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Abstract #1854

A k-Space Super-Resolution and Registration-Guided Thalamic Subregions Segmentation Model for Analyzing Thalamic Iron Changes in AD

Junjie He1,2, Dongxue Li1,3, Bangkang Fu3, Lisha Nie4, and Rongpin Wang1
1Department of Radiology, Guizhou Provincial People’s Hospital, Guiyang, China, 2College of Computer Science and Technology, Guizhou University, Guiyang, China, 3Medical College, Guizhou University, Guiyang, China, 4GE HealthCare MR Research, Beijing, China

Synopsis

Keywords: Analysis/Processing, biology, models, methods, Brain iron quantification, Quantity susceptibility mapping

Motivation: Alzheimer’s disease (AD) lacks effective treatment, and investigating iron homeostasis in specific thalamic subregions may reveal new pathological insights.

Goal(s): This study aims to identify precise imaging biomarkers and therapeutic targets through quantitative iron analysis of 50 thalamic subregions.

Approach: We applied T1-weighted super-resolution reconstruction and a registration-guided deep-learning model for segmenting 50 thalamic subregions, examining iron levels and their correlation with AD severity in 45 AD patients and 53 healthy controls.

Results: Significant iron alterations were found in 46 thalamic subregions in AD patients, with positive correlations to cognitive decline (MoCA scores).

Impact: There is currently a lack of research on quantitative iron analysis in fine-grained thalamic subregions of AD patients. This study could provide new prognostic assessments and therapeutic target references for AD research.

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Keywords