DEVELOPMENT OF DIFFUSE OPTICS FOR BED-SIDE MONITORING

University Of Pennsylvania, Philadelphia PA

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Abstract

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Diffuse optics holds a great deal of promise for implementation as a bedside monitor of blood flow, blood oxygen saturation and oxygen metabolism in the brain. Near-infrared spectroscopy (NIRS) for measuring blood oxygen saturation and total hemoglobin concentration has been under development for some time, and significant progress has been made. Our laboratory has played a leading role at the forefront of this subfield. Recently, we have introduced a new technology called diffuse correlation spectroscopy (DCS) that permits us to measure tissue blood flow. By combining these two methodologies together in a hybrid technology, we can now obtain a better picture of the underlying brain physiology and can even estimate brain oxygen metabolism as a surrogate marker of cerebral well-being. The key feature of the diffuse optical approach is its potential as a continuous, non-invasive, quantitative monitor at the bedside. We expect that it will have future applications to other brain disorders, but because of the clear rationale and immense public health significance of stroke, we have recently elected to focus effort on the management of acute, cortical strokes. By developing this bedside monitor, we hope that patient management that often focuses on optimization of cerebral blood flow can be utilized and individualized based on the underlying pathophysiology of each patient. We have now demonstrated their use in functional activation studies of intact human brain and patients with acute stroke. Further theoretical, algorithmic, and technological developments are necessary to improve the fidelity of the data.