title: Evaluating real-time image reconstruction in diffuse optical tomography using physiologically realistic test data.
abstract: In diffuse optical tomography (DOT), real-time image reconstruction of oxy- and deoxy-haemoglobin changes occurring in the brain could give valuable information in clinical care settings. Although non-linear reconstruction techniques could provide more accurate results, their computational burden makes them unsuitable for real-time applications. Linear techniques can be employed under the assumption that the expected change in absorption is small. Several approaches exist, differing primarily in their handling of regularization and the noise statistics. In real experiments, it is impossible to compute the true noise statistics, because of the presence of physiological oscillations in the measured data. This is even more critical in real-time applications, where no off-line filtering and averaging can be performed to reduce the noise level. Therefore, many studies substitute the noise covariance matrix with the identity matrix. In this paper, we examined two questions: does using the noise model with realistic, imperfect data yield an improvement in image quality compared to using the identity matrix; and what is the difference in quality between online and offline reconstructions. Bespoke test data were created using a novel process through which simulated changes in absorption were added to real resting-state DOT data. A realistic multi-layer head model was used as the geometry for the reconstruction. Results validated our assumptions, highlighting the validity of computing the noise statistics from the measured data for online image reconstruction, which was performed at 2 Hz. Our results can be directly extended to a real application where real-time imaging is required.
authors.affiliation: Department of Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, UK.
authors.forename: Sabrina
authors.initials: S
authors.lastname: Brigadoi
authors.affiliation: Department of Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, UK ; Department of Computer Science, University College London, London WC1E 6BT, UK.
authors.forename: Samuel
authors.initials: S
authors.lastname: Powell
authors.affiliation: Department of Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, UK.
authors.forename: Robert J
authors.initials: RJ
authors.lastname: Cooper
authors.affiliation: Department of Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, UK.
authors.forename: Laura A
authors.initials: LA
authors.lastname: Dempsey
authors.affiliation: Department of Computer Science, University College London, London WC1E 6BT, UK.
authors.forename: Simon
authors.initials: S
authors.lastname: Arridge
authors.affiliation: Department of Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, UK.
authors.forename: Nick
authors.initials: N
authors.lastname: Everdell
authors.affiliation: Department of Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, UK.
authors.forename: Jeremy
authors.initials: J
authors.lastname: Hebden
authors.affiliation: Department of Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, UK.
authors.forename: Adam P
authors.initials: AP
authors.lastname: Gibson
keywords: (100.0100) Image processing; (100.3010) Image reconstruction techniques; (100.3190) Inverse problems