Year: 2022
Type: article
Abstract: Tracer-kinetic models allow for the quantification of kinetic parameters such as blood flow from dynamic contrast-enhanced magnetic resonance (MR) images. Fitting the observed data with multi-compartment exchange models is desirable, as they are physiologically plausible and resolve directly for blood flow and microvascular function. However, the reliability of model fitting is limited by the low signal-to-noise ratio, temporal resolution, and acquisition length. This may result in inaccurate parameter estimates. This study introduces physics-informed neural networks (PINNs) as a means to perform myocardial perfusion MR quantification, which provides a versatile scheme for the inference of kinetic parameters. These neural networks can be trained to fit the observed perfusion MR data while respecting the underlying physical conservation laws described by a multi-compartment exchange model. Here, we provide a framework for the implementation of PINNs in myocardial perfusion MR. The approach is validated both in silico and in vivo. In the in silico study, an overall decrease in mean-squared error with the ground-truth parameters was observed compared to a standard non-linear least squares fitting approach. The in vivo study demonstrates that the method produces parameter values comparable to those previously found in literature, as well as providing parameter maps which match the clinical diagnosis of patients.
Source: Medical Image Analysis
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $3,970
Funders: Wellcome Trust, King's College Hospital NHS Foundation Trust, National Institute for Health and Care Research, British Heart Foundation, Imperial College London, King's College London, Engineering and Physical Sciences Research Council, Centre For Medical Engineering, King’s College London, EPSRC Centre for Doctoral Training in Medical Imaging
Awards: G1211483440, G1247657515, G1271318647, King's College London Medical Engineering Centre of Research Excellence, G2176728088, G2417159676, G3031513811, EPSRC Centre for Doctoral Training in Medical Imaging, G4822818415, G5296857752, G5893523844, Automated Non-Invasive Coronary Artery Disease detection using Artificial Intelligence (ANICAD), G6312190743, Calibrated multimodality quantitative myocardial perfusion analysis, G7587803570, G8303168876, G8775246936, G938335843, G961285871
OpenAlex ID: W3108748322