Enrique Campos-Náñez

Needle-Based Interventions With the Image-Guided Surgery Toolkit (IGSTK): From Phantoms to Clinical Trials

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 systems 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.

Treatment Planning and Image Guidance for Radiofrequency Ablation of Large Tumors

This article addresses the two key challenges in computer-assisted percutaneous tumor ablation: planning multiple overlapping ablations for large tumors while avoiding critical structures, and executing the prescribed plan. Toward semiautomatic treatment planning for image-guided surgical interventions, we develop a systematic approach to the needle-based ablation placement task, ranging from preoperative planning algorithms to an intraoperative execution platform. The planning system incorporates clinical constraints on ablations and trajectories using a multiple objective optimization formulation, which consists of optimal path selection and ablation coverage optimization based on integer programming. The system implementation is presented and validated in both phantom and animal studies. The presented system can potentially be further extended for other ablation techniques such as cryotherapy.

Radiofrequency ablation of lung tumors in swine assisted by a navigation device with preprocedural volumetric planning.

PURPOSE To develop an image guidance system that incorporates volumetric planning of spherical ablations and electromagnetic tracking of radiofrequency (RF) electrodes during insertion. MATERIALS AND METHODS Simulated tumors were created in three live swine by percutaneously injecting agar nodules into the lung. A treatment plan was devised for each tumor with 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 with use of the electromagnetic tracking system that guided the insertion of the probe into precomputed locations. After a 72-hour survival period, animals were killed and 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 two to four ablation spheres. The image registration error was 1.0 mm +/- 0.64. The overall error of probe insertion was 9.4 mm +/- 3.0 (N = 8). Analysis of histopathologic sections confirmed successful ablations of the tissue. CONCLUSIONS Computer-assisted RF ablation planning and electromagnetically tracked probe insertion were successful in three swine, validating the feasibility of electromagnetic tracking-assisted tumor targeting. Image misregistration caused by respiratory motion and tissue deformation contributed to the overall error of probe insertion.

Pricing of dialup services: an example of congestion-dependent pricing in the Internet

Recent research on dynamic pricing of multiclass loss networks has shown that the performance of optimal static pricing approaches that of optimal dynamic (congestion-dependent) pricing in the many small sources limit. In our own work with similar models, we have found it difficult to obtain large gains over static pricing in realistic settings, even when the many small sources assumption is violated. In this paper we give an example which is a stochastic control model for congestion-dependent pricing of Internet services. Our formulation captures the basic tradeoff in allocating bandwidth to two classes of users in maximizing average net revenue. Optimal pricing requires that the ISP anticipate and respond to changes in bandwidth consumption. Our goal is to quantify the gain that can be achieved through dynamic pricing over open loop pricing strategies which may or may not account for time-of-day effects. We frame the problem as a continuous-time Markov decision process for which we numerically compute optimal solutions.

Technology selection and capacity investment under uncertainty

We analyze the problem of technology selection and capacity investment for electricity generation in a competitive environment under uncertainty. Adopting a Nash-Cournot competition model, we consider the marginal cost as the uncertain parameter, although the results can be easily generalized to other sources of uncertainty such as a load curve. In the model, firms make three different decisions: (i) the portfolio of technologies, (ii) each technology’s capacity and (iii) the technology’s production level for every scenario. The decisions related to the portfolio and capacity are ex-ante and the production level is ex-post to the realization of uncertainty. We discuss open and closed-loop models, with the aim to understand the relationship between different technologies’ cost structures and the portfolio of generation technologies adopted by firms in equilibrium. For a competitive setting, to the best of our knowledge, this paper is the first not only to explicitly discuss the relation between costs and generation portfolio but also to allow firms to choose a portfolio of technologies. We show that portfolio diversification arises even with risk-neutral firms and technologies with different cost expectations. We also investigate conditions on the probability and cost under which different equilibria of the game arise.

