TY - JOUR
T1 - PINNing cerebral blood flow
T2 - analysis of perfusion MRI in infants using physics-informed neural networks
AU - Galazis, Christoforos
AU - Chiu, Ching En
AU - Arichi, Tomoki
AU - Bharath, Anil A.
AU - Varela, Marta
N1 - Publisher Copyright:
Copyright © 2025 Galazis, Chiu, Arichi, Bharath and Varela.
PY - 2025/2/14
Y1 - 2025/2/14
N2 - Arterial spin labelling (ASL) magnetic resonance imaging (MRI) enables cerebral perfusion measurement, which is crucial in detecting and managing neurological issues in infants born prematurely or after perinatal complications. However, cerebral blood flow (CBF) estimation in infants using ASL remains challenging due to the complex interplay of network physiology, involving dynamic interactions between cardiac output and cerebral perfusion, as well as issues with parameter uncertainty and data noise. We propose a new spatial uncertainty-based physics-informed neural network (PINN), SUPINN, to estimate CBF and other parameters from infant ASL data. SUPINN employs a multi-branch architecture to concurrently estimate regional and global model parameters across multiple voxels. It computes regional spatial uncertainties to weigh the signal. SUPINN can reliably estimate CBF (relative error −0.3±71.7−0.3±71.7), bolus arrival time (AT) (30.5±257.8)(30.5±257.8), and blood longitudinal relaxation time (T1b)(T1b) (−4.4 ±± 28.9), surpassing parameter estimates performed using least squares or standard PINNs. Furthermore, SUPINN produces physiologically plausible spatially smooth CBF and AT maps. Our study demonstrates the successful modification of PINNs for accurate multi-parameter perfusion estimation from noisy and limited ASL data in infants. Frameworks like SUPINN have the potential to advance our understanding of the complex cardio-brain network physiology, aiding in the detection and management of diseases. Source code is provided at: https://github.com/cgalaz01/supinn.
AB - Arterial spin labelling (ASL) magnetic resonance imaging (MRI) enables cerebral perfusion measurement, which is crucial in detecting and managing neurological issues in infants born prematurely or after perinatal complications. However, cerebral blood flow (CBF) estimation in infants using ASL remains challenging due to the complex interplay of network physiology, involving dynamic interactions between cardiac output and cerebral perfusion, as well as issues with parameter uncertainty and data noise. We propose a new spatial uncertainty-based physics-informed neural network (PINN), SUPINN, to estimate CBF and other parameters from infant ASL data. SUPINN employs a multi-branch architecture to concurrently estimate regional and global model parameters across multiple voxels. It computes regional spatial uncertainties to weigh the signal. SUPINN can reliably estimate CBF (relative error −0.3±71.7−0.3±71.7), bolus arrival time (AT) (30.5±257.8)(30.5±257.8), and blood longitudinal relaxation time (T1b)(T1b) (−4.4 ±± 28.9), surpassing parameter estimates performed using least squares or standard PINNs. Furthermore, SUPINN produces physiologically plausible spatially smooth CBF and AT maps. Our study demonstrates the successful modification of PINNs for accurate multi-parameter perfusion estimation from noisy and limited ASL data in infants. Frameworks like SUPINN have the potential to advance our understanding of the complex cardio-brain network physiology, aiding in the detection and management of diseases. Source code is provided at: https://github.com/cgalaz01/supinn.
KW - arterial spin labelling
KW - cardiac-brain network physiology
KW - cerebral blood perfusion
KW - neuroimaging
KW - physics-informed neural networks
UR - http://www.scopus.com/inward/record.url?scp=85219600757&partnerID=8YFLogxK
U2 - 10.3389/fnetp.2025.1488349
DO - 10.3389/fnetp.2025.1488349
M3 - Article
AN - SCOPUS:85219600757
SN - 2674-0109
VL - 5
JO - Frontiers in network physiology
JF - Frontiers in network physiology
M1 - 1488349
ER -
