A novel partial volume effects correction technique integrating deconvolution associated with denoising within an iterative PET image reconstruction.
Med. Phys.. 2015-01-21; 42(2): 804-819
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Merlin T(1), Visvikis D(2), Fernandez P(3), Lamare F(3).
(1)Université Bordeaux INCIA, CNRS UMR 5287, Hôpital de Bordeaux , Bordeaux 33 33076, France.
(2)INSERM, UMR1101, LaTIM, Université de Bretagne Occidentale, Brest 29 29609, France.
(3)Université Bordeaux INCIA, CNRS UMR 5287, Hôpital de Bordeaux, Bordeaux 33 33076, France.
PURPOSE: Partial volume effect (PVE) plays an important role in both qualitative and quantitative PET image accuracy, especially for small structures. A previously proposed voxelwise PVE correction method applied on PET reconstructed images involves the use of Lucy-Richardson deconvolution incorporating wavelet-based denoising to limit the associated propagation of noise. The aim of this study is to incorporate the deconvolution, coupled with the denoising step, directly inside the iterative reconstruction process to further improve PVE correction.
METHODS: The list-mode ordered subset expectation maximization (OSEM) algorithm has been modified accordingly with the application of the Lucy-Richardson deconvolution algorithm to the current estimation of the image, at each reconstruction iteration. Acquisitions of the NEMA NU2-2001 IQ phantom were performed on a GE DRX PET/CT system to study the impact of incorporating the deconvolution inside the reconstruction [with and without the point spread function (PSF) model] in comparison to its application postreconstruction and to
standard iterative reconstruction incorporating the PSF model. The impact of the denoising step was also evaluated. Images were semiquantitatively assessed by studying the trade-off between the intensity recovery and the noise level in the background estimated as relative standard deviation. Qualitative assessments of the developed methods were additionally performed on clinical cases.
RESULTS: Incorporating the deconvolution without denoising within the reconstruction achieved superior intensity recovery in comparison to both
standard OSEM reconstruction integrating a PSF model and application of the deconvolution algorithm in a postreconstruction process. The addition of the denoising step permitted to limit the SNR degradation while preserving the intensity recovery.
CONCLUSIONS: This study demonstrates the feasibility of incorporating the Lucy-Richardson deconvolution associated with a wavelet-based denoising in the reconstruction process to better correct for PVE. Future work includes further evaluations of the proposed method on clinical datasets and the use of improved PSF models.