11 citations · 24 across the 11 of their papers we have counts for
4 papers · 1 filter
TomoTransformer: Towards a Foundation Model for CT Reconstruction
AmirEhsan Khorashadizadeh, Benjamín Béjar
Supervised deep learning has advanced sparse-view tomographic reconstruction. However, conventional models, which typically map filtered back-projection (FBP) images or sinograms t…
LoFi: Neural Local Fields for Scalable Image Reconstruction
AmirEhsan Khorashadizadeh, Tobías I. Liaudat, Tianlin Liu +2
Neural fields or implicit neural representations (INRs) have attracted significant attention in computer vision and imaging due to their efficient coordinate-based representation o…
Glimpse: Generalized Locality for Scalable and Robust CT
AmirEhsan Khorashadizadeh, Valentin Debarnot, Tianlin Liu +1
Deep learning has become the state-of-the-art approach to medical tomographic imaging. A common approach is to feed the result of a simple inversion, for example the backprojection…
Implicit Neural Representation for Mesh-Free Inverse Obstacle Scattering
Tin Vlašić, Hieu Nguyen, AmirEhsan Khorashadizadeh +1
Implicit representation of shapes as level sets of multilayer perceptrons has recently flourished in different shape analysis, compression, and reconstruction tasks. In this paper,…