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Abstract #3992

Automatic arterial input function determination for DSC perfusion MRI using simulation-based physics informed neural network

Muhammad Asaduddin1, HyoSeok Lee1, Eung Yeop Kim2, and Sung-Hong Park1
1Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology(KAIST), Daejeon, Korea, Republic of, 2Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea, Republic of

Synopsis

Keywords: Analysis/Processing, AI/ML Software

Motivation: Existing methods for arterial input function (AIF) selection in DSC-MRI, such as deep learning, criterion-based, and clustering-based approaches, are either inaccurate, exhibit low reproducibility, or rely on specific clinical data for training.

Goal(s): Our goal was to predict the true ground-truth AIF from baseline simulated DSC-MRI time series data without the need for specific AIF pixel selection or training with clinical data.

Approach: We utilized a physics-informed neural network (PINN) approach using a simulation-based dataset.

Results: Our model predicted a more accurate AIF compared to existing methods, resulting in perfusion maps that highlighted perfusion anomalies which were otherwise difficult to detect.

Impact: Our model was trained on a large amount of simulation data without requiring clinical data. Moreover, Input data consisted solely of baseline DSC-MRI, eliminating the need for AIF selection, whether manual or automatical.

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Keywords