Sensitivity estimation in time-of-flight list-mode positron emission tomography

Area of Science:

• Medical Imaging
• Nuclear Medicine
• Physics

Background:

• Accurate quantification in Positron Emission Tomography (PET) relies on voxel-specific sensitivity.
• Sensitivity is influenced by gamma-ray attenuation and detector variations.
• Current methods often require external data for corrections.

Purpose of the Study:

• To develop a novel approach for self-attenuation and self-normalization corrections in PET.
• To utilize Time-of-Flight (TOF) list-mode data exclusively for these corrections.
• To improve the accuracy of PET image quantification without external calibration.

Main Methods:

• Derivation of a fully Bayesian statistical model for complete data.
• Development and evaluation of algorithms based on the derived theory.
• Utilizing numerical 2D list-mode simulations with varying TOF resolutions.

Main Results:

• The proposed algorithms effectively correct for unknown attenuation in simulated PET data.
• Successful scanner normalization corrections were achieved using the new method.
• The approach demonstrated accuracy with 2D list-mode Time-of-Flight PET data.

Conclusions:

• A new method for attenuation and normalization corrections in PET is presented.
• This method leverages Time-of-Flight (TOF) list-mode data.
• The approach offers a data-driven solution for enhanced PET image accuracy.