Reconstruction of patient-specific 3D bone surface from 2D calibrated fluoroscopic images and point distribution model

Reconstruction of patient-specific 3D bone surface from 2D calibrated fluoroscopic images and a point distribution model is discussed. We present a 2D/3D reconstruction scheme combining statistical extrapolation and regularized shape deformation with an iterative image-to-model correspondence establishing algorithm, and show its application to reconstruct the surface of proximal femur. The image-to-model correspondence is established using a non-rigid 2D point matching process, which iteratively uses a symmetric injective nearest-neighbor mapping operator and 2D thin-plate splines based deformation to find a fraction of best matched 2D point pairs between features detected from the fluoroscopic images and those extracted from the 3D model. The obtained 2D point pairs are then used to set up a set of 3D point pairs such that we turn a 2D/3D reconstruction problem to a 3D/3D one. We designed and conducted experiments on 11 cadaveric femurs to validate the present reconstruction scheme. An average mean reconstruction error of 1.2 mm was found when two fluoroscopic images were used for each bone. It decreased to 1.0 mm when three fluoroscopic images were used.

New concept for CTO recanalization using controlled antegrade and retrograde subintimal tracking: the CART technique

OBJECTIVES: To demonstrate the safety and feasibility of a new concept for CTO recanalization using a controlled antegrade and retrograde subintimal tracking technique (CART technique). BACKGROUND: A successful percutaneous recanalization of chronic coronary occlusions results in improved survival, as well as enhanced left ventricular function, reduction in angina, and improved exercise tolerance. However, successful recanalization of CTOs is still not optimal, and needs further improvements. METHODS: Ten patients with a CTO underwent the CART procedure. This technique combines the simultaneous use of the antegrade and retrograde approaches. A subintimal dissection is created antegradely and retrogradely, which allows the operator to limit the extension of the subintimal dissection in the CTO portion. A retrograde approach means that the occlusion site is approached in a retrograde fashion through the best collateral channel from any other patent coronary artery. RESULTS: The occlusion site was located in the RCA in 9 patients, and in the LAD in 1 patient. CTO duration varied from 7 to 84 months. Vessel recanalization was achieved in all patients. In all cases, the subintimal dissection was limited to the CTO region. No complications occurred in the collateral channel used for the retrograde approach. There were no in-hospital major adverse cardiac events. CONCLUSIONS: The CART technique is feasible, safe, and has a high success rate.

Single center experience with provisional abciximab therapy in complex lower limb interventions

BACKGROUND: Abciximab, a glycoprotein IIb/IIIa antagonist has been shown to improve patency and clinical outcome in patients undergoing endovascular recanalization of femoro-popliteal occlusions. However, data on abciximab therapy in complex peripheral catheter interventions of lower limbs are quite limited. The objective of this retrospective study was to evaluate the clinical and hemodynamic outcomes of patients treated with provisional abciximab during complex peripheral catheter interventions. PATIENTS AND METHODS: Analysis of a consecutive series of 44 patients with provisional abciximab therapy in complex peripheral catheter interventions with imminent risk of early rethrombosis defined as revascularization of arterial occlusions associated with one or more of the following additional circumstances named as time-consuming intervention > 3 hours, compromised contrast flow not solved by stenting, distal embolization not solved by mechanical thromboembolectomy, and peri-interventional notice of thrombus evolution despite adequate heparin adjustment of lower limbs. Adjunctive abciximab therapy was started in accordance to percutaneous coronary bailout situations. The decision to add abciximab was based on the decision of the operator and went along with the judgement that there is a rising risk of reocclusion due to the progressive complexity of an individual intervention. A bolus of 0.25 mg per kilogram of body weight, followed by a maintenance infusion of 0.125 microg/kg/min (up to a maximum dosage of 10 microg/min) for 12 hours was administered. Clinical and hemodynamic outcome was prospectively assessed at discharge, three and six months after the index procedure. RESULTS: The occluded artery of 44 limbs was in the iliac (2%), in the femoro-popliteal (73%) or below the knee segment (25%). Overall, occlusion length was 11.5 +/- 6.5 cm. Technical success rate was 95%. Mean ABI increased from 0.5 +/- 0.16 to 0.88 +/- 0.19 (p < 0.001) with immediate hemodynamic improvement of 91%. Overall, sustained clinical improvement was 84% and 66% at three and six months follow-up, with best results in iliac (100%), followed by below the knee (73%) and by femoro-popliteal segment (63%) at six months, respectively. Overall, secondary clinical improvement was 86% at six months. Minor and major bleeding complications were 16% and 9%, respectively. CONCLUSION: Abciximab should be noticed as medical adjunct in the interventional armamentarium to prevent imminent rethrombosis in complex peripheral catheter interventions.

