Partial volume correction of brain PET studies using iterative deconvolution in combination with HYPR denoising

Abstract

Background

Accurate quantification of PET studies depends on the spatial resolution of the PET data. The commonly limited PET resolution results in partial volume effects (PVE). Iterative deconvolution methods (IDM) have been proposed as a means to correct for PVE. IDM improves spatial resolution of PET studies without the need for structural information (e.g. MR scans). On the other hand, deconvolution also increases noise, which results in lower signal-to-noise ratios (SNR). The aim of this study was to implement IDM in combination with HighlY constrained back-PRojection (HYPR) denoising to mitigate poor SNR properties of conventional IDM.

Methods

An anthropomorphic Hoffman brain phantom was filled with an [¹⁸F]FDG solution of ~25 kBq mL⁻¹ and scanned for 30 min on a Philips Ingenuity TF PET/CT scanner (Philips, Cleveland, USA) using a dynamic brain protocol with various frame durations ranging from 10 to 300 s. Van Cittert IDM was used for PVC of the scans. In addition, HYPR was used to improve SNR of the dynamic PET images, applying it both before and/or after IDM. The Hoffman phantom dataset was used to optimise IDM parameters (number of iterations, type of algorithm, with/without HYPR) and the order of HYPR implementation based on the best average agreement of measured and actual activity concentrations in the regions. Next, dynamic [¹¹C]flumazenil (five healthy subjects) and [¹¹C]PIB (four healthy subjects and four patients with Alzheimer's disease) scans were used to assess the impact of IDM with and without HYPR on plasma input-derived distribution volumes (V _T) across various regions of the brain.

Results

In the case of [¹¹C]flumazenil scans, Hypr-IDM-Hypr showed an increase of 5 to 20% in the regional V _T whereas a 0 to 10% increase or decrease was seen in the case of [¹¹C]PIB depending on the volume of interest or type of subject (healthy or patient). References for these comparisons were the V _Ts from the PVE-uncorrected scans.

Conclusions

IDM improved quantitative accuracy of measured activity concentrations. Moreover, the use of IDM in combination with HYPR (Hypr-IDM-Hypr) was able to correct for PVE without increasing noise.

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