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An electromagnetic reverse method of coil sensitivity mapping for parallel MRI - theoretical framework.

Jin Jin¹, Feng Liu, Ewald Weber

¹MedTeQ Centre, The School of Information Technology and Electrical Engineering, The University of Queensland St. Lucia, Brisbane, Qld 4072, Australia. jinjin@itee.uq.edu.au

Journal of Magnetic Resonance (San Diego, Calif. : 1997)

|September 14, 2010

Summary

This summary is machine-generated.

A new iterative optimization method for parallel MRI (pMRI) provides highly accurate coil sensitivity profiles, significantly reducing image artifacts in SENSE reconstructions compared to traditional methods.

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Area of Science:

Magnetic Resonance Imaging
Image Reconstruction
Signal Processing

Background:

Parallel MRI (pMRI) relies on accurate coil sensitivity maps for image reconstruction.
Conventional methods like polynomial fitting for sensitivity mapping can be susceptible to noise and introduce artifacts.
Improving sensitivity map fidelity is crucial for enhancing pMRI performance.

Purpose of the Study:

To introduce a novel iterative optimization method for generating high-fidelity coil sensitivity profiles in pMRI.
To compare the performance of the proposed method against traditional polynomial fitting techniques.
To demonstrate the impact of improved sensitivity maps on SENSE reconstruction quality.

Main Methods:

Developed an iterative optimization algorithm to determine coil sensitivity profiles.
Defined the optimization cost function based on the difference between raw and desired profiles.
Utilized low-frequency electromagnetic and reciprocity theories to govern the minimization process.
Compared the method's performance against polynomial fitting across various noise levels.

Main Results:

The novel method generated sensitivity profiles with noise amplitudes an order of magnitude lower than polynomial fitting.
Sensitivity maps produced by the new method led to SENSE reconstructions with significantly fewer image artifacts.
The iterative optimization approach demonstrated superior fidelity and robustness to noise.

Conclusions:

The proposed iterative optimization method offers a significant advancement in sensitivity mapping for pMRI.
High-fidelity sensitivity maps are essential for reducing artifacts in SENSE reconstructions.
This technique has potential applications in both receive and transmit MRI (e.g., Transmit SENSE).