Transforming grayscale MRI images to color utilizing generative artiﬁcial intelligence to better understand multiple sclerosis

Journal of Central Nervous System Disease, 2025 · DOI: 10.1177/11795735241310138 · Published: January 1, 2025

Simple Explanation

Multiple sclerosis (MS) is a chronic autoimmune condition involving the brain and spinal cord that may result in permanent neurological disability. Magnetic resonance imaging (MRI) studies are used both in the diagnosis and surveillance of people with MS. Current MRI data appear black and white in appearance and grayscale values within voxels comprise the images that are seen. However, grayscale values are associated with a single variable, intensity. We hypothesized that the transformation from grayscale MRI data to color may provide additional information related to lesions resulting from MS. We were able to identify color differences in lesions when evaluating MRI images from a single subject.

Study Duration

10 Years

Participants

Single 31-year-old White male with MS

Evidence Level

Case Report

Key Findings

1
The transformation of grayscale MRI FLAIR images to color via generative AI revealed heterogeneity in the T2-hyperintensities present with some of the lesions having a more yellow appearance and others appearing more white in color
2
Previously identiﬁed lesions from the ﬁrst MRI study (Figure 1) with distinct colors appeared to have a similar appearance in 2014, although enlargement of some of the existing lesions were present.
3
For those lesions having a more yellow appearance, lower quantitative R1 (longitudinal relaxation rate represented by 1/T1 relaxation time) and R2 (transverse relaxation rate represented by 1/T2 relaxation time) and higher proton density (measure of the concentration of hydrogen protons) values were identiﬁed

Research Summary

This report demonstrated the potential value of transforming grayscale MRI images into color, visually enhancing the anatomical data and possibly providing more information that may inform on the impact of disease related to MS within the CNS. Colorizing MRI data appears to offer more intuitive data to the observer. In addition, the application of generative AI color rendering techniques may enable better appreciation of ﬁndings not apparent in grayscale. With advancing AI methods and capabilities along with the additional data that color provides in comparison to grayscale, perhaps new insights into the biology of disease may be recognized.

Practical Implications

Enhanced Visualization

Colorized MRI data may offer more intuitive and engaging visualizations for both clinicians and patients, potentially improving understanding of complex imaging data.

Improved Diagnostic Accuracy

The use of color in MRI may reveal subtle differences in lesions that are not apparent in grayscale, potentially leading to earlier and more accurate diagnoses.

Potential for New Insights

Combining colorized MRI with advanced AI techniques may unlock new insights into the underlying biology of diseases like multiple sclerosis.

Study Limitations

1
The study is based on a single case, limiting the generalizability of the findings.
2
The AI colorization technique may introduce biases or artifacts that could affect the interpretation of the images.
3
The ideal augmented intelligence methodologies for color transformation need further clarification.

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