Joint activity and attenuation estimation for PET with TOF data and single events

Area of Science:

• Medical Imaging
• Nuclear Medicine
• Image Reconstruction

Background:

• Maximum Likelihood Reconstruction of Activity and Attenuation (MLAA) with Time-of-Flight (TOF) PET data determines activity and attenuation maps up to scale and constant factors.
• Current methods rely on prior knowledge for constant determination, which can introduce inaccuracies due to patient variability.
• Inaccurate constants lead to quantitative errors in PET imaging.

Purpose of the Study:

• To develop a method for determining the scale and constant in TOF-MLAA without prior knowledge.
• To achieve quantitatively accurate activity and attenuation maps.
• To improve the precision of PET image reconstruction.

Main Methods:

• Utilized single events with depth-dependent attenuation factors, unlike coincidence events.
• Developed a three-step iterative algorithm: MLEM for activity, MLTR for attenuation, and single-event-based scaling for both.
• Simulated 2D XCAT phantom data (noise-free and noisy) with known scatter and randoms.

Main Results:

• The proposed method achieved approximately 1% NRMSE for the activity map and 5% NRMSE for the attenuation map in noise-free simulations.
• Results from noisy simulations were consistent with noise-free data, demonstrating robustness.
• Successfully determined unique solutions for attenuation and activity without relying on prior information.

Conclusions:

• Single events can be effectively used to determine constants in TOF-MLAA, enabling unique solutions for attenuation and activity maps.
• This approach eliminates the need for potentially inaccurate prior knowledge, enhancing quantitative accuracy.
• Future work will focus on extending the method to 3D PET imaging.