Multi-Coil MRI Reconstruction Challenge-Assessing Brain MRI Reconstruction Models and Their Generalizability to Varying Coil Configurations.

Deep-learning-based brain magnetic resonance imaging (MRI) reconstruction methods have the potential to accelerate the MRI acquisition process. Nevertheless, the scientific community lacks appropriate benchmarks to assess the MRI reconstruction quality of high-resolution brain images, and evaluate how these proposed algorithms will behave in the presence of small, but expected data distribution shifts. The multi-coil MRI (MC-MRI) reconstruction challenge provides a benchmark that aims at addressing these issues, using a large dataset of high-resolution, three-dimensional, T1-weighted MRI scans.

"Hands-On, Minds-On, and Science-Up": A Concept-Based Learning Laboratory With a Taste of Research Experience for an Undergraduate Biomedical Engineering Course.

In this paper, we bridged faculty research expertise with concept-based learning pedagogy to design and implement a unique laboratory experience for biomedical engineering undergraduate students enrolled in the biomechanics of tissues course at the University of Calgary. This laboratory aimed to increase student engagement, facilitate deeper understanding of course content, and provide an opportunity for accelerated undergraduate research through "hands-on," "minds-on," and "science-up" learning components, respectively. The laboratory exercise involves testing aortic tissues using a novel miniaturized planar biaxial machine.