# Deep Learning Computed Tomography based on the Defrise and Clack Algorithm

arXiv: https://arxiv.org/abs/2403.00426 · HTML: https://mlsanity.com/paper/2403.00426/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 8 references that are in the map.
- First posted on arXiv 1 Mar 2024.

## Builds on (most cited first, 2 of 2)

- [PYRO-NN: Python Reconstruction Operators in Neural Networks](https://mlsanity.com/paper/1904.13342/) (2019)
- [A Cone-Beam X-Ray CT Data Collection designed for Machine Learning](https://mlsanity.com/paper/1905.04787/) (2019)

## Built on by (most cited first, 6 of 6)

- [DRACO: Differentiable Reconstruction for Arbitrary CBCT Orbits](https://mlsanity.com/paper/2410.14900/) (2024)
- [Compressibility Analysis for the differentiable shift-variant Filtered Backprojection Model](https://mlsanity.com/paper/2501.11586/) (2025)
- [Learning Wavelet-Sparse FDK for 3D Cone-Beam CT Reconstruction](https://mlsanity.com/paper/2505.13579/) (2025)
- [An update to PYRO-NN: A Python Library for Differentiable CT Operators](https://mlsanity.com/paper/2511.08427/) (2025)
- [Robustness and Stability Analysis of Differentiable Shift-Variant FBP for Cone-Beam CT under Challenging Acquisition Settings](https://mlsanity.com/paper/2607.09828/) (2026)
- [GB-SVFBP: Gaussian-Based Shift-Variant FBP neural network](https://mlsanity.com/paper/2607.11584/) (2026)

## Abstract (arXiv)

This study presents a novel approach for reconstructing cone beam computed tomography (CBCT) for specific orbits using known operator learning. Unlike traditional methods, this technique employs a filtered backprojection type (FBP-type) algorithm, which integrates a unique, adaptive filtering process. This process involves a series of operations, including weightings, differentiations, the 2D Radon transform, and backprojection. The filter is designed for a specific orbit geometry and is obtained using a data-driven approach based on deep learning. The approach efficiently learns and optimizes the orbit-related component of the filter. The method has demonstrated its ability through experimentation by successfully learning parameters from circular orbit projection data. Subsequently, the optimized parameters are used to reconstruct images, resulting in outcomes that closely resemble the analytical solution. This demonstrates the potential of the method to learn appropriate parameters from any specific orbit projection data and achieve reconstruction. The algorithm has demonstrated improvement, particularly in enhancing reconstruction speed and reducing memory usage for handling specific orbit reconstruction.