Oct 30, 2022 · This work develops and validates a self-supervised probabilistic deep learning technique, the physics-informed variational autoencoder, to solve this problem.
A dataset consisting solely of sparse projection measurements from each object is used to jointly reconstruct all objects of the set. This approach has the ...
By imaging 2-dimensional projections, a 3-dimensional object can be reconstructed through a computational algorithm. Imaging at a greater number of rotation ...
Sep 8, 2024 · This approach has the potential to allow visualization of fragile samples with x-ray computed tomography. We release our code for reproducing ...
A Self-Supervised Approach to Reconstruction in Sparse X-Ray Computed Tomography · 1 code implementation • 30 Oct 2022 • Rey Mendoza, Minh Nguyen, Judith Weng ...
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A Self-Supervised Approach to Reconstruction in Sparse X-Ray Computed Tomography · Evaluating Research Grade Bioimpedance Hardware using Textile Electrodes for ...
A Self-Supervised Approach to Reconstruction in Sparse X-Ray Computed Tomography · 1 code implementation • 30 Oct 2022 • Rey Mendoza, Minh Nguyen, Judith Weng ...
Feb 26, 2024 · In this work, we propose Sparse2Inverse, a self-supervised method for the reconstruction from sparse-view CT data. We present a strategy ...
Proj2Proj: self-supervised low-dose CT reconstruction - PMC - NCBI
www.ncbi.nlm.nih.gov › PMC10909204
Feb 29, 2024 · It was shown that our method outperformed both traditional and compressed sensing-based iterative methods, and deep learning based unsupervised ...
We propose an ultra-sparse spiral sampling strategy for multispectral PAT, which we named U3S-PAT. Our strategy employs a sparse ring-shaped transducer.