3 resultados para multidisciplinary teams
em Duke University
Resumo:
CONCLUSION Radiation dose reduction, while saving image quality could be easily implemented with this approach. Furthermore, the availability of a dosimetric data archive provides immediate feedbacks, related to the implemented optimization strategies. Background JCI Standards and European Legislation (EURATOM 59/2013) require the implementation of patient radiation protection programs in diagnostic radiology. Aim of this study is to demonstrate the possibility to reduce patients radiation exposure without decreasing image quality, through a multidisciplinary team (MT), which analyzes dosimetric data of diagnostic examinations. Evaluation Data from CT examinations performed with two different scanners (Siemens DefinitionTM and GE LightSpeed UltraTM) between November and December 2013 are considered. CT scanners are configured to automatically send images to DoseWatch© software, which is able to store output parameters (e.g. kVp, mAs, pitch ) and exposure data (e.g. CTDIvol, DLP, SSDE). Data are analyzed and discussed by a MT composed by Medical Physicists and Radiologists, to identify protocols which show critical dosimetric values, then suggest possible improvement actions to be implemented. Furthermore, the large amount of data available allows to monitor diagnostic protocols currently in use and to identify different statistic populations for each of them. Discussion We identified critical values of average CTDIvol for head and facial bones examinations (respectively 61.8 mGy, 151 scans; 61.6 mGy, 72 scans), performed with the GE LightSpeed CTTM. Statistic analysis allowed us to identify the presence of two different populations for head scan, one of which was only 10% of the total number of scans and corresponded to lower exposure values. The MT adopted this protocol as standard. Moreover, the constant output parameters monitoring allowed us to identify unusual values in facial bones exams, due to changes during maintenance service, which the team promptly suggested to correct. This resulted in a substantial dose saving in CTDIvol average values of approximately 15% and 50% for head and facial bones exams, respectively. Diagnostic image quality was deemed suitable for clinical use by radiologists.
Resumo:
Allocating resources optimally is a nontrivial task, especially when multiple
self-interested agents with conflicting goals are involved. This dissertation
uses techniques from game theory to study two classes of such problems:
allocating resources to catch agents that attempt to evade them, and allocating
payments to agents in a team in order to stabilize it. Besides discussing what
allocations are optimal from various game-theoretic perspectives, we also study
how to efficiently compute them, and if no such algorithms are found, what
computational hardness results can be proved.
The first class of problems is inspired by real-world applications such as the
TOEFL iBT test, course final exams, driver's license tests, and airport security
patrols. We call them test games and security games. This dissertation first
studies test games separately, and then proposes a framework of Catcher-Evader
games (CE games) that generalizes both test games and security games. We show
that the optimal test strategy can be efficiently computed for scored test
games, but it is hard to compute for many binary test games. Optimal Stackelberg
strategies are hard to compute for CE games, but we give an empirically
efficient algorithm for computing their Nash equilibria. We also prove that the
Nash equilibria of a CE game are interchangeable.
The second class of problems involves how to split a reward that is collectively
obtained by a team. For example, how should a startup distribute its shares, and
what salary should an enterprise pay to its employees. Several stability-based
solution concepts in cooperative game theory, such as the core, the least core,
and the nucleolus, are well suited to this purpose when the goal is to avoid
coalitions of agents breaking off. We show that some of these solution concepts
can be justified as the most stable payments under noise. Moreover, by adjusting
the noise models (to be arguably more realistic), we obtain new solution
concepts including the partial nucleolus, the multiplicative least core, and the
multiplicative nucleolus. We then study the computational complexity of those
solution concepts under the constraint of superadditivity. Our result is based
on what we call Small-Issues-Large-Team games and it applies to popular
representation schemes such as MC-nets.