Non-invasive detection and assessment of coronary stenosis from blood mean residence times.

Coronary artery stenosis is an abnormal narrowing of a coronary artery caused by an atherosclerotic lesion that reduces lumen space. Fractional flow reserve (FFR) is the gold standard method to determine the severity of coronary stenosis based on the determination of rest and hyperemic pressure fields, but requires an invasive medical procedure. Normal FFR is 1.0 and FFR RT, to account for varying volume and flow rate of individual segments. BloodRT was computed in 100 patients who had undergone the pressure-wire FFR procedure, and a threshold for BloodRT was determined to assess the physiological significance of a stenosis, analogous to the diagnostic threshold for FFR. The threshold exhibited excellent discrimination in detecting significant from non-significant stenosis compared to the gold standard pressure-wire FFR, with sensitivity of 98% and specificity of 96%. When applied to clinical practice, this could potentially allow practicing cardiologists to accurately assess and quantify the severity of coronary stenosis without resorting to invasive catheter-based techniques. The first 100 patient study required a clinically determined blood flow rate as a key model input. To create a more non-invasive process, a multiple linear regression approach was employed to determine blood flow rate entering a given artery segment. To validate this method, BloodRT was computed for a new set of 100 patients using the regression derived blood flow rate. The sensitivity and specificity were 95% and 97%, respectively, indicating similar discrimination compared to the clinically derived flow rate. The method was also applied to a succession of stenosis in series. When BloodRT of each individual stenosis was well above the threshold for significance, the cumulative effect of all stenoses led to an overall BloodRT below the threshold of hemodynamic significance

Context dependent spectral unmixing.

A hyperspectral unmixing algorithm that finds multiple sets of endmembers is proposed. The algorithm, called Context Dependent Spectral Unmixing (CDSU), is a local approach that adapts the unmixing to different regions of the spectral space. It is based on a novel function that combines context identification and unmixing. This joint objective function models contexts as compact clusters and uses the linear mixing model as the basis for unmixing. Several variations of the CDSU, that provide additional desirable features, are also proposed. First, the Context Dependent Spectral unmixing using the Mahalanobis Distance (CDSUM) offers the advantage of identifying non-spherical clusters in the high dimensional spectral space. Second, the Cluster and Proportion Constrained Multi-Model Unmixing (CC-MMU and PC-MMU) algorithms use partial supervision information, in the form of cluster or proportion constraints, to guide the search process and narrow the space of possible solutions. The supervision information could be provided by an expert, generated by analyzing the consensus of multiple unmixing algorithms, or extracted from co-located data from a different sensor. Third, the Robust Context Dependent Spectral Unmixing (RCDSU) introduces possibilistic memberships into the objective function to reduce the effect of noise and outliers in the data. Finally, the Unsupervised Robust Context Dependent Spectral Unmixing (U-RCDSU) algorithm learns the optimal number of contexts in an unsupervised way. The performance of each algorithm is evaluated using synthetic and real data. We show that the proposed methods can identify meaningful and coherent contexts, and appropriate endmembers within each context. The second main contribution of this thesis is consensus unmixing. This approach exploits the diversity and similarity of the large number of existing unmixing algorithms to identify an accurate and consistent set of endmembers in the data. We run multiple unmixing algorithms using different parameters, and combine the resulting unmixing ensemble using consensus analysis. The extracted endmembers will be the ones that have a consensus among the multiple runs. The third main contribution consists of developing subpixel target detectors that rely on the proposed CDSU algorithms to adapt target detection algorithms to different contexts. A local detection statistic is computed for each context and then all scores are combined to yield a final detection score. The context dependent unmixing provides a better background description and limits target leakage, which are two essential properties for target detection algorithms

The Rapid Acquisition and Application of Geophysical Data to the Sustainable and Proficient Management of Shallow Aquifers and Cemeteries

Rapidly acquired non-invasive geophysical data is key to reducing the risk inherent in subsurface investigations. It achieves this risk reduction by provision of spatiotemporally dense datasets and new methods to measure the efficacy of acquisition, analysis, and modeling. In a first example, I use two geophysical methods—electrical resistivity tomography and time-domain electromagnetics—to investigate the subsurface in a rapidly urbanizing alluvial floodplain setting. Specifically I focus on the geologic structure of a shallow alluvial aquifer in the Brazos River floodplain of Texas, characterizing dynamic hydrological interactions between the aquifer and the adjacent river. Based on new geophysical insights, I determine how the sedimentary architecture of the shallow alluvial aquifer acts as a control on its recharge and discharge and how bidirectional preferential flow pathways establish hydrologic communication between the aquifer and the river at human and geologic time scales. In a second example, I develop a protocol to improve identification of unmarked graves in a historic African-American cemetery. I show that a geophysicist’s detection proficiency, expressed in terms of true-positive, true-negative, false-positive, and false-negative percentages, can be improved using radar signatures of nearby known targets as a proxy for ground-truth

DETERMINE: Novel Radar Techniques for Humanitarian Demining

Today the plague of landmines represent one of the greatest curses of modern time, killing and maiming innocent people every day. It is not easy to provide a global estimate of the problem dimension, however, reported casualties describe that the majority of the victims are civilians, with almost a half represented by children. Among all the technologies that are currently employed for landmine clearance, Ground Penetrating Radar (GPR) is one of those expected to increase the efficiency of operation, even if its high-resolution imaging capability and the possibility of detecting also non-metallic landmines are unfortunately balanced by the high sensor false alarm rate. Most landmines may be considered as multiple layered dielectric cylinders that interact with each other to produce multiple reflections, which will be not the case for other common clutter objects. Considering that each scattering component has its own angular radiation pattern, the research has evaluated the improvements that multistatic configurations could bring to the collected information content. Employing representative landmine models, a number of experimental campaigns have confirmed that GPR is capable of detecting the internal reflections and that the presence of such scattering components could be highlighted changing the antennas offset. In particular, results show that the information that can be extracted relevantly changes with the antenna separation, demonstrating that this approach can provide better confidence in the discrimination and recognition process. The proposed bistatic approach aims at exploiting possible presence of internal structure beneath the target, which for landmines means the activation or detonation assemblies and possible internal material diversity, maintaining a limited acquisition effort. Such bistatic configurations are then included in a conceptual design of a highly flexible GPR system capable of searching for landmines across a large variety of terrains, at reasonably low cost and targeting operators safety