Method and program for assessing clinical dementia measures and future outcomes using predicted age differences

JP2024524497A5Inactive Publication Date: 2025-04-01ACROBIZ USA INC
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Patent Information

Application Number
JP2023581059
Authority / Receiving Office
JP · JP
Patent Type
Applications
Current Assignee / Owner
Priority Date
2021-06-29
Filing Date
2022-03-25
Publication Date
2025-04-01
Estimated Expiration
Not applicable · inactive patent

AI Technical Summary

Technical Problem

Current methods for assessing dementia progression, such as the Clinical Dementia Rating (CDR), are limited by their inability to predict future outcomes and are reliant on clinician judgment and informant reliability, and diffusion MRI data suffer from distortion artifacts that hinder accurate analysis.

Method used

A method using brain age scores derived from white matter microstructural features in diffusion tensor imaging (DTI) to predict dementia severity and progression, incorporating a novel artifact correction technique that accounts for both magnetic susceptibility-induced distortions and intensity biases in diffusion MRI data.

Benefits of technology

The method effectively predicts dementia progression over 2-3 years, providing a surrogate marker for Clinical Dementia Rating changes by accurately correcting diffusion MRI artifacts and leveraging white matter anisotropy and diffusivity metrics.

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Abstract

1. A method for estimating an individual's Clinical Dementia Scale (CDR) and future change in CDR from predicted age difference (PAD) based on MRI data of the individual's brain, comprising the steps of: (1) acquiring MRI data of the individual's brain using a T1-weighted (T1w) imaging sequence and a diffusion tensor imaging (DTI) sequence; (2) processing the acquired T1w data to obtain white matter (WM) segments and gray matter (GM) segments; and (3) applying MNI to the WM and GM segments acquired in step (2) to obtain a deformation map of the individual's brain. (4) correcting artifacts in the DTI data; (5) obtaining a factor of anisotropy (FA) map and a mean diffusivity (MD) map for the individual in native space based on the artifact-corrected DTI data; (6) extracting FA and MD values ​​of WM regions of the individual's brain using the FA and MD maps obtained in step (5) and the deformation map obtained in step (3); (7) calculating WM PAD by inputting the extracted FA and MD of WM into an established dMRI brain age model; and (8) predicting the individual's CDR and CDR change from the WM PAD using the CDR prediction model.
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