scholarly journals Validation of the Image Registration Technique from Functional Near Infrared Spectroscopy (fNIRS) Signal and Positron Emission Tomography (PET) Image

2020 ◽  
Vol 4 (9) ◽  
pp. 63-69
Keyword(s):  
Positron Emission Tomography ◽  
Infrared Spectroscopy ◽  
Image Registration ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Emission Tomography ◽  
Functional Near Infrared Spectroscopy ◽  
Registration Method ◽  
Positron Emission ◽  
The Brain

: Functional near infrared spectroscopy (fNIRS) is an imaging system that can measure hemodynamic changes of the brain. However, the system incapability to measure beyond the brain cortex region make it usage less appealing for in-depth brain studies. To overcome this, many researchers combine fNIRS with other imaging modalities to gain better understanding of the brain activities. In this paper, we described the theory of the registering fNIRS signals and positron emission tomography (PET) image method and performed experiments to validate it. The registration method was validated using specially designed phantom for fNIRS and PET. Polaris system was used to track the position of the phantom which is based on the Polaris markers during fNIRS and PET procedures. The Polaris markers share the same coordinate, thus the fNIRS and PET were calibrated to each other through these markers. To register the fNIRS signal on the PET image, the phantom position in fNIRS coordinate is translated to PET coordinate which allow the probe and the markers being coordinated in PET. Polaris markers were used as the references marker to determine the transformation matrices. The result shows that the fNIRS channel can be viewed on the PET image of the phantom. The transformation error from Polaris to PET is less than 1.00 mm and the precision test is less than 0.1mm while the accuracy is less than 2.8 mm. This result suggests that our theory on the registration method could be used for multimodal image registration between fNIRS and other modalities.

A Review on Brain Imaging Techniques for BCI Applications

2016 ◽  
pp. 39-70
Author(s):  
Saugat Bhattacharyya ◽  
Anwesha Khasnobish ◽  
Poulami Ghosh ◽  
Ankita Mazumder ◽  
D. N. Tibarewala
Keyword(s):  
Magnetic Resonance Imaging ◽  
Positron Emission Tomography ◽  
Magnetic Resonance ◽  
Brain Imaging ◽  
Near Infrared ◽  
Emission Tomography ◽  
Resonance Imaging ◽  
Functional Near Infrared Spectroscopy ◽  
Positron Emission ◽  
The Brain

Evolution has endowed human race with the most adroit brain, and to harness its potential to the fullest the concept of brain computer interface (BCI) has emerged. One of the most crucial components of BCI is the technique of brain imaging. The first approach in the field of brain imaging was to measure the electrical and magnetic activity of the brain, the techniques being known as Electroencephalography and Magnetoencephalography. Striving for furtherance, researchers came up with another alternative known as Magnetic Resonance Imaging. But it being confined to only structural imaging, the functional aspects of brain were mapped using functional magnetic resonance imaging. A similar but comparatively newer neuroimaging modality is Functional Near Infrared Spectroscopy. Transcranial Magnetic Stimulation neuro-physiological technique is based on the principle of electromagnetic induction. Based on nuclear medicine the brain imaging technologies that are widely explored in the world of BCI are Positron Emission Tomography and Single Positron Emission Tomography.

scholarly journals Changes in cerebral oxygenation and cerebral blood flow during hemodialysis – A simultaneous near-infrared spectroscopy and positron emission tomography study

2018 ◽  
Vol 40 (2) ◽  
pp. 328-340 ◽  
Author(s):  
Harmke A Polinder-Bos ◽  
Jan Willem J Elting ◽  
Marcel JH Aries ◽  
David Vállez García ◽  
Antoon TM Willemsen ◽  
...  
Keyword(s):  
Positron Emission Tomography ◽  
Blood Flow ◽  
Cerebral Blood Flow ◽  
Infrared Spectroscopy ◽  
Oxygen Content ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Emission Tomography ◽  
Positron Emission ◽  
Relative Change

Near-infrared spectroscopy (NIRS) is used to monitor cerebral tissue oxygenation (rSO2) depending on cerebral blood flow (CBF), cerebral blood volume and blood oxygen content. We explored whether NIRS might be a more easy applicable proxy to [15O]H2O positron emission tomography (PET) for detecting CBF changes during hemodialysis. Furthermore, we compared potential determinants of rSO2 and CBF. In 12 patients aged ≥ 65 years, NIRS and PET were performed simultaneously: before (T1), early after start (T2), and at the end of hemodialysis (T3). Between T1 and T3, the relative change in frontal rSO2 (ΔrSO2) was −8 ± 9% ( P = 0.001) and −5 ± 11% ( P = 0.08), whereas the relative change in frontal gray matter CBF (ΔCBF) was −11 ± 18% ( P = 0.009) and −12 ± 16% ( P = 0.007) for the left and right hemisphere, respectively. ΔrSO2 and ΔCBF were weakly correlated for the left (ρ 0.31, P = 0.4), and moderately correlated for the right (ρ 0.69, P = 0.03) hemisphere. The Bland-Altman plot suggested underestimation of ΔCBF by NIRS. Divergent associations of pH, pCO2 and arterial oxygen content with rSO2 were found compared to corresponding associations with CBF. In conclusion, NIRS could be a proxy to PET to detect intradialytic CBF changes, although NIRS and PET capture different physiological parameters of the brain.

