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Found 4 papers, 2 papers with code
Physics-Informed Learning for Time-Resolved Angiographic Contrast Agent Concentration Reconstruction
In our work, we implicitly include this information in a neural network-based model that is trained on a dataset of image-based blood flow simulations.
Transient Hemodynamics Prediction Using an Efficient Octree-Based Deep Learning Model
Nevertheless, the prediction of high-resolution transient CFD simulations for complex vascular geometries poses a challenge to conventional deep learning models.
Gradient-Based Geometry Learning for Fan-Beam CT Reconstruction
The cost function is parameterized by a trained neural network which regresses an image quality metric from the motion affected reconstruction alone.
On the Benefit of Dual-domain Denoising in a Self-supervised Low-dose CT Setting
In this work, we present an end-to-end trainable CT reconstruction pipeline that contains denoising operators in both the projection and the image domain and that are optimized simultaneously without requiring ground-truth high-dose CT data.
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