SWI processing using a local phase difference modulated venous enhancement filter with noise compensation.

Area of Science:

• Medical Imaging
• Neuroimaging
• Magnetic Resonance Imaging

Background:

• Susceptibility weighted imaging (SWI) is crucial for visualizing magnetic susceptibility variations in the brain.
• Current SWI processing methods face challenges with contrast-to-noise ratio (CNR) and blooming artifacts.
• Enhancing SWI quality is vital for diagnosing neurological conditions.

Purpose of the Study:

• To develop an automated filtering scheme to improve SWI.
• To enhance the contrast-to-noise ratio (CNR) and reduce blooming artifacts in SWI.
• To improve the diagnostic utility of SWI for neurological disorders.

Main Methods:

• Developed a novel weighted high-pass (WHP) filter incorporating a weighting function based on neighboring phase differences.
• Introduced neighborhood-based noise compensation weights derived from channel phase distribution, magnitude, and noise variance.
• Applied the noise-compensated WHP (WHPC) filter to in vivo SWI data acquired at 1.5T and 3.0T.

Main Results:

• The WHPC filter achieved an average CNR improvement of 68.40% compared to the homodyne high-pass (HHP) filter.
• Statistical analysis (two-tailed t-tests) revealed significant differences (p < 0.001) in phase mask separation between WHPC and HHP.
• The enhanced SWI quality was demonstrated across different magnetic field strengths (1.5T and 3.0T).

Conclusions:

• The WHPC filter effectively enhances susceptibility-related (SuR) contrast in magnitude SWI.
• The filter's performance is tuned by the mean separation of the phase mask, optimizing artifact reduction.
• The improved SWI quality facilitates the evaluation of conditions such as mild cognitive impairment, brain tumors, and hemorrhagic stroke.