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Updated: Jan 9, 2026

A Standardized Pipeline for Examining Human Cerebellar Grey Matter Morphometry using Structural Magnetic Resonance Imaging

A Standardized Pipeline for Examining Human Cerebellar Grey Matter Morphometry using Structural Magnetic Resonance Imaging

Published on: February 4, 2022

Multimodal structural MRI synthesis pipeline across age.

Ho-Kyun Kim, Jonghun Kim, Hyunjin Park

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|December 3, 2025

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This study developed a deep learning model to synthesize T1-weighted and T2-weighted magnetic resonance imaging (MRI) data across the lifespan. The model generates high-quality synthetic MRI, enabling reliable age-related brain changes analysis.

Area of Science:

Neuroimaging
Artificial Intelligence
Medical Image Analysis

Background:

T1-weighted (T1w) and T2-weighted (T2w) MRI are crucial for brain morphology but are resource-intensive.
Acquiring both MRI types is challenging for populations with motion issues, like children and the elderly.
Deep learning offers potential for synthesizing missing MRI contrasts.

Purpose of the Study:

To develop a deep learning-based cross-modal synthesis model for T1w and T2w MRI.
To incorporate age information across the entire lifespan into the synthesis model.
To evaluate the model's performance and its ability to capture age-related brain changes.

Main Methods:

Proposed a deep learning cross-modal synthesis model for T1w and T2w MRI.

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Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

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Published on: November 8, 2012

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Last Updated: Jan 9, 2026

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Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

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Published on: November 8, 2012

Trained and validated the model on diverse age cohorts (early development, young adulthood, elderly).

Assessed image quality using mean squared error and peak signal-to-noise ratio; analyzed age-related effects on brain metrics.

Main Results:

Achieved high similarity between actual and synthesized MRI (low MSE, high PSNR across cohorts).
Demonstrated reliable detection of age-related effects in synthesized data.
Observed expected patterns like decreased microstructure profile covariance gradient and cortical thinning in specific brain regions.

Conclusions:

The developed pipeline effectively synthesizes multimodal MRI data.
This approach is valuable for participants experiencing difficulties during MRI acquisition.
The model facilitates the study of age-related brain development and aging using synthetic MRI data.