Diffuse Optical Tomography Using Bayesian Filtering in the Human Brain

• Main Authors: Estefania Hernandez-Martin, Jose Luis Gonzalez-Mora
• Format: Article
• Language: English
• Published: MDPI AG 2020-05-01
• Series: Applied Sciences
• Subjects: diffuse optical imaging; image reconstruction algorithms; Bayesian filtering
• Online Access: https://www.mdpi.com/2076-3417/10/10/3399

The present work describes noninvasive diffuse optical tomography (DOT), a technology for measuring hemodynamic changes in the brain. These changes provide relevant information that helps us to understand the basis of neurophysiology in the human brain. Advantages, such as portability, direct measurements of hemoglobin state, temporal resolution, and the lack of need to restrict movements, as is necessary in magnetic resonance imaging (MRI) devices, means that DOT technology can be used both in research and clinically. Here, we describe the use of Bayesian methods to filter raw DOT data as an alternative to the linear filters widely used in signal processing. Common problems, such as filter selection or a false interpretation of the results, which is sometimes caused by the interference of background physiological noise with neural activity, can be avoided with this new method.