Measuring intracranial pressure (ICP) is typically a highly invasive procedure, in which a ventricular catheter or pressure sensor is placed into the brain. To improve the availability of ICP measurements in non-intensive care patients and research and to reduce the invasiveness and underlying risks of ICP sensing, we developed a non-invasive method to measure ICP with Diffuse Correlation Spectroscopy (DCS) and machine learning. ICP baseline changes were induced in non-human primates <i>(Macaca mulatta)</i> through adjusting the height of a saline reservoir connected to the lateral ventricle via a catheter. ICP was precisely measured with an invasive parenchymal pressure sensor. Cerebral blood flow (CBF) was measured with DCS. The DCS system was operated by a software correlator able to resolve cardiac pulse waves at a sampling rate of 100Hz. To increase signal-to-noise ratio, multiple cardiac pulse waves in CBF were averaged based on systolic peak maximum in invasively measured arterial blood pressure. We hypothesized that the cerebral blood flow pulse waves will change their shape with increasing ICP. The shape of the curve was expressed in numerical features and passed into a regression forest training algorithm. Preliminary results show successful prediction of underlying ICP baselines by the decision forest in one animal. The prediction of non-invasive ICP was achieved with a sampling rate of 1 Hz, an equivalent of about 120 averaged pulses. A larger data set for increased generalizability is the next step to push this approach further.
Diffuse correlation spectroscopy (DCS) is an optical method for non-invasive measurements of blood flow in deep tissue microvasculature, such as the brain, without the need for tracers or ionizing radiation. The technique relies on determining temporal autocorrelations of light intensity fluctuations which arise due to time changing speckle patterns of moving scatterers when illuminated by a long coherence length laser. Measurements of blood flow using DCS have extensively been validated and have found some clinical translation already. High temporal resolution by fast sampling of the autocorrelation curves has recently been achieved by software based correlators. Here we demonstrate a new software correlator approach which uses components that are an order of magnitude cheaper than current approaches. We will present on the instrument design, as well as measurements of pulsatile blood flow on healthy volunteers. We will show blood flow measurements with a signal bandwidth of 50Hz and present on signal to noise ratios (SNR) of extracted pulse waveforms as a function of sampling rate. We will show how using an EKG based timing of the signal for averaging increases the fidelity of extracting the blood flow waveform even in low SNR environments. We will further present results of the pulsatile waveforms and the latency of the dicrotic notch as affected by posture changes in healthy volunteers.
During neoadjuvant chemotherapy for breast cancer, little information is available on the response or non-response of the tumor to the treatment. Pathologic complete response is correlated with survival, but patients and clinicians both must wait until after the patient undergoes surgery and the resected tissue is analyzed in order to assign pathologic response. Because structural imaging modalities and clinical palpation are poor predictors of pathologic response, there is need for an inexpensive imaging method which is sensitive to the changing physiology of the tumor. Such a method should be noninvasive, to permit frequent monitoring during therapy. Near-infrared optical imaging has already shown promise for monitoring neoadjuvant chemotherapy, with measurement of hemodynamics providing additional information over baseline chromophore concentrations. These contrasts rely on the highly vascularized nature of most breast tumors, as well as the abnormal vasculature, which can produce a different response to perturbations than healthy tissue. Here we describe the development of a new held-held spatial-frequency domain imaging (SFDI) device, to be used for measuring the response of breast tissue to local compression. Device design is described, as well as validation on optical phantoms, and in vivo. Compression studies were performed in soft optical phantoms containing stiff, tumor-mimicking inclusions, which indicate the potential for compression to be used to bring stiff lesions within a depth which can be measured with SFDI. Additionally, the hemodynamic response of pressure cuff venous occlusion is described, measured on the forearm, and this response is contrasted with the hemodynamic response to local tissue compression.
Guiding treatment in traumatic brain injury based on managing and optimizing cerebral perfusion pressure, which is the difference between mean arterial blood pressure and intracranial pressure (ICP), has been demonstrated to improve patient outcome. However, this requires ICP to be measured, which currently is only possible by placing pressure probes inside the brain. The feasibility of optical systems to measure ICP non-invasively has shown preliminary promising evidence of feasibility. To pursue the goal of non-invasive ICP acquisition further, an understanding of the influence of different pressure changes on the brain and their hemodynamic response is necessary. To investigate the frequency content of hemodynamic reactions to pressure changes in both ICP as well as arterial blood pressure (ABP), we induced changes of both pressures in non-human primates. We then demonstrate that ABP and ICP changes both influence cerebral blood flow and hemoglobin concentrations, measured with diffuse correlation spectroscopy (DCS) and near-infrared spectroscopy (NIRS), respectively. We found that the magnitude of induced oscillations is dependent on the frequency of the oscillation. Our data suggests, changes in ABP and ICP influence the hemodynamics differently, which we can use as a basis for non-invasive ICP measurements.