Published January 24, 2021
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LoDoPaB-CT Challenge Reconstructions compared in "Quantitative comparison of deep learning-based image reconstruction methods for low-dose and sparse-angle CT applications"
Creators
- 1. University of Bremen
- 2. Heidelberg University
- 3. University of British Columbia
- 4. University of Basel
Description
Supplementing record containing the LoDoPaB-CT challenge submissions compared in the article "Quantitative comparison of deep learning-based image reconstruction methods for low-dose and sparse-angle CT applications".
Below are references for the included methods.
cinn: A. Denker et al., 2020, Conditional Normalizing Flows for Low-Dose Computed Tomography Image Reconstructiondiptv: D. Otero Baguer et al., 2020, Computed tomography reconstruction using deep image prior and learned reconstruction methods
(DIVαℓ implementation)fbp: Filtered back-projection
(ODL implementation)fbpistaunet: T. Liu et al., 2020, Interpreting U-Nets via Task-Driven Multiscale Dictionary Learning
(Implementation by T. Liu)fbpmsdnet: D. Pelt et al., 2017, A mixed-scale dense convolutional neural network for image analysis
(Implementation based on msd_pytorch by A. Hendriksen)fbpunet: K. H. Jin et al., 2017, Deep Convolutional Neural Network for Inverse Problems in Imaging
(DIVαℓ implementation)fbpunetpp: Z. Zhou et al., 2018, UNet++: A Nested U-Net Architecture for Medical Image Segmentation
(Implementation and network weights by A. Hadjifaradji)ictnet: D. Bauer et al., 2021, iCTU-Net (submitted, based on iCT-Net)learnedpd: J. Adler et al., 2018, Learned Primal-Dual Reconstruction
(DIVαℓ implementation)tv: Total Variation Regularization
(DIVαℓ implementation)
Files
lodopab_recons_cinn.zip
Files
(16.7 GB)
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