Phase unwrapping with a rapid opensource minimum spanning tree algorithm (ROMEO).

Area of Science:

• Medical Imaging
• Magnetic Resonance Imaging (MRI)
• Computational Imaging

Background:

• Phase unwrapping is critical for accurate MRI, especially in challenging conditions like high magnetic fields or near implants.
• Existing methods can be slow and less reliable in complex phase topographies, limiting their use in large-scale studies.

Purpose of the Study:

• To develop a rapid and accurate MRI phase-unwrapping technique for challenging phase topographies.
• The technique should be fast enough for large-group studies, including Quantitative Susceptibility Mapping and functional MRI.
• Address limitations of current methods in high magnetic fields, near metal implants, and in postoperative cavities.

Main Methods:

• Proposed a path-following phase-unwrapping algorithm named ROMEO.
• ROMEO estimates signal coherence in space and time, using MRI magnitude and phase information.
• A quality map guides unwrapping via a minimum spanning tree algorithm, tested against PRELUDE and BEST PATH at various field strengths (3T, 7T, 9.4T).

Main Results:

• ROMEO demonstrated higher reliability and excellent temporal stability for multi-echo/multi-time-point data compared to PRELUDE and BEST PATH.
• The algorithm does not require image masking and delivers results within seconds, even for large, complex datasets.
• Achieved unwrapping in 9 seconds for a 7T head dataset (208 × 208 × 96 matrix, 31 echoes).

Conclusions:

• ROMEO is faster and more accurate than existing exact phase-unwrapping methods.
• Its speed enables potential on-console application, well within typical MRI acquisition times.
• The algorithm provides exact results rapidly, enhancing the feasibility of advanced MRI techniques in large studies.