Abstract
With recent computed tomography (CT) efforts using Artificial Intelligence (AI) and Deep Learning (DL)techniques, there is a strong need for differentiable forward projection models that can be integrated into existing DL frameworks. We developed a pytorch-based package library providing differentiable forward and back projection functions and classes to facilitate forward and back propagation of CT operations in
the training procedure. This forward projectors support three CT projection geometries: cone, parallel and modular beams. This package can be used with both CPU and GPU with CUDA.
- Developers:
-
Kim, Hyojin [1] ; Champley, Kyle [1]
- Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
- Release Date:
- 2022-10-07
- Project Type:
- Open Source, Publicly Available Repository
- Software Type:
- Scientific
- Version:
- 1.0
- Licenses:
-
MIT License
- Sponsoring Org.:
-
USDOE National Nuclear Security Administration (NNSA)Primary Award/Contract Number:AC52-07NA27344
- Code ID:
- 108702
- Site Accession Number:
- LLNL-CODE-848657
- Research Org.:
- Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
- Country of Origin:
- United States
Citation Formats
Kim, Hyojin, and Champley, Kyle M.
LivermorE Al Projector for Computed Tomography Tasks .
Computer Software.
https://github.com/LLNL/leap.
USDOE National Nuclear Security Administration (NNSA).
07 Oct. 2022.
Web.
doi:10.11578/dc.20230622.3.
Kim, Hyojin, & Champley, Kyle M.
(2022, October 07).
LivermorE Al Projector for Computed Tomography Tasks .
[Computer software].
https://github.com/LLNL/leap.
https://doi.org/10.11578/dc.20230622.3.
Kim, Hyojin, and Champley, Kyle M.
"LivermorE Al Projector for Computed Tomography Tasks ." Computer software.
October 07, 2022.
https://github.com/LLNL/leap.
https://doi.org/10.11578/dc.20230622.3.
@misc{
doecode_108702,
title = {LivermorE Al Projector for Computed Tomography Tasks },
author = {Kim, Hyojin and Champley, Kyle M.},
abstractNote = {With recent computed tomography (CT) efforts using Artificial Intelligence (AI) and Deep Learning (DL)techniques, there is a strong need for differentiable forward projection models that can be integrated into existing DL frameworks. We developed a pytorch-based package library providing differentiable forward and back projection functions and classes to facilitate forward and back propagation of CT operations in
the training procedure. This forward projectors support three CT projection geometries: cone, parallel and modular beams. This package can be used with both CPU and GPU with CUDA.},
doi = {10.11578/dc.20230622.3},
url = {https://doi.org/10.11578/dc.20230622.3},
howpublished = {[Computer Software] \url{https://doi.org/10.11578/dc.20230622.3}},
year = {2022},
month = {oct}
}