The new modifications introduced in the T1D simulator allow to extend its domain of validity from "single-meal" to "single-day" scenarios, thus enabling a more realistic framework for in silico testing of advanced diabetes technologies including glucose sensors, new insulin molecules and artificial pancreas.

We present three image-guided navigation systems developed for needle-based interventional radiology procedures, using the open source image-guided surgery toolkit (IGSTK). The clinical procedures we address are vertebroplasty, RF ablation of large lung tumors, and lung biopsy. In vertebroplasty, our system replaces the use of fluoroscopy, reducing radiation exposure to patient and physician. We evaluate this system using a custom phantom and compare the results obtained by a medical student, an interventional radiology fellow, and an attending physician. In RF ablation of large lung tumors, our system provides an automated interventional plan that minimizes damage to healthy tissue and avoids critical structures, in addition to accurate guidance of multiple electrode insertions. We evaluate the system's performance using an animal model. Finally, in the lung biopsy procedure, our system replaces the use of computed tomographic (CT) fluoroscopy, reducing radiation exposure to patient and physician, while at the same time enabling oblique trajectories which are considered challenging under CT fluoroscopy. This system is currently being used in an ongoing clinical trial at Georgetown University Hospital and was used in three cases.

Background:Patients with diabetes rely on blood glucose (BG) monitoring devices to manage their condition. As some self-monitoring devices are becoming more and more accurate, it becomes critical to understand the relationship between system accuracy and clinical outcomes, and the potential benefits of analytical accuracy.Methods:We conducted a 30-day in-silico study in type 1 diabetes mellitus (T1DM) patients using continuous subcutaneous insulin infusion (CSII) therapy and a variety of BG meters, using the FDA-approved University of Virginia (UVA)/Padova Type 1 Simulator. We used simulated meter models derived from the published characteristics of 43 commercial meters. By controlling random events in each parallel run, we isolated the differences in clinical performance that are directly associated with the meter characteristics.Results:A meter’s systematic bias has a significant and inverse effect on HbA1c (P < .01), while also affecting the number of severe hypoglycemia events. On the other hand, error, defined as the fraction of measurements beyond 5% of the true value, is a predictor of severe hypoglycemia events (P < .01), but in the absence of bias has a nonsignificant effect on average glycemia (HbA1c). Both bias and error have significant effects on total daily insulin (TDI) and the number of necessary glucose measurements per day (P < .01). Furthermore, these relationships can be accurately modeled using linear regression on meter bias and error.Conclusions:Two components of meter accuracy, bias and error, clearly affect clinical outcomes. While error has little effect on HbA1c, it tends to increase episodes of severe hypoglycemia. Meter bias has significant effects on all considered metrics: a positive systemic bias will reduce HbA1c, but increase the number of severe hypoglycemia attacks, TDI use, and number of fingersticks per day.

The ARG algorithm was successfully validated in a pilot clinical trial, encouraging further tests with a larger number of patients and in outpatient settings.

Purpose-The purpose of the study was to develop an image guidance system that incorporates volumetric planning of spherical ablations and electromagnetic tracking of radiofrequency electrodes during insertion.Methods-Simulated tumors were created in 3 live swine by percutaneously injecting agar nodules into the lung. A treatment plan was devised for each tumor using our optimization software to solve the planning problem. The desired output was the minimum number of overlapping ablation spheres necessary to ablate each tumor and the margin. The insertion plan was executed using the electromagnetic tracking system that guided the insertion of the probe into pre-computed locations. After a 72 hour survival, histopathologic sections of the tissue were examined for cell viability and burn pattern analysis.Results-A planning algorithm to spherically cover the tumors and the margin was computed. Electromagnetic tracking allowed successful insertion of the instrument and impedance roll-off was reached in all ablations. Depending on their size, the tumors and the tumor margins were successfully covered with 2 to 4 ablation spheres. The image registration error was 1.0 ± 0.64mm. The overall error of probe insertion was 9.4 ± 3.0mm (n=8). Histopathologic sections confirmed successful ablations of the tissue.Conclusions-Computer assisted RF ablation planning and electromagnetically tracked probe insertion were successful in 3 swine, thus validating the feasibility of electromagnetic tracking assisted tumor targeting. Image mis-registration due to respiratory motion and tissue deformation contributed to the overall error of probe insertion.

We analyze the price-formation process in an infinite-horizon oligopoly model where hydroelectric generators engage in dynamic price-based competition. The analysis focuses on the role of “indifference” prices, i.e., prices that equate the gains from releasing or storing water. Strategies where players bid their indifference prices and the marginal player undercuts the lowest-cost unsuccessful bidder constitute a Markov Perfect Equilibrium (MPE) under appropriate conditions. These conditions involve symmetric production capacity and nonfractional (i.e., “all or nothing”) output by successful bidders. Although the MPE solution represents an equilibrium consistent with dynamic strategic behavior, it requires computational sophistication by market participants. However, a basic “learning” procedure involving indifference prices converges to an MPE.

Simulation-based algorithms for maximizing the average reward of a parameterized Markov chain often rely upon the existence of a state which is recurrent for all choices of parameter values. The question of which recurrent state should serve to mark the end of a regenerative cycle is a very important practical consideration in applications. Even when all of the states of the process are recurrent, some states tend to be visited more often than others, and lengthy renewal cycles tend to result in high variance estimates of the gradient. To address this difficulty, we analyze a mechanism for adjusting this special state dynamically ( -adaptation) as applied to the "batch" simulation-based optimization algorithm of a previous paper. We show that the desirable convergence properties of the original "batch" algorithm are retained with -adaptation, namely the almost sure convergence of the parameter vector to a critical point.

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