Short-separation regression incorporated diffuse optical tomography image reconstruction modeling for high-density functional near-infrared spectroscopy

Abstract

Short-separation (SS) regression and diffuse optical tomography (DOT) image reconstruction, two widely adopted methods in functional near-infrared spectroscopy (fNIRS), were demonstrated to individually facilitate the separation of brain activation and physiological signals, with further improvement using both sequentially. We hypothesized that doing both simultaneously would further improve the performance.Aim: Motivated by the success of these two approaches, we propose a method, SS-DOT, which applies SS and DOT simultaneously. Approach:The method, which employs spatial and temporal basis functions to represent the hemoglobin concentration changes, enables us to incorporate SS regressors into the time series DOT model. To benchmark the performance of the SS-DOT model against conventional sequential models, we use fNIRS resting state data augmented with synthetic brain response as well as data acquired during a ball squeezing task. The conventional sequential models comprise performing SS regression and DOT. Results:The results show that the SS-DOT model improves the image quality by increasing the contrast-to-background ratio by a threefold improvement. The benefits are marginal at small brain activation. Conclusions:The SS-DOT model improves the fNIRS image reconstruction quality.

scatterBrains: an open database of human head models and companion optode locations for realistic Monte Carlo photon simulations

“…However, many recent works using MCX/MMC have still used atlasbased head models (or even simpler slab or sphere geometries) while restricting the simulations to a fixed probe configuration at a single location. 24,30,32,[42][43][44] This is driven in part by difficulties in manually placing optodes and potentially limits the works' investigative scope. Recently, both Nizam et al 45 and Bürmen et al 46 have acknowledged the need in the field for open reference datasets, as well as user-friendly multiple optode placement, for both machine learning training and validation of custom MC software.…”

“…However, many recent works using MCX/MMC have still used atlas-based head models (or even simpler slab or sphere geometries) while restricting the simulations to a fixed probe configuration at a single location. 24 , 30 , 32 , 42 – 44 This is driven in part by difficulties in manually placing optodes and potentially limits the works’ investigative scope. Recently, both Nizam et al.…”