Improving diagnostic precision in amyloid brain PET imaging through data-driven motion correction

Area of Science:

• Neuroimaging
• Radiochemistry
• Medical Physics

Background:

• Head motion during brain PET/CT imaging compromises image quality and diagnostic accuracy.
• ¹⁸F-flutemetamol (FMM) PET/CT is used for amyloid deposition detection.
• Accurate imaging is crucial for diagnosing neurodegenerative diseases.

Purpose of the Study:

• To evaluate a novel head motion correction algorithm for FMM brain PET/CT.
• To assess the algorithm's impact on image quality and amyloid detection accuracy.
• To determine the algorithm's potential for routine clinical application.

Main Methods:

• Retrospective analysis of 108 FMM brain PET/CT scans.
• Application of a 3D motion correction algorithm involving signal analysis and iterative reconstruction.
• Visual review for amyloid deposition and quantitative uptake ratio (UR) analysis.

Main Results:

• Motion correction significantly improved image quality (p < 0.001).
• Enhanced interobserver agreement in visual assessment of amyloid deposition.
• Increased quantitative differences in UR in patients with head motion.

Conclusions:

• The motion correction algorithm effectively enhances image quality and diagnostic agreement.
• The algorithm is recommended for routine post-processing in amyloid PET/CT.
• Potential applicability to other brain PET radiotracers.