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Normal and aberrant cognitive functions are the result of the dynamic interplay between large-scale neural circuits. Describing the nature of these interactions has been a challenging task yet important for neurodegenerative disease evolution. The origin of Alzheimer’s disease lies in the hippocampus and subsequently diffuses to the temporal, parietal and prefrontal cortices. Determining the sources of dementia is crucial to the prediction of the disease evolution and choice of treatment. State-of-the-art method for determining dementia progression are network diffusion models derived from the heat equation without diffusion sources. We propose a different research avenue based on epidemic modeling to localize the disease sources. These models may better characterize the empirical spread of dementia through brain regions. We explore an estimation algorithm based on a susceptible-infected (SI) epidemic algorithm and a network diffusion model for comparison purposes emulating the disease evolution from sources (susceptible) to non-recovered (atrophy, infected) areas. The goal is to identify the probable disease diffusion sources, which we accomplish via a ranking heuristic based upon steady-state convergence times. Graph centrality measures are employed to provide a baseline for further comparison. Our results applied on structural brain networks in dementia suggest that epidemic models are able to accurately describe the different node roles in controlling trajectories of brain networks comparably to the existing diffusion approach.

Purpose: 3D printed patient specific vascular models provide the ability to perform precise and repeatable benchtop experiments with simulated physiological blood flow conditions. This approach can be applied to CT-derived patient geometries to determine coronary flow related parameters such as Fractional Flow Reserve (FFR). To demonstrate the utility of this approach we compared bench-top results with non-invasive CT-derived FFR software based on a computational fluid dynamics algorithm and catheter based FFR measurements. Materials and Methods: Twelve patients for whom catheter angiography was clinically indicated signed written informed consent to CT Angiography (CTA) before their standard care that included coronary angiography (ICA) and conventional FFR (Angio-FFR). The research CTA was used first to determine CT-derived FFR (Vital Images) and second to generate patient specific 3D printed models of the aortic root and three main coronary arteries that were connected to a programmable pulsatile pump. Benchtop FFR was derived from pressures measured proximal and distal to coronary stenosis using pressure transducers. Results: All 12 patients completed the clinical study without any complication, and the three FFR techniques (Angio-FFR, CT-FFR, and Benchtop FFR) are reported for one or two main coronary arteries. The Pearson correlation among Benchtop FFR/ Angio-FFR, CT-FFR/ Benchtop FFR, and CT-FFR/ Angio-FFR are 0.871, 0.877, and 0.927 respectively. Conclusions: 3D printed patient specific cardiovascular models successfully simulated hyperemic blood flow conditions, matching invasive Angio-FFR measurements. This benchtop flow system could be used to validate CTderived FFR diagnostic software, alleviating both cost and risk during invasive procedures.

© 2017 SPIE. We consider the problem of identifying frames in a cardiac ultrasound video associated with left ventricular chamber end-systolic (ES, contraction) and end-diastolic (ED, expansion) phases of the cardiac cycle. Our procedure involves a simple application of non-negative matrix factorization (NMF) to a series of frames of a video from a single patient. Rank-2 NMF is performed to compute two end-members. The end members are shown to be close representations of the actual heart morphology at the end of each phase of the heart function. Moreover, the entire time series can be represented as a linear combination of these two end-member states thus providing a very low dimensional representation of the time dynamics of the heart. Unlike previous work, our methods do not require any electrocardiogram (ECG) information in order to select the end-diastolic frame. Results are presented for a data set of 99 patients including both healthy and diseased examples.