The imaging of endovascular devices during neurovascular procedures such as the coiling of aneurysms guided with CBCT imaging may be challenging due to the presence of highly attenuating materials such as platinum in the coil and stent marker, nickel-titanium in the stent, iodine in the contrast agent, and tantalum in the embolization agent. The use of dual-energy imaging followed by a basis material decomposition image processing-scheme may improve the feature separation and recognition. Two sets of testing were performed to validate this concept. The first trial was the acquisition of dual-energy micro-CBCT data of a 3D-printed simple aneurysm model using a 49.5 μm pixel size CMOS detector (Teledyne DALSA, Waterloo, ON.). Two sets of projection data were acquired using beam energies of 35 kVp and 70 kVp. Axial slices were reconstructed and used to carry out the material decomposition processing. The second trial was the acquisition of dual-energy CBCT images of a RS-240T angiographic head phantom (Radiology Support Devices Inc., CA.) with an iodine vascular insert using a Toshiba Infinix BiPlane C-arm system coupled to a flat panel detector. Two sets of image data were acquired using beam energies of 80 kVp and 120 kVp. Following image reconstruction, slices of the phantom were decomposed using the same processing as previously. The resulting image data over both trials indicate that the decomposition process was successful in separating the kinds of materials commonly used during a neurovascular intervention, such as platinum, cobalt-chromium, and iodine. The normalized root mean square error metric was used to quantitatively assess this. This indicates a basis for future more clinically relevant testing of our methods.

In “Estimating Large-Scale Tree Logit Models,” S. Jagabathula, P. Rusmevichientong, A. Venkataraman, and X. Zhao tackle the demand estimation problem under the tree logit model, also known as the nested logit or d-level nested logit model. The model is ideal for scenarios in which products can be grouped naturally based on their attributes into a hierarchy or taxonomy, such as flight itineraries grouped by departure time (morning or evening) and number of stops (nonstop or one stop). The current estimation methods are not practical for real-world applications that can involve hundreds or even thousands of products. The authors develop a fast, iterative method that computes a sequence of parameter estimates using simple closed-form updates by exploiting the structure of the negative log-likelihood objective. Numerical results on both synthetic and real data show that their proposed algorithm outperforms state-of-the-art optimization methods, especially for large-scale tree logit models with thousands of products.

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