Improving susceptibility mapping using a threshold-based K-space/image domain iterative reconstruction approach.

Area of Science:

• Medical Imaging
• Biophysics
• Computational Neuroscience

Background:

• Quantitative susceptibility mapping (QSM) is crucial for neuroimaging.
• Ill-posed inverse problems in QSM lead to streaking artifacts.
• Accurate susceptibility quantification, especially in vessels, remains challenging.

Purpose of the Study:

• To introduce a novel iterative approach for improved susceptibility quantification.
• To reduce streaking artifacts in QSM using geometric constraints.
• To enhance the accuracy of QSM in biological tissues, particularly blood vessels.

Main Methods:

• A threshold-based k-space/image domain iterative approach was developed.
• Geometric information from the susceptibility map was used as a constraint.
• Simulations and in vivo data were used to validate the method's accuracy and robustness.

Main Results:

• The iterative approach significantly suppressed streaking artifacts.
• Bias in susceptibility quantification within vessels decreased from ~10% to 2%.
• Processing a 512×512×256 dataset required less than 30 seconds with typically three iterations.

Conclusions:

• The developed iterative method enhances susceptibility quantification accuracy.
• Streaking artifacts are reduced in whole-brain susceptibility maps from single-orientation acquisitions.
• This technique offers a robust and efficient solution for QSM.