An iterative reconstruction method of complex images using expectation maximization for radial parallel MRI

Area of Science:

• Medical Imaging
• Computational Imaging
• Signal Processing

Background:

• Radial k-space trajectories in MRI offer motion robustness and are suitable for parallel MRI (pMRI).
• Analytic reconstruction methods like filtered back-projection can degrade image quality with insufficient projection views.
• Existing methods struggle with highly undersampled radial MRI data.

Purpose of the Study:

• To develop a novel expectation maximization (EM) based reconstruction method for radial MRI.
• To optimize the EM method for radial parallel MRI (pMRI) using multichannel coil sensitivity information.
• To improve image reconstruction quality from undersampled radial k-space data.

Main Methods:

• Remodeled the expectation maximization (EM) algorithm for complex MRI image reconstruction.
• Formulated a reconstruction method incorporating multichannel RF coil sensitivity for radial pMRI.
• Validated the method using synthetic and in vivo MRI datasets.

Main Results:

• The proposed EM method yields superior reconstructed images compared to analytic methods, especially with highly subsampled data.
• Demonstrated monotonic convergence properties, outperforming conjugate gradient methods.
• Successfully reconstructed complex MRI images from radial trajectories.

Conclusions:

• The novel EM-based reconstruction method significantly improves image quality in radial MRI, particularly for undersampled datasets.
• The method shows promise for advanced applications in radial parallel MRI.
• Offers a robust alternative to conventional analytic reconstruction techniques.