Transcatheter aortic valve replacement (TAVR) surgery has a risk of cognitive impairment and neurological injury. Currently, there are few options for non-invasively monitoring brain activity and perfusion, with electroencephalography, transcranial Doppler, and near-infrared spectroscopy (NIRS) all having significant drawbacks. By combining NIRS with diffuse correlation spectroscopy (DCS) we can obtain a more complete picture of cerebral hemodynamics during TAVR procedures and examine the link to neurological outcomes. We show examples of post-valve replacement hemodynamic changes that correspond with worse/better patient outcomes
Real-time noninvasive cerebral blood flow monitoring during cardiac surgery could decrease rates of neurologic injury associated with hypothermic circulatory arrests (HCA). We used combined frequency domain near-infrared spectroscopy and diffuse correlation spectroscopy (FDNIRS-DCS) to measure cerebral oxygen saturation and an index of blood flow (CBFi) in 12 adults undergoing HCA. Our measurements revealed negligible CBFi during retrograde cerebral perfusion (RCP: CBFi 91.2%±3.3% drop; HCA-only: 95.5%±1.8% drop). There was a significant difference during antegrade cerebral perfusion (p = 0.003). We conclude that FDNIRS-DCS can be a powerful tool to optimize cerebral perfusion and that RCP’s efficacy needs to be further examined.
Non-invasive monitoring of cerebral blood flow at the bedside using diffuse correlation spectroscopy is being investigated as a potential tool to improve brain health outcomes after surgery. In this work we characterize the performance of diffuse correlation spectroscopy measurements in assessing cerebral blood flow in the presence of systemic physiology interference through measurements on several healthy volunteers during CO2 inhalation. We report group averaged responses and the role of multi-layer models in increasing the accuracy of CBF estimates. We compare optical blood flow recordings with transcranial Doppler ultrasound and MRI ASL data.
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 (CBFi) estimates.
Aim: We explore the effectiveness of MC DCS models in recovering accurate CBFi 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 CBFi timecourses. The results of the MC CBFi values from a set of human subject hypercapnia sessions were compared with CBFi 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 CBFi 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.
Diffuse correlation spectroscopy (DCS) is an increasingly widespread non-invasive technology to measure tissue perfusion. Extending this technique into adult brain monitoring to assess real-time cerebral blood flow (CBF) requires addressing the influence of extracerebral contributions on DCS measurements. We compare several Monte Carlo based forward simulation models on the efficacy of CBF isolation, including ones generated directly from individual subject MRI scans. We conclude that a multi-layer curved surface representation is beneficial, and that the traditional single-layer homogenous model is insufficient; however, detailed structural information such as cortical folding represented in an individualized tissue-specific model may not be needed.
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