A Review on MR Image Intensity Inhomogeneity Correction

Area of Science:

• Medical Imaging
• Image Processing
• Computational Mathematics

Background:

• Intensity inhomogeneity (IIH) is a common artifact in Magnetic Resonance (MR) imaging.
• Existing correction techniques aim to mitigate this artifact, improving image quality and diagnostic accuracy.
• Mathematical modeling plays a crucial role in developing advanced IIH correction methods.

Purpose of the Study:

• To review recent advancements in mathematical modeling for MR imaging intensity inhomogeneity.
• To compare different modeling approaches, including low-frequency, hypersurface, and statistical models.
• To discuss quantitative evaluation and comparative studies of IIH correction techniques.

Main Methods:

• Review of literature on mathematical modeling of intensity inhomogeneity in MR imaging.
• Categorization and comparison of different modeling techniques based on their principles and performance.
• Discussion of model complexity, computational cost, and dependency on image segmentation.

Main Results:

• Low-frequency models are widely used but can corrupt essential tissue information.
• Hypersurface and statistical models offer better adaptability and stability but are more computationally intensive.
• The performance of complex models is often linked to the success of integrated image segmentation.

Conclusions:

• No single model is universally superior; the choice depends on specific imaging needs and resources.
• Further research is needed for robust quantitative evaluation and comparative analysis of IIH correction methods.
• Developing more efficient and accurate mathematical models for IIH remains an active area of MR imaging research.