Reduced-order modeling of light transport in tissue for real-time monitoring of brain hemodynamics using diffuse optical tomography
Reduced-order modeling of light transport in tissue for real-time monitoring of brain hemodynamics using diffuse optical tomography
This paper proposes a new reconstruction method for diffuse optical tomography using reduced-order models of light transport in tissue. The models, which directly map optical tissue parameters to optical flux measurements at the detector locations, are derived based on data generated by numerical simulation of a reference model. The reconstruction algorithm based on the reduced-order models is a few orders of magnitude faster than the one based on a finite element approximation on a fine mesh incorporating a priori anatomical information acquired by magnetic resonance imaging. We demonstrate the accuracy and speed of the approach using a phantom experiment and through numerical simulation of brain activation in a rat's head. The applicability of the approach for real-time monitoring of brain hemodynamics is demonstrated through a hypercapnic experiment. We show that our results agree with the expected physiological changes and with results of a similar experimental study. However, by using our approach, a three-dimensional tomographic reconstruction can be performed in ~3 s per time point instead of the 1 to 2 h it takes when using the conventional finite element modeling approach.
Biomedical optics, Diffuse optical tomography, Hemodynamic response, Image reconstruction, Reduced-order model
Vidal-Rosas, Ernesto E.
1da82633-b581-468e-b41a-117b6893a84d
Billings, Stephen
a67e1eb0-9795-42b0-9d3b-2bc158373cb2
Zheng, Ying
abc38a5e-a4ba-460e-92e2-b766d11d2b29
Mayhew, John E.
561ac855-01c3-499e-a7f2-ff0e42f5cc23
Johnston, David K.
9a73c140-f1c8-44c3-b2c1-3c84e90cc5d5
Kennerley, Aneurin J.
cf6cdd1e-8908-4a89-95af-bbd3c09f2efe
Coca, Daniel
322b6da4-a36b-401c-8a67-fd4b7f4d036d
February 2014
Vidal-Rosas, Ernesto E.
1da82633-b581-468e-b41a-117b6893a84d
Billings, Stephen
a67e1eb0-9795-42b0-9d3b-2bc158373cb2
Zheng, Ying
abc38a5e-a4ba-460e-92e2-b766d11d2b29
Mayhew, John E.
561ac855-01c3-499e-a7f2-ff0e42f5cc23
Johnston, David K.
9a73c140-f1c8-44c3-b2c1-3c84e90cc5d5
Kennerley, Aneurin J.
cf6cdd1e-8908-4a89-95af-bbd3c09f2efe
Coca, Daniel
322b6da4-a36b-401c-8a67-fd4b7f4d036d
Vidal-Rosas, Ernesto E., Billings, Stephen, Zheng, Ying, Mayhew, John E., Johnston, David K., Kennerley, Aneurin J. and Coca, Daniel
(2014)
Reduced-order modeling of light transport in tissue for real-time monitoring of brain hemodynamics using diffuse optical tomography.
Journal of Biomedical Optics, 19 (2), [026008].
(doi:10.1117/1.JBO.19.2.026008).
Abstract
This paper proposes a new reconstruction method for diffuse optical tomography using reduced-order models of light transport in tissue. The models, which directly map optical tissue parameters to optical flux measurements at the detector locations, are derived based on data generated by numerical simulation of a reference model. The reconstruction algorithm based on the reduced-order models is a few orders of magnitude faster than the one based on a finite element approximation on a fine mesh incorporating a priori anatomical information acquired by magnetic resonance imaging. We demonstrate the accuracy and speed of the approach using a phantom experiment and through numerical simulation of brain activation in a rat's head. The applicability of the approach for real-time monitoring of brain hemodynamics is demonstrated through a hypercapnic experiment. We show that our results agree with the expected physiological changes and with results of a similar experimental study. However, by using our approach, a three-dimensional tomographic reconstruction can be performed in ~3 s per time point instead of the 1 to 2 h it takes when using the conventional finite element modeling approach.
Text
026008_1
- Version of Record
More information
Published date: February 2014
Keywords:
Biomedical optics, Diffuse optical tomography, Hemodynamic response, Image reconstruction, Reduced-order model
Identifiers
Local EPrints ID: 489030
URI: http://eprints.soton.ac.uk/id/eprint/489030
ISSN: 1083-3668
PURE UUID: 8554d635-e5fa-4ded-acc0-909bb989d86b
Catalogue record
Date deposited: 11 Apr 2024 16:42
Last modified: 13 Apr 2024 02:08
Export record
Altmetrics
Contributors
Author:
Ernesto E. Vidal-Rosas
Author:
Stephen Billings
Author:
John E. Mayhew
Author:
David K. Johnston
Author:
Aneurin J. Kennerley
Author:
Daniel Coca
Download statistics
Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.
View more statistics