intelligence (AI) in compensating the lack of time-of-flight (TOF) of the GE Omni Legend PET/CT, which utilizes BGO scintillation crystals.

Methods: The current study evaluates the image quality of the GE Omni Legend PET/CT using a NEMA IQ phantom. It investigates the impact on imaging performance of various deep learning precision levels (low, medium, high) across different data acquisition durations. Quantitative analysis was performed using metrics such as contrast recovery coefficient (CRC), background variability (BV), and contrast to noise Ratio (CNR). Additionally, patient images reconstructed with various deep learning precision levels are presented to illustrate the impact on image quality.

Results: The deep learning approach significantly reduced background variability, particularly for the smallest region of interest. We observed improvements in background variability of 11.8 $%$ , 17.2 $%$ , and 14.3 $%$ for low, medium, and high precision deep learning, respectively. The results also indicate a significant improvement in larger spheres when considering both background variability and contrast recovery coefficient. The high precision deep learning approach proved advantageous for short scans and exhibited potential in improving detectability of small lesions. The exemplary patient study shows that the noise was suppressed for all deep learning cases, but low precision deep learning also reduced the lesion contrast (about -30 $%$ ), while high precision deep learning increased the contrast (about 10 $%$ ).

Conclusion: This study conducted a thorough evaluation of deep learning algorithms in the GE Omni Legend PET/CT scanner, demonstrating that these methods enhance image quality, with notable improvements in CRC and CNR, thereby optimizing lesion detectability and offering opportunities to reduce image acquisition time.

Keywords: Background variability; Contrast recovery coefficient; Contrast to noise ratio; Deep learning; GE Omni Legend; Time of flight.