PETPVC: a toolbox for performing partial volume correction techniques in positron emission tomography.

Area of Science:

• Medical Imaging
• Nuclear Medicine
• Image Processing

Background:

• Positron emission tomography (PET) images suffer from partial volume effects (PVEs), degrading quantitative accuracy.
• Structural information from MRI or CT is used in partial volume correction (PVC) techniques to mitigate PVEs.
• Scanner resolution effects necessitate compensation for precise PET quantification.

Purpose of the Study:

• To introduce the PETPVC toolbox, a comprehensive suite of methods for applying PVC to PET data.
• To evaluate the performance of various PVC techniques, including classic, novel, and hybrid approaches.
• To assess the impact of point-spread function (PSF) mismatch and the potential of combined PVC methods for lesion quantification.

Main Methods:

• The PETPVC toolbox implements eight core PVC techniques, combinable into 22 distinct methods.
• Simulated brain PET data were used to test PVC utility under ideal conditions and with PSF mismatch.
• Anatomy-based PVC and deconvolution-based approaches were compared, alongside novel hybrid PVC methods.

Main Results:

• Anatomy-based PVC techniques achieved complete recovery of the PET signal in cortical grey matter (GM) under ideal conditions.
• Deconvolution-based methods showed incomplete recovery due to premature iterative process termination.
• PVC techniques are sensitive to PSF mismatch, introducing bias; hybrid methods improved GM and lesion recovery.

Conclusions:

• The PETPVC toolbox provides a versatile platform for applying diverse PVC techniques to PET data.
• Hybrid PVC methods demonstrate potential for enhanced lesion quantification in PET imaging.
• The open-source, cross-platform PETPVC toolbox facilitates improved accuracy in PET data analysis.