Dual Multiplicative Intrinsic Components Optimization for Enhanced MRI Bias Field Correction

The Dual Multiplicative Intrinsic Components Optimization (DMICO) technique is presented in this study to enhance magnetic resonance imaging (MRI) bias field correction and tissue segmentation. More specifically, the research focuses on resolving difficulties of intensity inhomogeneity in MRI illustrations. Through the use of DMICO, MRI data is separated into bias field and intrinsic image components. This allows for optimum energy reduction, which improves both stability and computing efficiency. The usefulness of DMICO has been shown via testing on 1.5T and 3T MRI datasets. It successfully outperformed state-of-the-art approaches when it came to segmentation tasks. The method was able to attain a Jaccard Similarity Index (JSI) of up to 0.94 and a segmentation accuracy of 96%, demonstrating considerable gains in high-field MRI instances that had large intensity variability. Additionally, the recurrent refining of our method improves energy convergence and decreases artifacts, which ultimately results in improved tissue delineation in complicated three-dimensional imaging. The accuracy and effectiveness of this framework make it an attractive option for applications in clinical MRI, which might possibly facilitate more precise diagnosis and treatment planning.