Statistical normalization techniques for magnetic resonance imaging.

Area of Science:

• Medical Imaging
• Neuroimaging
• Image Processing

Background:

• Magnetic resonance imaging (MRI) intensities are arbitrary and vary across subjects and visits.
• Existing intensity normalization methods (e.g., histogram matching) lack biological interpretability.
• There's a need for formalized principles for comparable MRI image intensities.

Purpose of the Study:

• To establish criteria for effective MRI image normalization.
• To propose novel, biologically motivated normalization techniques for multisequence brain imaging.
• To ensure MRI intensity comparability within and across subjects and study visits.

Main Methods:

• Developed a set of criteria for image normalization.
• Proposed simple, robust, biologically motivated normalization techniques for multisequence brain imaging.
• Validated methods across thousands of brain MRI scans from Alzheimer's disease, multiple sclerosis, and healthy subjects.

Main Results:

• The proposed normalization techniques satisfy the established criteria.
• Methods demonstrated consistent interpretation across different MRI acquisitions.
• Effective normalization was achieved for diverse patient cohorts and imaging centers.

Conclusions:

• The proposed biologically motivated normalization methods offer a significant improvement for MRI analysis.
• These techniques enable consistent and interpretable MRI intensity values.
• This work provides a foundation for more reliable cross-study and cross-subject MRI comparisons.