PINNing cerebral blood flow: analysis of perfusion MRI in infants using physics-informed neural networks
File(s)
Published version
Author(s)
Galazis, Christoforos
Chiu, Ching-En
Arichi, Tomoki
Bharath, Anil A
Varela, Marta
Type
Journal Article
Abstract
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), bolus arrival time (AT)
(30.5 ± 257.8), and blood longitudinal relaxation time (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
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), bolus arrival time (AT)
(30.5 ± 257.8), and blood longitudinal relaxation time (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
Date Issued
2025-02-14
Date Acceptance
2025-01-20
Citation
Frontiers in Network Physiology, 2025, 5
ISSN
2674-0109
Publisher
Frontiers Media S.A.
Journal / Book Title
Frontiers in Network Physiology
Volume
5
Copyright Statement
© 2025 Galazis, Chiu, Arichi, Bharath and Varela. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
License URL
Identifier
10.3389/fnetp.2025.1488349
Subjects
physics-informed neural networks
cardiac-brain network physiology
neuroimaging
arterial spin labelling
cerebral blood perfusion
Publication Status
Published
Article Number
1488349
Date Publish Online
2025-02-14