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¹Commissariat à l'Énergie Atomique (CEA), Institut d'Imagerie BioMédicale (I2BM), Service Hospitalier Frédéric Joliot (SHFJ), Orsay F-91401, France. simon.stute@cea.fr

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Summary

A novel method using a post-smoothing technique effectively eliminates edge artifacts in positron emission tomography (PET) iterative reconstructions. This approach improves image quality by balancing noise and spatial resolution without compromising contrast recovery.

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Area of Science:

Medical Imaging
Computational Physics
Image Reconstruction

Background:

Iterative reconstructions in Positron Emission Tomography (PET) require accurate system matrix (SM) modeling for optimal spatial resolution.
Realistic SM modeling can lead to undesirable edge artifacts due to ill-posed inverse problems and band-limited data.
Image-based point-spread function (PSF) reconstructions are susceptible to these artifacts.

Purpose of the Study:

To propose and evaluate a practical method for suppressing edge artifacts in PET iterative reconstructions.
To investigate the impact of reconstruction parameters and PSF modeling on edge artifact suppression.
To assess the performance of the proposed method under realistic simulation conditions.

Main Methods:

A method of sieves approach was employed, involving standard PSF reconstruction followed by post-smoothing using the PSF as a convolution kernel.
Analytical simulations were used to study noise-free data with an exactly known PSF.
Monte-Carlo simulations were performed to evaluate the method with realistic data, including unknown SM and noise.

Main Results:

The proposed method successfully suppressed edge artifacts when the PSF model was accurate, although requiring more iterations.
Under realistic conditions, the choice of geometric projector and PSF model width did not significantly affect noise or contrast recovery.
Equivalent results were achieved using either blobs or voxels for PSF modeling.

Conclusions:

The proposed post-smoothing method effectively removes edge artifacts in PET iterative reconstructions.
This technique offers a practical approach for fast reconstructions using simplified models, yielding artifact-free images.
The method provides an improved trade-off between noise and spatial resolution compared to standard reconstructions.