Clinical evaluation of a block sequential regularized expectation maximization reconstruction algorithm in 18F-FDG...

Area of Science:

• Nuclear Medicine
• Medical Imaging
• Radiology

Background:

• Positron Emission Tomography/Computed Tomography (PET/CT) is crucial for oncologic imaging.
• Reconstruction algorithms significantly impact image quality and diagnostic accuracy.
• Ordered Subset Expectation Maximization (OSEM) is a widely used algorithm, but improvements are sought.

Purpose of the Study:

• To evaluate the clinical performance of the Block Sequential Regularized Expectation Maximization (BSREM) algorithm.
• To compare BSREM against OSEM in oncologic PET/CT studies.
• To assess image quality, artifact reduction, noise levels, and lesion detectability.

Main Methods:

• 410 PET/CT reconstructions from 41 patients with 2010 lesions were analyzed.
• Images were reconstructed using BSREM (varying beta values) and OSEM (with/without Time-of-Flight [TOF]).
• Image quality, sharpness, noise, artifacts, and lesion detectability were scored; lesion size and SUVmax were measured.

Main Results:

• BSREM with TOF showed superior performance across all evaluated categories.
• BSREM (non-TOF) significantly outperformed OSEM (non-TOF) and was comparable/better than OSEM (TOF) in most aspects.
• Increasing beta values in BSREM decreased SUVmax; TOF reconstructions yielded higher SUVmax than non-TOF.

Conclusions:

• BSREM reconstruction offers a significant improvement over OSEM for oncologic PET/CT.
• BSREM is suitable for clinical routine, particularly with TOF, for enhanced image quality and lesion detection.
• BSREM can also improve image quality in PET/CT systems without TOF capability.