Significance: Contamination of diffuse correlation spectroscopy (DCS) measurements of cerebral blood flow (CBF) due to systemic physiology remains a significant challenge in the clinical translation of DCS for neuromonitoring. Tunable, multi-layer Monte Carlo-based (MC) light transport models have the potential to remove extracerebral flow cross-talk in cerebral blood flow index ( CBF i ) estimates. Aim: We explore the effectiveness of MC DCS models in recovering accurate CBF i changes in the presence of strong systemic physiology variations during a hypercapnia maneuver. Approach: Multi-layer slab and head-like realistic (curved) geometries were used to run MC simulations of photon propagation through the head. The simulation data were post-processed into models with variable extracerebral thicknesses and used to fit DCS multi-distance intensity autocorrelation measurements to estimate CBF i timecourses. The results of the MC CBF i values from a set of human subject hypercapnia sessions were compared with CBF i values estimated using a semi-infinite analytical model, as commonly used in the field. Results: Group averages indicate a gradual systemic increase in blood flow following a different temporal profile versus the expected rapid CBF response. Optimized MC models, guided by several intrinsic criteria and a pressure modulation maneuver, were able to more effectively separate CBF i changes from scalp blood flow influence than the analytical fitting, which assumed a homogeneous medium. Three-layer models performed better than two-layer ones; slab and curved models achieved largely similar results, though curved geometries were closer to physiological layer thicknesses. Conclusion: Three-layer, adjustable MC models can be useful in separating distinct changes in scalp and brain blood flow. Pressure modulation, along with reasonable estimates of physiological parameters, can help direct the choice of appropriate layer thicknesses in MC models.
Significance: Contamination of diffuse correlation spectroscopy (DCS) measurements of cerebral blood flow (CBF) due to systemic physiology remains a significant challenge in the clinical translation of DCS for neuromonitoring. Tunable, multi-layer Monte Carlo-based (MC) light transport models have the potential to remove extracerebral flow cross-talk in cerebral blood flow index ( CBF i ) estimates. Aim: We explore the effectiveness of MC DCS models in recovering accurate CBF i changes in the presence of strong systemic physiology variations during a hypercapnia maneuver. Approach: Multi-layer slab and head-like realistic (curved) geometries were used to run MC simulations of photon propagation through the head. The simulation data were post-processed into models with variable extracerebral thicknesses and used to fit DCS multi-distance intensity autocorrelation measurements to estimate CBF i timecourses. The results of the MC CBF i values from a set of human subject hypercapnia sessions were compared with CBF i values estimated using a semi-infinite analytical model, as commonly used in the field. Results: Group averages indicate a gradual systemic increase in blood flow following a different temporal profile versus the expected rapid CBF response. Optimized MC models, guided by several intrinsic criteria and a pressure modulation maneuver, were able to more effectively separate CBF i changes from scalp blood flow influence than the analytical fitting, which assumed a homogeneous medium. Three-layer models performed better than two-layer ones; slab and curved models achieved largely similar results, though curved geometries were closer to physiological layer thicknesses. Conclusion: Three-layer, adjustable MC models can be useful in separating distinct changes in scalp and brain blood flow. Pressure modulation, along with reasonable estimates of physiological parameters, can help direct the choice of appropriate layer thicknesses in MC models.
Authors: Lorenzo Cortese; Giuseppe Lo Presti; Marco Pagliazzi; Davide Contini; Alberto Dalla Mora; Hamid Dehghani; Fabio Ferri; Jonas B Fischer; Martina Giovannella; Fabrizio Martelli; Udo M Weigel; Stanislaw Wojtkiewicz; Marta Zanoletti; Turgut Durduran Journal: Biomed Opt Express Date: 2021-05-11 Impact factor: 3.732
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Authors: Hasan Ayaz; Wesley B Baker; Giles Blaney; David A Boas; Heather Bortfeld; Kenneth Brady; Joshua Brake; Sabrina Brigadoi; Erin M Buckley; Stefan A Carp; Robert J Cooper; Kyle R Cowdrick; Joseph P Culver; Ippeita Dan; Hamid Dehghani; Anna Devor; Turgut Durduran; Adam T Eggebrecht; Lauren L Emberson; Qianqian Fang; Sergio Fantini; Maria Angela Franceschini; Jonas B Fischer; Judit Gervain; Joy Hirsch; Keum-Shik Hong; Roarke Horstmeyer; Jana M Kainerstorfer; Tiffany S Ko; Daniel J Licht; Adam Liebert; Robert Luke; Jennifer M Lynch; Jaume Mesquida; Rickson C Mesquita; Noman Naseer; Sergio L Novi; Felipe Orihuela-Espina; Thomas D O'Sullivan; Darcy S Peterka; Antonio Pifferi; Luca Pollonini; Angelo Sassaroli; João Ricardo Sato; Felix Scholkmann; Lorenzo Spinelli; Vivek J Srinivasan; Keith St Lawrence; Ilias Tachtsidis; Yunjie Tong; Alessandro Torricelli; Tara Urner; Heidrun Wabnitz; Martin Wolf; Ursula Wolf; Shiqi Xu; Changhuei Yang; Arjun G Yodh; Meryem A Yücel; Wenjun Zhou Journal: Neurophotonics Date: 2022-08-30 Impact factor: 4.212
Authors: Tracy M Flanders; Shih-Shan Lang; Tiffany S Ko; Kristen N Andersen; Jharna Jahnavi; John J Flibotte; Daniel J Licht; Gregory E Tasian; Susan T Sotardi; Arjun G Yodh; Jennifer M Lynch; Benjamin C Kennedy; Phillip B Storm; Brian R White; Gregory G Heuer; Wesley B Baker Journal: J Pediatr Date: 2021-05-15 Impact factor: 6.314
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