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Cardiac Cine Magnetic Resonance (CMR) Imaging has made a significant paradigm shift in medical imaging technology, thanks to its capability of acquiring high spatial and temporal resolution images of different structures within the heart that can be used for reconstructing patient-specific ventricular computational models. In this work, we describe the development of dynamic patient-specific right ventricle (RV) models associated with normal subjects and abnormal RV patients to be subsequently used to assess RV function based on motion and kinematic analysis. We first constructed static RV models using segmentation masks of cardiac chambers generated from our accurate, memory-efficient deep neural architecture - CondenseUNet - featuring both a learned group structure and a regularized weight-pruner to estimate the motion of the right ventricle. In our study, we use a deep learning-based deformable network that takes 3D input volumes and outputs a motion field which is then used to generate isosurface meshes of the cardiac geometry at all cardiac frames by propagating the end-diastole (ED) isosurface mesh using the reconstructed motion field. The proposed model was trained and tested on the Automated Cardiac Diagnosis Challenge (ACDC) dataset featuring 150 cine cardiac MRI patient datasets. The isosurface meshes generated using the proposed pipeline were compared to those obtained using motion propagation via traditional non-rigid registration based on several performance metrics, including Dice score and mean absolute distance (MAD). 

Pursuing productivity, students often adopt a divide-and-conquer strategy that undercuts collaborative learning opportunities. In this study, we introduce a task structuring and role scaffolding paradigm to create opportunities for transactive exchange in such performance-oriented tasks and experimentally compare two prompting strategies -- one designed to create a focused discussion and another to intensify transactivity -- while controlling for time on task. We find significant learning gains of each strategy when used separately, but not in tandem. 

The UCNA experiment was designed to measure the neutron β-asymmetry parameter A 0 using polarized ultracold neutrons (UCN). UCN produced via downscattering in solid deuterium were polarized via transport through a 7 T magnetic field, and then directed to a 1 T solenoidal electron spectrometer, where the decay electrons were detected in electron detector packages located on the two ends of the spectrometer. A value for A 0 was then extracted from the asymmetry in the numbers of counts in the two detector packages. We summarize all of the results from the UCNA experiment, obtained during run periods in 2007, 2008–2009, 2010, and 2011–2013, which ultimately culminated in a 0.67% precision result for A 0 .