# DRACO: Differentiable Reconstruction for Arbitrary CBCT Orbits

arXiv: https://arxiv.org/abs/2410.14900 · HTML: https://mlsanity.com/paper/2410.14900/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 26 references that are in the map.
- First posted on arXiv 18 Oct 2024.

## Builds on (most cited first, 7 of 7)

- [A disciplined approach to neural network hyper-parameters: Part 1 -- learning rate, batch size, momentum, and weight decay](https://mlsanity.com/paper/1803.09820/) (2018)
- [Implicit Neural Representation in Medical Imaging: A Comparative Survey](https://mlsanity.com/paper/2307.16142/) (2023)
- [PYRO-NN: Python Reconstruction Operators in Neural Networks](https://mlsanity.com/paper/1904.13342/) (2019)
- [Learning 3D Gaussians for Extremely Sparse-View Cone-Beam CT Reconstruction](https://mlsanity.com/paper/2407.01090/) (2024)
- [Data-Driven Filter Design in FBP: Transforming CT Reconstruction with Trainable Fourier Series](https://mlsanity.com/paper/2401.16039/) (2024)
- [Deep Learning Computed Tomography based on the Defrise and Clack Algorithm](https://mlsanity.com/paper/2403.00426/) (2024)
- [Task-based Generation of Optimized Projection Sets using Differentiable Ranking](https://mlsanity.com/paper/2303.11724/) (2023)

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

- [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)
- [Soft Tuy-Completeness for Robust Projection Selection in Cone-Beam CT](https://mlsanity.com/paper/2605.24023/) (2026)
- [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 paper introduces a novel method for reconstructing cone beam computed tomography (CBCT) images for arbitrary orbits using a differentiable shift-variant filtered backprojection (FBP) neural network. Traditional CBCT reconstruction methods for arbitrary orbits, like iterative reconstruction algorithms, are computationally expensive and memory-intensive. The proposed method addresses these challenges by employing a shift-variant FBP algorithm optimized for arbitrary trajectories through a deep learning approach that adapts to a specific orbit geometry. This approach overcomes the limitations of existing techniques by integrating known operators into the learning model, minimizing the number of parameters, and improving the interpretability of the model. The proposed method is a significant advancement in interventional medical imaging, particularly for robotic C-arm CT systems, enabling faster and more accurate CBCT reconstructions with customized orbits. Especially this method can also be used for the analytical reconstruction of non-continuous orbits like circular plus arc. The experimental results demonstrate that the proposed method significantly accelerates the reconstruction process compared to conventional iterative algorithms. It achieves comparable or superior image quality, as evidenced by metrics such as the mean squared error (MSE), the peak signal-to-noise ratio (PSNR), and the structural similarity index measure (SSIM). The validation experiments show that the method can handle data from different trajectories, demonstrating its flexibility and robustness across different scan geometries. Our method demonstrates a significant improvement, particularly for the sinusoidal trajectory, achieving a 38.6% reduction in MSE, a 7.7% increase in PSNR, and a 5.0% improvement in SSIM. Furthermore, the computation time for reconstruction was reduced by more than 97%.