Near-infrared spectroscopy (NIRS) combined with indocyanine green (ICG) dilution is applied externally on the head to determine the cerebral hemodynamics of neurointensive care patients. We applied Monte Carlo simulation for the analysis of a number of problems associated with this method. First, the contamination of the optical density (OD) signal due to the extracerebral tissue was assessed. Second, the measured OD signal depends essentially on the relative blood content (with respect to its absorption) in the various transilluminated tissues. To take this into account, we weighted the calculated densities of the photon distribution under baseline conditions within the different tissues with the changes and aberration of the relative blood volumes that are typically observed under healthy and pathologic conditions. Third, in case of NIRS ICG dye dilution, an ICG bolus replaces part of the blood such that a transient change of absorption in the brain tissues occurs that can be recorded in the OD signal. Our results indicate that for an exchange fraction of =30% of the relative blood volume within the intracerebral tissue, the OD signal is determined from 64 to 74% by the gray matter and between 8 to 16% by the white matter maximally for a distance of d=4.5 cm.
Conventional near infrared spectroscopy (NIRS) for oxymetry has been extended recently with an indocyanine green (ICG) dye dilution method. The injection of ICG allows estimation of cerebral blood volume (CBV ), the mean transit time (mttICG) and the cerebral blood flow (CBF) from the optical density signals. However, the optical density signal obtained through the skull is substantially in influenced by extracerebral tissue.
Monte Carlo simulation is used to simulate for the first time light propagation during NIRS with ICG to characterize extracerebral contamination. Simulations are performed on the anatomical structure of an adult head obtained using 3D-magnetic resonance imaging (MRI). ICG is modelled by increasing the absorption coefficients of the tissues, taking their relative blood volumes into account. The effect of ICG on extracerebral contamination of NIRS signals is discussed.
Recently, conventional near infrared spectroscopy (NIRS) for oxymetry has been extended with an indocyanine green (ICG) dye dilution method which allows the estimation of cerebral blood flow (CBF) and cerebral blood volume (CBV). The signal obtained through the skull is substantially influenced by extracerebral tissue. In order to quantify and eliminate extracerebral contamination of the optical density signal we have applied two approaches. Firstly, we used spatially resolved spectroscopy (SRS) with a two receiver arrangement, with separations between emitter and two receivers in distances of d1=4.0cm and d2=6.5cm. The magnitude of the determined extracerebral contamination was verified with NIRS measurements in patients after brain herniation. Intracerebral circulatory arrest was confirmed by transcerebral Doppler examination. Secondly, Monte Carlo simulation was used to simulate the light propagation through the head to quantify the extracerebral contamination of the optical density signal of NIRS. The anatomical structure is determined from 3D-magnetic resonance imaging (MRI) using a voxel resolution of 0.8 x 0.8 x 0 .8 mm3 for three different pairs of T1/T2 values. We segment the MRI data to obtain a material matrix describing the composition of skin, skull, cerebral spinal fluid (CSF), grey and white matter. Each voxel in this material matrix characterizes the light absorption and dispersion coefficient of the identified material. This material matrix is applied in the Monte Carlo simulation. With SRS an extracerebral contamination of 65% of the optical density signal is extracted, while the Monte Carlo simulation results show that the extracerebral contamination decreases from 70% to 30% with increasing emitter-receiver distance. Differences between the NIRS ICG dye dilution technique and conventional NIRS oxymetry concerning the
extracerebral contamination are discussed.
KEYWORDS: Monte Carlo methods, Photons, Brain, Near infrared spectroscopy, Sensors, Magnetic resonance imaging, Tissues, Tissue optics, Absorption, Dispersion
When near infrared spectroscopy (NIRS) is applied noninvasively to the adult head for brain monitoring, extra-cerebral bone and surface tissue exert a substantial influence on the cerebral signal. Most attempts to subtract extra-cerebral contamination involve spatially resolved spectroscopy (SRS). However, inter-individual variability of anatomy restrict the reliability of SRS. We simulated the light propagation with Monte Carlo techniques on the basis of anatomical structures determined from 3D-magnetic resonance imaging (MRI) exhibiting a voxel resolution of 0.8 x 0.8 x 0.8 mm3 for three different pairs of T1/T2 values each. The MRI data were used to define the material light absorption and dispersion coefficient for each voxel. The resulting spatial matrix was applied in the Monte Carlo Simulation to determine the light propagation in the cerebral cortex and overlaying structures. The accuracy of the Monte Carlo Simulation was furthermore increased by using a constant optical path length for the photons which was less than the median optical path length of the different materials. Based on our simulations we found a differential pathlength factor (DPF) of 6.15 which is close to with the value of 5.9 found in the literature for a distance of 4.5cm between the external sensors. Furthermore, we weighted the spatial probability distribution of the photons within the different tissues with the probabilities of the relative blood volume within the tissue. The results show that 50% of the NIRS signal is determined by the grey matter of the cerebral cortex which allows us to conclude that NIRS can produce meaningful cerebral blood flow measurements providing that the necessary corrections for extracerebral contamination are included.
KEYWORDS: Image processing, Digital signal processing, Cameras, Imaging systems, Visualization, Sensors, Retina, Eye, Signal processing, Data processing
The smart camera is an intelligent compact camera system based on concepts which are taken from the human visual and nervous systems. Images are taken by a smart sensor which performs first low level image processing steps. Preprocessed images are then handed over to a digital signal processor (DSP). The DSP is responsible of high level image processing. It further reduces the image data down to essential information. Based on this data, the camera can be programmed to take decisions autonomously, e.g. halt an assembly line or remove an object from the line. Because of its visual capabilities and the built-in intelligence, the camera is independent of supervising systems, although it might update or alarm the supervising system if necessary.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.