FDG-PET Parametric Imaging by Total Variation Minimization

TL;DR

This paper introduces a total variation minimization method for FDG-PET parametric imaging, enhancing image quality by reducing noise while preserving spatial details, outperforming traditional filtering techniques.

Contribution

The paper proposes a novel total variation minimization approach for FDG-PET imaging that improves image quality and regional differentiation compared to standard methods.

Findings

Significant improvement in image quality demonstrated in brain phantom simulations.

Enhanced spatial homogeneity within brain regions and better regional distinction.

Outperforms Gaussian and median filtering in noise reduction and detail preservation.

Abstract

Parametric imaging of the cerebral metabolic rate for glucose (CMRGlc) using [18F]-fluorodeoxyglucose positron emission tomography is considered. Traditional imaging is hindered due to low signal to noise ratios at individual voxels. We propose to minimize the total variation of the tracer uptake rates while requiring good fit of traditional Patlak equations. This minimization guarantees spatial homogeneity within brain regions and good distinction between brain regions. Brain phantom simulations demonstrate significant improvement in quality of images by the proposed method as compared to Patlak images with post-filtering using Gaussian or median filters.