# A gradient-based approach to fast and accurate head motion compensation in cone-beam CT

arXiv: https://arxiv.org/abs/2401.09283 · HTML: https://mlsanity.com/paper/2401.09283/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 47 references that are in the map.
- First posted on arXiv 17 Jan 2024.

## Builds on (most cited first, 6 of 6)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [The Importance of Skip Connections in Biomedical Image Segmentation](https://mlsanity.com/paper/1608.04117/) (2016)
- [PYRO-NN: Python Reconstruction Operators in Neural Networks](https://mlsanity.com/paper/1904.13342/) (2019)
- [Gradient-Based Geometry Learning for Fan-Beam CT Reconstruction](https://mlsanity.com/paper/2212.02177/) (2022)
- [Appearance Learning for Image-based Motion Estimation in Tomography](https://mlsanity.com/paper/2006.10390/) (2020)
- [Optimizing CT Scan Geometries With and Without Gradients](https://mlsanity.com/paper/2302.06251/) (2023)

## Built on by (most cited first, 5 of 5)

- [Differentiable Score-Based Likelihoods: Learning CT Motion Compensation From Clean Images](https://mlsanity.com/paper/2404.14747/) (2024)
- [On the Influence of Smoothness Constraints in Computed Tomography Motion Compensation](https://mlsanity.com/paper/2405.19079/) (2024)
- [Continuity-driven Synergistic Diffusion with Neural Priors for Ultra-Sparse-View CBCT Reconstruction](https://mlsanity.com/paper/2602.07980/) (2026)
- [Revisiting Pose Sensitivity in Splat-based Computed Tomography under Sparse-view Reconstruction](https://mlsanity.com/paper/2608.04752/) (2026)
- [Differentiable Jitter Correction using Deep Learning-based Image Quality Metric for Phase-Contrast Micro-CT](https://mlsanity.com/paper/2608.27034/) (2026)

## Abstract (arXiv)

Cone-beam computed tomography (CBCT) systems, with their flexibility, present a promising avenue for direct point-of-care medical imaging, particularly in critical scenarios such as acute stroke assessment. However, the integration of CBCT into clinical workflows faces challenges, primarily linked to long scan duration resulting in patient motion during scanning and leading to image quality degradation in the reconstructed volumes. This paper introduces a novel approach to CBCT motion estimation using a gradient-based optimization algorithm, which leverages generalized derivatives of the backprojection operator for cone-beam CT geometries. Building on that, a fully differentiable target function is formulated which grades the quality of the current motion estimate in reconstruction space. We drastically accelerate motion estimation yielding a 19-fold speed-up compared to existing methods. Additionally, we investigate the architecture of networks used for quality metric regression and propose predicting voxel-wise quality maps, favoring autoencoder-like architectures over contracting ones. This modification improves gradient flow, leading to more accurate motion estimation. The presented method is evaluated through realistic experiments on head anatomy. It achieves a reduction in reprojection error from an initial average of 3mm to 0.61mm after motion compensation and consistently demonstrates superior performance compared to existing approaches. The analytic Jacobian for the backprojection operation, which is at the core of the proposed method, is made publicly available. In summary, this paper contributes to the advancement of CBCT integration into clinical workflows by proposing a robust motion estimation approach that enhances efficiency and accuracy, addressing critical challenges in time-sensitive scenarios.