Accurate and reliable non-invasive monitoring of early systemic disease—such as ongoing hemorrhage, sepsis, and acute respiratory disease like COVID-19—is one of the largest unmet needs in biomedicine. An early alert to progression with high sensitivity and an acceptable false-positive rate would allow medical staff to risk-stratify patients, saving resources, lives, and in the context of pandemic disease, minimize staff exposure. Noninvasive technologies have thus far failed to produce a reliable early detection system, reflecting the limitation of uniplex approaches to describe complex pathophysiology. Our team, in collaboration with an STTR start-up, have developed an optico-impedance system combining near-infrared spectroscopy and electrical impedance tomography measured at three locations (thorax, abdomen, limb) together with machine learning methods to provide exceptional diagnostic performance in systemic disease. The optical portion consists of 6 pairs of time-multiplexed red and IR LEDs embedded in custom 3D-printed probes, which are each connected to the leg of a trifurcated fiber bundle, allowing measurement of three-location, two-distance broadband 550-950 nm spectra using a single commercial spectrometer. Data is demultiplexed and analyzed using derivative spectroscopy to quantify oxy/deoxyhemoglobin. Additional diagnostic signal was obtained from: impedance tomography and spectroscopy, ECG and plethysmography. In one of the largest porcine hemorrhage studies to date (n = 60), we demonstrate an 85% accuracy to detect a 2-3% blood volume loss. Preliminary results from 11 healthy human subjects undergoing lower body negative pressure (LBNP) challenge show a 95% accuracy in detecting 15-mmHg changes in pressure—an excellent surrogate for occult hemorrhage. Our system fills a critical need, including in the current pandemic, where clinicians struggle to predict which patients will deteriorate.
Magnetic Resonance Electrical Properties Tomography (MREPT) is an imaging modality that uses MR data to directly calculate the conductivity of the imaged object. This study evaluates if MREPT can be used to image differences between cancerous and benign prostate tissue. A total of 39 freshly excised prostates were imaged. MR data and four MREPT approaches were analyzed. Including a new MREPT approach that overlaps tiles (subdomains) resulting in an efficient approach that minimizes artifacts. No direct threshold value was found to differentiate the malignant from benign tissues. However, significance differences were found when comparing malignant and benign differences (differenced on a per slice basis), which reveals there are measurable differences between the two tissues. Ongoing work aims to develop a calibration technique that can exploit these differences so that malignant tissue can be robustly identified.
A non-invasive and accurate modality that can continuously monitor stroke volume (SV) for extended periods of time is desired to allow for more proactive care of an increasing population of patients living with heart failure. Electrical impedance tomography (EIT) has been proposed as a method for accurate, non-invasive, continuous, and long-term SV monitoring. While cardiac EIT has been explored, clinical translation has yet to occur and a standardized method for evaluation and comparison of cardiac EIT images is desired. This work explores an automated process for segmenting and extracting features from the images that allow for evaluation and comparison. A simulation study was conducted using the 4D XCAT model to evaluate the proposed method’s ability to automatically segment and extract features from images reconstructed at various phases of the cardiac cycle. The same procedure was then applied to EIT reconstructions on data collected from five healthy volunteers. The automated segmentation is able to accurately capture the heart region-of-interest (ROI) in various images and extract features, which allows comparison of desired signals across reconstructions. ROI mean conductivity, ROI area, sum of conductivities within the ROI, and ROI maximum conductivity were chosen as promising features from the simulation study, with R2 values of 0.61, 0.73, 0.75, and 0.66 for a single heart-cycle, and minimum SV distinguishability of 25.54, 12.16, 12.16, and 17.22 ml. In experimental data, the area feature showed the least variation across individual reconstructions while the sum feature showed the highest variation.
Telemonitoring is becoming increasingly important as the proportion of the population living with cardiovascular disease (CVD) increases. Currently used health parameters in the suite of telemonitoring tools lack the sensitivity and specificity to accurately predict heart failure events, forcing physicians to play a reactive versus proactive role in patient care. A novel cardiac output (CO) monitoring device is proposed that leverages a custom smart phone application and a wearable electrical impedance tomography (EIT) system. The purpose of this work is to explore the potential of using respiratory-gated EIT to quantify stroke volume (SV) and assess its feasibility using real data. Simulations were carried out using the 4D XCAT model to create anatomically realistic meshes and electrical conductivity profiles representing the human thorax and the intrathoracic tissue. A single 5-second period respiration cycle with chest/lung expansion was modeled with end-diastole (ED) and end-systole (ES) heart volumes to evaluate how effective EIT-based conductivity changes represent clinically significant differences in SV. After establishing a correlation between conductivity changes and SV, the applicability of the respiratory-gated EIT was refined using data from the PhysioNet database to estimate the number of useful end-diastole (ED) and end-systole (ES) heart events attained over a 3.3 minute period. The area associated with conductivity changes was found to correlate to SV with a correlation coefficient of 0.92. A window of 12.5% around peak exhalation was found to be the optimal phase of the respiratory cycle from which to record EIT data. Within this window, ~47 useable ED and ES were found with a standard deviation of 28 using 3.3 minutes of data for 20 patients.
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