# Frequency domain diffuse optical spectroscopy and diffuse correlation spectroscopy for assessing inspiratory muscle metabolism in mechanically ventilated patients

> **NIH NIH R21** · BOSTON UNIVERSITY (CHARLES RIVER CAMPUS) · 2022 · $200,840

## Abstract

PROJECT SUMMARY
Mechanical ventilation (MV), which is used to assist or replace spontaneous breathing in critically ill patients, led
to $27 billion in expenditures in the US in 2010, accounting for 12% of all hospital costs. In that same year there
were 2.7 episodes of MV per 1000 population, highlighting the enormous importance of this procedure. The
COVID-19 pandemic has substantially increased these numbers, although precise rates are not yet available.
MV is used, in part, to “unload”, or reduce the metabolic effort of respiratory muscles in order to redirect oxygen
delivery to vital organs. As the patients’ conditions improve, key inspiratory muscles (e.g. diaphragm, scalenes,
sternomastoid, etc.) need to take over spontaneous breathing independent of the ventilator. This “reloading” is
precarious due to muscle disuse atrophy, induced by unloading. This is further complicated by other common
conditions such as septic or cardiogenic shock, which can severely limit oxygen delivery independent of muscle
status. What’s needed is a methodology that can continuously monitor blood flow and oxygen utilization of
inspiratory muscles so that respiratory effort can be continuously optimized during MV. This project aims to
develop a comprehensive blood flow index, oxygenation, and metabolic measurement platform for inspiratory
muscle physiology by integrating wideband frequency-domain diffuse optical spectroscopy (wbDOS) and diffuse
correlation spectroscopy (DCS) to tackle this unmet need. wbDOS is a new all-digital frequency-domain DOS
technique that captures amplitude and phase measurements over a wide bandwidth of modulation frequencies
(50-500 MHz) at high speeds (>100 Hz). wbDOS and DCS will combine synergistically to provide pathlength-
corrected estimates of absolute Hb/Mb concentrations and blood flow index (BFi), allowing for the extraction of
tissue regional oxygen metabolic rate (MRO2i), a parameter directly linked to oxygen utilization. We hypothesize
that wbDOS and DCS measurements can be acquired simultaneously at high speed (>10 Hz) with parallel
detection and integrated electronics. This speed is needed to capture inspiratory/expiratory dynamics at the
respiratory rate. Additionally, we hypothesize wideband frequency-domain DOS measurements will provide
improved quantification of optical properties, BFi and MRO2i when optically integrated with DCS as compared to
single frequency FD-DOS or CW-NIRS. We will validate this through rigorous system testing using flow-channel
tissue-mimicking optical phantoms. A multi-layer inverse model will be developed to better capture inspiratory
muscle metabolism by accounting for subcutaneous lipid thickness and skin tones. We will also expand on our
recent work in Deep Neural Network (DNN) processing to develop high-speed algorithms for calculating Hb/Mb
concentrations, StO2 (%), BFi (mm2/s), and MRO2i at 10 Hz. We will conduct a feasibility study (n=10) of healthy
volunteers during respiratory muscle ...

## Key facts

- **NIH application ID:** 10482330
- **Project number:** 5R21EB031250-02
- **Recipient organization:** BOSTON UNIVERSITY (CHARLES RIVER CAMPUS)
- **Principal Investigator:** Darren Michael Roblyer
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $200,840
- **Award type:** 5
- **Project period:** 2021-09-15 → 2024-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10482330

## Citation

> US National Institutes of Health, RePORTER application 10482330, Frequency domain diffuse optical spectroscopy and diffuse correlation spectroscopy for assessing inspiratory muscle metabolism in mechanically ventilated patients (5R21EB031250-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10482330. Licensed CC0.

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