MODULARITY BASED FUZZY COMMUNITY DETECTION IN SOCIAL NETWORKS

Fuzzy community detection is to identify fuzzy communities in a network, which are groups of vertices in the network such that the membership of a vertex in one community is in [0,1] and that the sum of memberships of vertices in all communities equals to 1. Fuzzy communities are pervasive in social networks, but only a few works have been done for fuzzy community detection. Recently, a one-step forward extension of Newman’s Modularity, the most popular quality function for disjoint community detection, results into the Generalized Modularity (GM) that demonstrates good performance in finding well-known fuzzy communities. Thus, GMis chosen as the quality function in our research. We first propose a generalized fuzzy t-norm modularity to investigate the effect of different fuzzy intersection operators on fuzzy community detection, since the introduction of a fuzzy intersection operation is made feasible by GM. The experimental results show that the Yager operator with a proper parameter value performs better than the product operator in revealing community structure. Then, we focus on how to find optimal fuzzy communities in a network by directly maximizing GM, which we call it Fuzzy Modularity Maximization (FMM) problem. The effort on FMM problem results into the major contribution of this thesis, an efficient and effective GM-based fuzzy community detection method that could automatically discover a fuzzy partition of a network when it is appropriate, which is much better than fuzzy partitions found by existing fuzzy community detection methods, and a crisp partition of a network when appropriate, which is competitive with partitions resulted from the best disjoint community detections up to now. We address FMM problem by iteratively solving a sub-problem called One-Step Modularity Maximization (OSMM). We present two approaches for solving this iterative procedure: a tree-based global optimizer called Find Best Leaf Node (FBLN) and a heuristic-based local optimizer. The OSMM problem is based on a simplified quadratic knapsack problem that can be solved in linear time; thus, a solution of OSMM can be found in linear time. Since the OSMM algorithm is called within FBLN recursively and the structure of the search tree is non-deterministic, we can see that the FMM/FBLN algorithm runs in a time complexity of at least O (n2). So, we also propose several highly efficient and very effective heuristic algorithms namely FMM/H algorithms. We compared our proposed FMM/H algorithms with two state-of-the-art community detection methods, modified MULTICUT Spectral Fuzzy c-Means (MSFCM) and Genetic Algorithm with a Local Search strategy (GALS), on 10 real-world data sets. The experimental results suggest that the H2 variant of FMM/H is the best performing version. The H2 algorithm is very competitive with GALS in producing maximum modularity partitions and performs much better than MSFCM. On all the 10 data sets, H2 is also 2-3 orders of magnitude faster than GALS. Furthermore, by adopting a simply modified version of the H2 algorithm as a mutation operator, we designed a genetic algorithm for fuzzy community detection, namely GAFCD, where elite selection and early termination are applied. The crossover operator is designed to make GAFCD converge fast and to enhance GAFCD’s ability of jumping out of local minimums. Experimental results on all the data sets show that GAFCD uncovers better community structure than GALS.

MITEP: REFLECTIVE JOURNEY

This research looks at the use of the Interactive Student Notebook (ISN) in the math classroom and the impact on student achievement as part of the MiTEP program. A reflective critical analysis of the MiTEP program discusses impact on teacher pedagogy, leadership, and connections to people and resources. The purpose of the study stemmed from the lack of student retention, poor organizational skills, and the students’ inability to demonstrate college readiness skills such as how to study, completing homework, and thinking independently. Motivation also stemmed from teacher frustration. The research was conducted at Linden Grove Middle School in Kalamazoo Michigan in a strategic math class. Twenty-two sixth graders, thirty-two seventh graders, and forty eighth graders were part of the study.Students were given the Strategic Math Inventory (SMI) test in week 1 of the class and again at the end of a 12 week marking period. Students participated in an attitude survey to record their feelings about the use of the ISN in the strategic math classroom. The data compared the control group (the previous year’s [2012-2013] growth data) to the experimental group, the current year’s (2013-2014) growth data. Both groups were statistically similar in that the mean average was about a 4th grade level equivalency and the groups had similar numbers of grade level students. The significant findings were in the amount of growth made using the ISN. The control group started with a mean average of 586.6 and ended with a mean average of 697.1, making about one year’s growth from a 4th to a 5th grade level equivalency. The experimental group started with a mean average of 585.2 and ended with a mean average of 744.2, making about two years growth from a 4th to a 6th grade level equivalency. This is double the growth of the control group. The Cohen’s test resulted in a score of 0.311 which describes that the teaching method, the use of the ISN in the math classroom had a medium impact on student growth.