On-line tuning of prices for network services

Recent investigations into the pricing of multiclass loss networks have shown that static prices are optimal in the asymptotic regime of many small sources. These results suggest that nearly optimal prices for highly aggregated systems can be computed from the solution to a limiting deterministic optimization model. When the assumption of many small sources does not hold, static prices are still preferable (for practical reasons), but we are left with the difficult issue of computing an optimal solution when the stochastic nature of the process cannot be ignored. In this paper, we develop a computational procedure for optimizing static prices that operates by adjusting prices in response to actual customer arrivals and departures and is robust to parametric uncertainty about the underlying system. We provide initial arguments for the convergence properties of our optimization algorithm, and we illustrate its application in several numerical examples.

Predicting Systems Performance through Requirements Quality Attributes Model

Abstract Poor requirements definition can adversely impact system cost and performance for government acquisition programs. This can be mitigated by ensuring requirements statements are written in a clear and unambiguous manner that reflects high linguistic quality. This paper introduces a statistical model that uses requirements quality factors to predict system operational performance. This model is created using empirical data from current major acquisition programs within the federal government. Operational Requirements Documents and Operational Test Reports are the data sources, respectively, for the system requirements statements and the accompanying operational test results used for model development. A commercial-off-the-shelf requirements quality analysis tool is used to determine the linguistic quality metrics for the requirements statements. Following model construction, cross validation of the data is employed to confirm the predictive value of the model. In all, the results establish that requirements quality is indeed a predictive factor for end system operational performance; and the resulting statistical model can inform requirements decisions based on likelihood of successful operational performance.

Effect of BGM Accuracy on the Clinical Performance of CGM: An In-Silico Study:

Background: Standard management of type 1 diabetes (T1D) relies on blood glucose monitoring based on a range of technologies from self-monitoring of blood glucose (BGM) to continuous glucose monitoring (CGM). Even as CGM technology matures, patients utilize BGM for calibration and dosing. The question of how the accuracy of both technologies interact is still not well understood. Methods: We use a recently developed data-driven simulation approach to characterize the relationship between CGM and BGM accuracy especially how BGM accuracy impacts CGM performance in four different use cases with increasing levels of reliance on twice daily calibrated CGM. Simulations are used to estimate clinical outcomes and isolate CGM and BGM accuracy characteristics that drive performance. Results: Our results indicate that meter (BGM) accuracy, and more specifically systematic positive or negative bias, has a significant effect on clinical performance (HbA1c and severe hypoglycemia events) in all use-cases generated for twice daily calibrated CGMs. Moreover, CGM sensor accuracy can amplify or mitigate, but not eliminate these effects. Conclusion: As a system, BGM and CGM and their mode of use (use-case) interact to determine clinical outcomes. Clinical outcomes (eg, HbA1c, severe hypoglycemia, time in range) can be closely approximated by linear relationships with two BGM accuracy characteristics, namely error and bias. In turn, the coefficients of this linear relationship are determined by the use-case and by CGM accuracy (MARD).

Clinical Impact of Blood Glucose Monitoring Accuracy: An In-Silico Study

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.

Treatment planning and image guidance for radiofrequency ablation of liver tumors

Radiofrequency ablation is becoming an increasingly attractive option for minimally invasive treatment of liver tumors. In this procedure, the tumor and its margin are ablated using radiofrequency ablation probes that cover a region from 2cm to 7cm in diameter. For a large or irregularly shaped tumor, multiple ablations with overlapping probe placements are required. In this paper, we propose a treatment planning system to optimize these placements. A general optimization framework based on inverse planning methods is designed to generate the treatment plan. An objective function is defined to describe the coverage of the ablation volumes. Powells method and simulated annealing algorithms are used to find the solution. Pre-computed mask volumes and an initial placement based on a Euclidean Distance Transform are used to speed up the computation, which can generally take a few seconds to several minutes. To ensure accurate placement of the ablation probe, we also propose a system architecture for integrating the treatment planning system with our previously developed image-guided surgery system, which uses an electromagnetic tracking device. We present some preliminary results from synthetic data to validate our treatment planning algorithm and system concept.