Browsing by Author "Teodoro, P."
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- Deformable MRI-Ultrasound registration using correlation-based attribute matching for brain shift correction: accuracy and generality in multi-site data.Publication . Machado, I.; Toews, M.; Luo, J.; Unadkat, P.; Essayed, W.; George, E.; Teodoro, P.; Carvalho, H.; Martins, J.; Golland, P.; Pieper, S.; Frisken, S.; Golby, A.; Wells, W.; Ou, Y.Intraoperative tissue deformation, known as brain shift, decreases the benefit of using preoperative images to guide neurosurgery. Non-rigid registration of preoperative magnetic resonance (MR) to intraoperative ultrasound (iUS) has been proposed as a means to compensate for brain shift. We focus on the initial registration from MR to predurotomy iUS. We present a method that builds on previous work to address the need for accuracy and generality of MR-iUS registration algorithms in multi-site clinical data. High-dimensional texture attributes were used instead of image intensities for image registration and the standard difference-based attribute matching was replaced with correlation-based attribute matching. A strategy that deals explicitly with the large field-of-view mismatch between MR and iUS images was proposed. Key parameters were optimized across independent MR-iUS brain tumor datasets acquired at 3 institutions, with a total of 43 tumor patients and 758 reference landmarks for evaluating the accuracy of the proposed algorithm. Despite differences in imaging protocols, patient demographics and landmark distributions, the algorithm is able to reduce landmark errors prior to registration in three data sets (5.37±4.27, 4.18±1.97 and 6.18±3.38 mm, respectively) to a consistently low level (2.28±0.71, 2.08±0.37 and 2.24±0.78 mm, respectively). This algorithm was tested against 15 other algorithms and it is competitive with the state-of-the-art on multiple datasets. We show that the algorithm has one of the lowest errors in all datasets (accuracy), and this is achieved while sticking to a fixed set of parameters for multi-site data (generality). In contrast, other algorithms/tools of similar performance need per-dataset parameter tuning (high accuracy but lower generality), and those that stick to fixed parameters have larger errors or inconsistent performance (generality but not the top accuracy). Landmark errors were further characterized according to brain regions and tumor types, a topic so far missing in the literature.
- Extended- Range Marine Unmanned Surface Vehicles for Border Surveillance MissionsPublication . Fernandes, J. F. P.; Branco, P. J. Costa; Marat-Mendes, R.; Póvoa, R.; Teodoro, P.; Neves, J.; Marques, H.; Pinheiro, P.; Afonso, P.; Serrano, D.; Assunção, M.This work focuses on the development of extended-range marine unmanned surface vehicles (USV) for border surveillance missions. USVs present many advantages for marine applications due to their energy-saving capabilities and lack of in-board pilot needs. Nevertheless, due to their small scales, USVs often present reduced mission ranges. In order to overcome this handicap, this work proposes the application of innovative energy management strategies to optimize the available energy during one mission. This solution is based on path and speed optimizations, taking into account the most probable weather and sea conditions, to define a mission plan. The proposed energy management optimization is supported by the USV simulation model. This model is calibrated and verified with two experimental tests: one in an indoor swimming pool and the other in an outdoor enclosed harbour. Applying the proposed optimization strategy results in energy savings between 10 and 35.9%, subject to the maximization of mission time. This work is carried out within the international project SEMS4USV, supported by Frontex.