We develop and validate a Modified Beer-Lambert law for blood flow based on diffuse correlation spectroscopy (DCS) measurements. The new formulation enables blood flow monitoring from temporal intensity autocorrelation function data taken at single or multiple delay-times. Consequentially, the speed of the optical blood flow measurement can be substantially increased. The scheme facilitates blood flow monitoring of highly scattering tissues in geometries wherein light propagation is diffusive or non-diffusive, and it is particularly well-suited for utilization with pressure measurement paradigms that employ differential flow signals to reduce contributions of superficial tissues.

Breast cancer is one of the most common cancers among women. Approximately one in eight women in the United States will develop breast cancer during their lifetime, and, of these, about 20–30% will ultimately die of the disease.¹ Thus, early detection and accurate diagnosis of breast cancer are important. Existing clinical methods used for breast cancer screening and diagnosis, however, have drawbacks. X-ray mammography, for example, has an $\sim 22\%$ false-negative rate in women under 50² and sometimes cannot accurately distinguish between benign and malignant tumors.³ Even though the false-positive rate of individual mammography is less than 10%,^{4, 5} 18% of women with no breast cancer will have undergone a biopsy after 10 mammograms.⁵ Techniques such as ultrasound and magnetic resonance imaging (MRI) are sometimes used in addition to X-ray mammography, but they have limitations such as high cost, low throughput, limited specificity (MRI), and low sensitivity (ultrasound). Thus, new methods are needed to detect cancers earlier for treatment, to detect cancers missed by mammography,^{6, 7, 8} to reduce the false-positive rate,^{4, 5} and to monitor tumor progression during cancer therapy.

We introduce and validate a pressure measurement paradigm that reduces extracerebral contamination from superficial tissues in optical monitoring of cerebral blood flow with diffuse correlation spectroscopy (DCS). The scheme determines subject-specific contributions of extracerebral and cerebral tissues to the DCS signal by utilizing probe pressure modulation to induce variations in extracerebral blood flow. For analysis, the head is modeled as a two-layer medium and is probed with long and short source-detector separations. Then a combination of pressure modulation and a modified Beer-Lambert law for flow enables experimenters to linearly relate differential DCS signals to cerebral and extracerebral blood flow variation without a priori anatomical information. We demonstrate the algorithm’s ability to isolate cerebral blood flow during a finger-tapping task and during graded scalp ischemia in healthy adults. Finally, we adapt the pressure modulation algorithm to ameliorate extracerebral contamination in monitoring of cerebral blood oxygenation and blood volume by near-infrared spectroscopy.

The critical closing pressure ( CrCP) of the cerebral circulation depends on both tissue intracranial pressure and vasomotor tone. CrCP defines the arterial blood pressure ( ABP) at which cerebral blood flow approaches zero, and their difference ( ABP − CrCP) is an accurate estimate of cerebral perfusion pressure. Here we demonstrate a novel non-invasive technique for continuous monitoring of CrCP at the bedside. The methodology combines optical diffuse correlation spectroscopy (DCS) measurements of pulsatile cerebral blood flow in arterioles with concurrent ABP data during the cardiac cycle. Together, the two waveforms permit calculation of CrCP via the two-compartment Windkessel model for flow in the cerebral arterioles. Measurements of CrCP by optics (DCS) and transcranial Doppler ultrasound (TCD) were carried out in 18 healthy adults; they demonstrated good agreement (R = 0.66, slope = 1.14 ± 0.23) with means of 11.1 ± 5.0 and 13.0 ± 7.5 mmHg, respectively. Additionally, a potentially useful and rarely measured arteriole compliance parameter was derived from the phase difference between ABP and DCS arteriole blood flow waveforms. The measurements provide evidence that DCS signals originate predominantly from arteriole blood flow and are well suited for long-term continuous monitoring of CrCP and assessment of arteriole compliance in the clinic.