Medical imaging systems such as those used in positron emission tomography (PET) are capable of spatial resolutions that enable the imaging of small, functionally important brain structures. However, the quality of data from PET brain studies is often limited by subject motion during acquisition. This is particularly challenging for patients with neurological disorders or with dynamic research studies that can last 90 min or more. Restraining head movement during the scan does not eliminate motion entirely and can be unpleasant for the subject. Head motion can be detected and measured using a variety of techniques that either use the PET data itself or an external tracking system. Advances in computer vision arising from the video gaming industry could offer significant benefits when re-purposed for medical applications. A method for measuring rigid body type head motion using the Microsoft Kinect v2 is described with results presenting  ⩽0.5 mm spatial accuracy. Motion data is measured in real-time at 30 Hz using the KinectFusion algorithm. Non-rigid motion is detected using the residual alignment energy data of the KinectFusion algorithm allowing for unreliable motion to be discarded. Motion data is aligned to PET listmode data using injected pulse sequences into the PET/CT gantry allowing for correction of rigid body motion. Pilot data from a clinical dynamic PET/CT examination is shown.

Urban street networks in the United States have been primarily designed for automobile traffic with negligible considerations to non-motorized transportation users. Due to environmental issues and quality of life concerns, communities are reclaiming street spaces for active modes and slowing the speeds in their downtown. Moreover, tactical urbanism, i.e., the use of street space for innovative purposes other than moving automobile traffic, is becoming attractive due to reduced automobile travel demand and the need for outdoor activities in the age of the COVID-19 pandemic. This study provides details of the modeling of an urban downtown network (in the City of San Jose) using microscopic traffic simulation. The model is then applied to evaluate the effectiveness of street design changes at varying demand scenarios. The microsimulation approach was chosen because it allows for the detailed modeling and visualization of the transportation networks, including movements of individual vehicles, bicyclists, and pedestrians. The street design change demonstrated here involves one-way to two-way street conversion, but the framework of network-wide impact evaluation may also be used for complete street conversions. The base conditions network was also tested under different travel demand reduction scenarios (10%, 20%, and 30%) to identify the corridors in the city network in which the tactical urbanism strategies (e.g., open-air dining) may be best accommodated. The study provides framework for the use of a microscopic model as part of a decision support system to evaluate and effectively implement complete streets/tactical urbanism strategies.

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Background: Despite numerous studies demonstrating the effectiveness of Restricted Crossing U-Turn (RCUT) intersection design, its implementation remains uneven and close to zero in some large states, including California. This paper provides a comprehensive framework to estimate the operational and safety performance of future RCUT designs. The framework is demonstrated for a geometrically constrained intersection located on a high-speed rural expressway. The operational evaluation relies on microscopic simulation models of existing TWSC and alternate RCUT designs used to estimate network-wide performance measures. Methods: Two approaches are demonstrated for future safety estimation; first, an HSM-prescribed Empirical Bayes (EB) approach that uses Safety Performance Function (SPF) predictions combined with the crash history of the site. For typical applications, EB estimates may be combined with CMFs for RCUT found in the literature. This approach remains the preferred option for safety estimation. However, for geometrically constrained locations where atypical RCUT designs need to be evaluated, a surrogate measure-based approach that uses trajectory data from the simulation model is described. Results: Surrogate measure-based safety analysis revelated that the RCUT design with no-left turn from mainline would be the most appropriate design for this location. Conclusion: The framework demonstrated here may be used by agencies to estimate the future benefits of the first-time application of treatments that have been successful elsewhere.