A Review on Brain Imaging Techniques for BCI Applications

2017 ◽  
pp. 300-330 ◽  
Author(s):  
Saugat Bhattacharyya ◽  
Anwesha Khasnobish ◽  
Poulami Ghosh ◽  
Ankita Mazumder ◽  
D. N. Tibarewala
Keyword(s):  
Magnetic Resonance Imaging ◽  
Positron Emission Tomography ◽  
Magnetic Resonance ◽  
Brain Imaging ◽  
Near Infrared ◽  
Emission Tomography ◽  
Resonance Imaging ◽  
Functional Near Infrared Spectroscopy ◽  
Positron Emission ◽  
The Brain

Evolution has endowed human race with the most adroit brain, and to harness its potential to the fullest the concept of brain computer interface (BCI) has emerged. One of the most crucial components of BCI is the technique of brain imaging. The first approach in the field of brain imaging was to measure the electrical and magnetic activity of the brain, the techniques being known as Electroencephalography and Magnetoencephalography. Striving for furtherance, researchers came up with another alternative known as Magnetic Resonance Imaging. But it being confined to only structural imaging, the functional aspects of brain were mapped using functional magnetic resonance imaging. A similar but comparatively newer neuroimaging modality is Functional Near Infrared Spectroscopy. Transcranial Magnetic Stimulation neuro-physiological technique is based on the principle of electromagnetic induction. Based on nuclear medicine the brain imaging technologies that are widely explored in the world of BCI are Positron Emission Tomography and Single Positron Emission Tomography.

scholarly journals Functional Near Infrared Spectroscopy (fNIRS) based Odor Detection and Classification using Functional Data Analysis

2021 ◽  
Author(s):  
Faezeh Moradi ◽  
Shima T. Moein ◽  
Issa Zakeri ◽  
Kambiz Pourrezaei
Keyword(s):  
Infrared Spectroscopy ◽  
Data Analysis ◽  
Near Infrared Spectroscopy ◽  
Functional Data Analysis ◽  
Functional Data ◽  
Near Infrared ◽  
Stimulus Condition ◽  
Functional Near Infrared Spectroscopy ◽  
Odor Detection ◽  
The Brain

AbstractAn objective approach for odor detection is to analyze the brain activity using imaging techniques during the odor stimulation. In this study, Functional Near Infrared Spectroscopy (fNIRS) is used to record hemodynamic response from the frontal region of the brain by using a 4-channel fNIRS system. The fNIRs data is collected during the odor detection task in which the subjects were asked to press a button when they detect the given odor. Functional Data Analysis (FDA) was applied on fNIRs data to convert discrete measured samples of data to continuous smooth curves. The FDA method enables us to use the bases coefficients of fNIRS smoothed curves for features that represent the shape of the raw fNIRS signal. With the learning algorithm that we proposed, these features were used to train the support vector machine classifier. We evaluated the odor detection problem, in two binary classification cases: odorant vs. non-odorant and odorant vs. fingertapping. The model achieved a classification accuracy of 94.12% and 97.06% over the stimulus condition in the two cases, respectively. Moreover to find the actual predictors we used the extracted defined features (slope, standard deviation, and delta) to train our classifier. We achieved an average accuracy of 91.18 % on classifying odorant vs. non-odorant and an accuracy of 94.12% for odorant vs. fingertapping on the stimulus condition. The results determined that fNIRs signals of odorant and non-odorant are distinguishable without being affected by the motor activity during the experiment.These findings suggest that fNIRs measurement on the forehead could be potentially used for objective and comparably inexpensive assessment of odor detection in cases that the subjective report is unreliable.

scholarly journals Brain–Computer Interfacing Using Functional Near-Infrared Spectroscopy (fNIRS)

Biosensors ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 389
Author(s):  
Kogulan Paulmurugan ◽  
Vimalan Vijayaragavan ◽  
Sayantan Ghosh ◽  
Parasuraman Padmanabhan ◽  
Balázs Gulyás
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Brain Function ◽  
Near Infrared ◽  
Functional Near Infrared Spectroscopy ◽  
Non Invasive ◽  
Neuron Function ◽  
Brain Computer Interfacing ◽  
The Brain ◽  
Computer Interfacing

Functional Near-Infrared Spectroscopy (fNIRS) is a wearable optical spectroscopy system originally developed for continuous and non-invasive monitoring of brain function by measuring blood oxygen concentration. Recent advancements in brain–computer interfacing allow us to control the neuron function of the brain by combining it with fNIRS to regulate cognitive function. In this review manuscript, we provide information regarding current advancement in fNIRS and how it provides advantages in developing brain–computer interfacing to enable neuron function. We also briefly discuss about how we can use this technology for further applications.

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