97 citations
- University of California San DiegoUS22 papers
- San Diego State UniversityUS9 papers
- Stanford UniversityUS3 papers
- Imperial College LondonGB2 papers
- Johns Hopkins UniversityUS2 papers
- Matsys (United States)US2 papers
- University of California, IrvineUS2 papers
- University of TrentoIT2 papers
- University of WashingtonUS2 papers
- Aix-Marseille UniversitéFR1 paper
- Amen ClinicsUS1 paper
- Amsterdam NeuroscienceNL1 paper
25 papers
Computed Tomography (CT)-derived Cardiovascular Flow Estimation Using Physics-Informed Neural Networks Improves with Sinogram-based Training: A Simulation Study
Jinyuxuan Guo, Gurnoor Singh Khurana, Alejandro Gonzalo Grande +2
Background: Non-invasive imaging-based assessment of blood flow plays a critical role in evaluating heart function and structure. Computed Tomography (CT) is a widely-used imaging…
ON-OFF Neuromorphic ISING Machines using Fowler-Nordheim Annealers
Zihao Chen, Zhili Xiao, Mahmoud Akl +17
We introduce NeuroSA, a neuromorphic architecture specifically designed to ensure asymptotic convergence to the ground state of an Ising problem using a Fowler-Nordheim quantum mec…
A Categorical Framework for Quantifying Emergent Effects in Network Topology
Johnny Jingze Li, Sebastian Prado Guerra, Kalyan Basu +1
Emergent effect is crucial to understanding the properties of complex systems that do not appear in their basic units, but there has been a lack of theories to measure and understa…
The Past, Present, and Future of the Brain Imaging Data Structure (BIDS)
Russell A. Poldrack, Christopher J. Markiewicz, Stefan Appelhoff +111
The Brain Imaging Data Structure (BIDS) is a community-driven standard for the organization of data and metadata from a growing range of neuroscience modalities. This paper is mean…
Rapid and stain-free quantification of viral plaque via lens-free holography and deep learning
Tairan Liu, Yuzhu Li, Hatice Ceylan Koydemir +8
We present a rapid and stain-free quantitative viral plaque assay using lensfree holographic imaging and deep learning. This cost-effective, compact, and automated device significa…
Data-Consistent Non-Cartesian Deep Subspace Learning for Efficient Dynamic MR Image Reconstruction
Zihao Chen, Yuhua Chen, Yibin Xie +2
Non-Cartesian sampling with subspace-constrained image reconstruction is a popular approach to dynamic MRI, but slow iterative reconstruction limits its clinical application. Data-…