Abstract #3209

Blood-brain barrier estimation from DCE MRI using physics-based deep learning approach

Jamal Firmat Banzi^1,2, Abdul-Mojeed Olabisi ILYAS¹, Yang LIU^3,4, Huabing LIU^1,3, Yi Zhang⁵, Jianpan HUANG⁶, and Kannie W.Y. CHAN^1,3,4,7

¹Hongkong Centre for Cerebro-Cardiovascular Health Engineering, Hongkong, Hong Kong, ²Department of Informatics and Information Technology, Sokoine University, Morogoro, Tanzania, ³Department of Biomedical Engineering, City University of Hong Kong, Hongkong, Hong Kong, ⁴Shenzhen Research Institute, Shenzhen, China, ⁵Department of Biomedical Engineering, Zhejiang University, Hangzhou, China, ⁶Department of Diagnostic Radiology, The University of Hong Kong, Hongkong, Hong Kong, ⁷Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, United States

Synopsis

Keywords: Quantitative Imaging, DSC & DCE Perfusion

Motivation: Pharmacokinetic modelling of DCE MRI faces significant challenges of noisy parameter maps, unreliability, and long processing time.

Goal(s): To propose a deep learning approach for estimating blood-brain barrier (BBB) permeability in DCE MRI, comparing its performance with the standard non-linear least squares (NLLS) method.

Approach: A physics-derived neural network (DCE-PhysicsNet) was trained on simulated DCE-MRI signals from the extended Tofts-Kety model with patient-specific arterial input function.

Results: Our approach outperformed NLLS in the digital phantom with a coefficient of variation up to 50% for the estimated permeability surface area product (PS). Generally, DCE-PhysicsNet parameter estimations correlate with previous studies despite differences in approach.

Impact: The proposed deep learning approach demonstrates a reliable estimation of BBB parameters for DCE MRI using only a contrast-enhanced scan. It has the potential to facilitate the wide clinical use of DCE MRI.

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