Motion compensation for PET image reconstruction using deformable tetrahedral meshes

Area of Science:

• Medical Imaging
• Biomedical Engineering
• Computational Science

Background:

• Respiratory motion significantly challenges Positron Emission Tomography (PET) imaging accuracy.
• Classical deformable image registration (DIR) methods struggle with non-reproducible organ motion and volume variations during breathing.
• Existing limitations necessitate advanced motion correction techniques in PET reconstruction.

Purpose of the Study:

• To develop and evaluate a novel motion correction method for PET image reconstruction.
• To adapt motion estimation models, specifically finite element method (FEM) based approaches, for PET.
• To overcome the limitations of traditional DIR methods in handling respiratory organ motion.

Main Methods:

• Reconstruction of radiation activity on deforming tetrahedral meshes, moving beyond voxelized images.
• Reformulation of the tomographic reconstruction problem using a time-dependent system matrix based on tetrahedral meshes.
• Implementation of the Maximum Likelihood Expectation Maximization (MLEM) algorithm for motion-compensated reconstruction.

Main Results:

• Simulations demonstrate the capability of the tetrahedral mesh-based method to correct motion artifacts in PET images.
• The developed method shows superior performance compared to classical DIR approaches, especially for complex deformations and large volume variations.
• The approach effectively addresses the challenges posed by respiratory-induced organ motion.

Conclusions:

• The tetrahedral mesh-based motion-compensated reconstruction is a promising technique for improving PET imaging quality.
• This method offers a more robust solution for handling complex respiratory motion and tissue deformations than DIR.
• The technique can be integrated with biomechanical models for enhanced motion correction, potentially reducing the need for additional internal imaging.