A Machine Learning Model to Harmonize Volumetric Brain MRI Data for Quantitative Neuroradiologic Assessment of Alzheimer Disease

2024/12/18 by Damiano Archetti, Vikram Venkatraghavan, Béla Weiss +13 · 1 voice · 1 citation
Engineering · Medicine · #Medical Imaging and Analysis #Radiomics and Machine Learning in Medical Imaging #Radiation Dose and Imaging

paper · doi:10.1148/ryai.240030

openalex publication_date 2024/12/18 · openalex created_date 2024/12/19 · openalex updated_date 2026/07/31

Abstract

A multiclass Neuroharmony model was developed and evaluated against other approaches for harmonizing volumetric data in a clinical setting using a large, multicenter brain MRI dataset of individuals undergoing neuroradiologic assessment of Alzheimer disease.

Citations

• Open Access Series of Imaging Studies (OASIS): Cross-sectional MRI Data in Young, Middle Aged, Nondemented, and Demented Older Adults

Cited by

• Harmonization in Magnetic Resonance Imaging: A Survey of Acquisition, Image-level, and Feature-level Methods

Discussions

• #ThrowbackThursday – Neuroharmony model achieved state-of-the-art performance in harmonizing brain volumetric data from new MRI scanners (Jan 2025) https://doi.org/10.1148/ryai.240030 #AI #Radiomics # [bsky, 3 points, 0 comments]

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