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Interaction between noise suppression and inhomogeneity correction in MRI
Albert Montillo1, Jayaram Udupa1, Leon Axel2
1Univ. of Pennsylvania, Philadelphia, PA.
Proceedings of Spie--The International Society for Optical Engineering
|November 21, 2019
Summary
This study compares noise suppression algorithms for SPAMM-MRI, crucial for automated diagnosis of cardiovascular disease. Optimized algorithms improve image quality by reducing noise while preserving important structural edges.
Area of Science:
- Medical Imaging
- Cardiovascular Disease Research
- Image Analysis
Background:
- Cardiovascular disease is a leading cause of death globally.
- SPAMM-MRI aids in patient diagnosis, but requires automated image analysis for clinical use.
- Image noise and intensity inhomogeneity hinder automation of SPAMM-MRI analysis.
Purpose of the Study:
- To quantitatively compare and rank noise suppression algorithms for SPAMM-MRI.
- To investigate the interplay between inhomogeneity correction and noise suppression.
- To assess the impact of algorithm ordering on image analysis outcomes.
Main Methods:
- Quantitative comparison of noise suppression algorithms using residual noise and edge strength metrics.
- Statistical analysis of significance levels and confidence intervals.
- Evaluation of noise suppression and inhomogeneity correction interactions.
Main Results:
- Identified and ranked optimal noise suppression algorithms for SPAMM-MRI.
- Demonstrated that inhomogeneity correction can increase noise variance in thoracic regions.
- Quantified the benefits of inhomogeneity estimation for noise suppression and vice versa.
Conclusions:
- Optimized noise suppression and inhomogeneity correction are vital for automated SPAMM-MRI analysis.
- The order of applying these algorithms affects image quality and diagnostic potential.
- Further research can enhance SPAMM-MRI's clinical utility in cardiovascular disease diagnosis.
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