Robust autocalibrated structured low-rank EPI ghost correction.

Area of Science:

• Medical Imaging
• Image Reconstruction
• Magnetic Resonance Imaging

Background:

• Echo-planar imaging (EPI) is prone to artifacts like ghosting.
• Existing methods like Autocalibrated LORAKS struggle with imperfect autocalibration data.
• Navigator signals are conventionally used but can be problematic.

Purpose of the Study:

• To introduce Robust Autocalibrated LORAKS (RAC-LORAKS), a novel structured low-rank method for EPI ghost correction.
• To develop a method robust to imperfect autocalibration data and eliminate the need for conventional EPI navigator signals.
• To improve EPI ghost suppression in various complex imaging scenarios.

Main Methods:

• RAC-LORAKS simultaneously estimates low-rank matrix structure using both autocalibration and EPI data.
• It incorporates complementary autocalibration information for improved EPI reconstruction within a multi-contrast joint framework.
• The method was evaluated using simulations and in vivo data, including comparisons to existing techniques.

Main Results:

• RAC-LORAKS demonstrated strong ghost elimination performance across diverse EPI acquisition types.
• Effective ghost suppression was observed in gradient-echo brain imaging, diffusion-encoded brain imaging, and cardiac imaging.
• The method proved robust even with imperfect autocalibration data.

Conclusions:

• RAC-LORAKS offers effective echo-planar imaging ghost suppression.
• The method's robustness to imperfect autocalibration data enhances its practical utility.
• RAC-LORAKS represents an advancement in EPI artifact correction.