Low-cost embedded system for optical imaging of intrinsic signals

Abstract

This paper describes the proof-of-concept evaluation of a low-cost imaging system for obtaining functional connectivity maps of in vivo murine models. This non-contact system is based on the Raspberry Pi 3 and its V2 camera and offers a method for obtaining resting-state images of brain activity without the use of extrinsic contrast agents. The system was fully characterized in terms of dark signal, linearity, sensor noise resolution and spatial frequency response. One mouse was observed in vivo and functional connectivity maps were obtained by combining resting-state analysis and optical intrinsic signals imaging. Intra-mouse variations in functional connectivity remain consistent across multiple imaging sessions. In principle, inexpensive optical imaging of intrinsic signals allows the study of the mechanisms underlying human brain disorders in well-controlled murine models.

Multiparametric Remote Investigation in the near-IR through Optical Fiber for In Situ Measurements

“…Essentially, the heterogeneities inside the sample produce multiple scattering, whereas the absorption is predominantly linked to hemoglobin or water through the tissue [ 13 ]. This method has been successfully applied to numerous kinds of tissues, such as skin [ 14 ], breast [ 15 ], or lung [ 16 ] and to molecular concentration monitoring [ 17 ]. More particularly, DRS has been mostly beneficial in the medical field toward fast tissue diagnosis in a guided surgery for the near-IR [ 18 ], which explains that the most recent advances have allowed for the fast implementation of new solutions such as optimizing optical heads with a polarization modality [ 19 ], using multiple coherent light sources simultaneously for diffuse and autofluorescence acquisition in imaging mode [ 20 ], and the analysis which is post-processed with machine learning approach [ 21 ].…”

Sistema inteligente para el reconocimiento de figuras geométricas basado en Python con Raspberry PI

“…Se elaboró el sistema inteligente para el reconocimiento de figuras geométricas basado en Python con Raspberry Pi el cual tiene empleado varios dispositivos eléctricos, su principal componente es la placa Raspberry Pi donde va su codificación del funcionamiento y manejo (Vega-Luna, et al, 2018), tarjeta bluetooth que viene incorporado en la placa que permite la conectividad entres nuestros dispositivos o componentes, servomotor que da movimiento al sistema inteligente. También teniendo en cuenta el software Python el cual permitió hacer la codificación y diseño de la interfaz del reconocimiento de figuras geométricas (Guevara, et al, 2019).…”