Noise reduction using a Bayesian penalized-likelihood reconstruction algorithm on a time-of-flight PET-CT scanner

Area of Science:

• Medical Imaging
• Nuclear Medicine
• Image Reconstruction Algorithms

Background:

• Positron Emission Tomography (PET) imaging is crucial for diagnosing and monitoring diseases.
• Image reconstruction algorithms aim to improve image quality by managing noise amplification.
• Ordered-Subset Expectation Maximization (OSEM) is a widely used PET reconstruction algorithm.

Purpose of the Study:

• To compare the noise properties of the Q.Clear Block Sequential Regularized Expectation Maximization (BSREM) algorithm with the OSEM algorithm.
• To evaluate the performance of BSREM in controlling noise amplification during PET image reconstruction.
• To assess the impact of BSREM on image quality metrics such as contrast recovery and noise levels.

Main Methods:

• Utilized both NEMA IQ phantom and whole-body patient data acquired on a state-of-the-art PET/CT system.
• Reconstructed phantom data using BSREM with varying beta-factors and OSEM with Point Spread Function (PSF) and Time-of-Flight (TOF) information.
• Evaluated performance using Contrast Recovery (CR), Coefficient of Variation (COV), Contrast-to-Noise Ratio (CNR), SUV ratio, metabolic active tumor volumes (MATVs), and Signal-to-Noise Ratio (SNR).

Main Results:

• BSREM demonstrated higher CR and CNR, and lower COV than OSEM across various phantom datasets.
• BSREM achieved comparable noise levels (COV) to OSEM with significantly shorter acquisition times (2-4x reduction).
• Patient data analysis showed similar trends, with BSREM reducing SNR by at least a factor of 2 while preserving contrast.

Conclusions:

• The BSREM reconstruction algorithm effectively reduces noise in PET imaging by a factor of 2-4 compared to OSEM.
• This noise reduction is achieved without compromising image contrast, offering significant advantages.
• BSREM enables potential reductions in injected radiotracer dose or acquisition time, improving patient safety and workflow efficiency.