Abstract #3202

Automated T1w/T2w Processing for Reliable and Efficient Myelination Assessment in Pediatric MRI

Tianjiao Chen^1,2,3, Linlin Zhu¹, Xiaoyu Wang¹, Yitong Bian¹, Shuai Wang¹, ZeHong Cao⁴, XIanjun Li¹, Feng Shi⁴, and Jian Yang^1,2,3

¹The First Affiliated Hospital of Xi’an Jiaotong University, Xi'an, China, ²Shaanxi Engineering Research Center of Computational Imaging and Medical Intelligence, Xi’an, China, China, ³Xi’an Key Laboratory of Medical Computational Imaging, Xi’an, China, ⁴department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd, Shanghai, China

Synopsis

Keywords: Machine Learning/Artificial Intelligence, Data Processing

Motivation: Effective myelination assessment is crucial for pediatric brain development but is limited by time-consuming manual processes.

Goal(s): Automate T1w/T2w processing to enhance clinical feasibility and reliability.

Approach: Developed an automated T1w/T2w workflow for external calibration and myelination analysis.

Results: The automated method achieved high consistency with manual results and showed stronger age correlation, significantly reducing processing time.

Impact: The automated T1w/T2w processing method enhances the accuracy, speed, and clinical applicability of myelination assessment in pediatric MRI, supporting broader and more reliable clinical use.

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