Geometry Calibration & Motion Compensation
Geometry calibration and motion compensation correct a reconstruction for errors in the assumed acquisition geometry, that is, where the source, detector and object actually were for each projection. Small misalignments, such as a tilted detector, an offset rotation axis, a flexing gantry or a patient who moves, produce blur, double edges and streaks that no regulariser can remove.
Calibration can use phantoms with markers, or be marker-free self-calibration that tunes the geometry parameters for data consistency or image sharpness. Motion compensation estimates rigid or non-rigid motion of the object during the scan, which is common in head and extremity CBCT, dental imaging and long industrial scans.
Recent work makes the reconstruction operator differentiable with respect to the geometry, so pose and calibration parameters can be fitted by gradient descent; closely related are acquisition-trajectory design and view selection. Accurately documented geometry is also what makes measured datasets such as the FIPS and walnut collections valuable.
Browse all 28 Geometry Calibration & Motion Compensation papers in the searchable directory
Start here
If you are new to this topic, these are good first reads, chosen by the maintainers.
- A Gradient-Based Approach to Fast and Accurate Head Motion Compensation in Cone-Beam CT (TMI, 2025 · code)
Gradient-based head-motion compensation for CBCT using derivatives of the back-projection operator, with code. - Gradient-based geometry learning for fan-beam CT reconstruction (PMB, 2023)
Learns the fan-beam acquisition geometry itself by gradient descent through a differentiable reconstruction. - CT Reconstruction With PDF: Parameter-Dependent Framework for Data From Multiple Geometries and Dose Levels (TMI, 2021)
One reconstruction network that takes geometry and dose as inputs, so it works across several acquisition setups. - Artifact-resistant motion estimation with a patient-specific artifact model for motion-compensated cone-beam CT (Med Phys, 2013)
Motion estimation for motion-compensated CBCT that stays robust to sparse-view artifacts.
Papers
All 28 papers, newest first.
Browse all 28 Geometry Calibration & Motion Compensation papers in the searchable directory
Datasets, benchmarks and toolkits
Entries from the directory whose tasks or tags match this topic.
- Walnut-CBCT (official page) · Dataset — Projection only. Finnish Inverse Problems Society. Real cone-beam projections of a walnut at several angular samplings — a standard test bed for sparse-view and limited-angle CBCT.
- Finnish Inverse Problems Society (FIPS) datasets (official page) · Dataset — Projection only. A family of real measured sinograms (walnut, lotus root, apple, carrot, phantoms) with carefully documented acquisition geometry — the cleanest public source for testing reconstruction operators.
- tomosipo · Toolkit — Painless specification of 2D/3D projection geometries; wraps ASTRA and integrates with PyTorch.
- DBT-Reconstruction · Toolkit — Open-source reconstruction toolbox for digital breast tomosynthesis, covering DBT-specific geometry alongside FBP and iterative solvers.
- Discorpy · Toolkit — Sub-pixel camera and lens distortion calibration; used to correct detector/geometry distortions before reconstruction.
- DIRECTT Reconstruction GUI · Toolkit — A Python GUI built on the ASTRA toolbox for reconstructing X-ray CT data, with the DIRECTT algorithm included.
- CTrex · Toolkit — A research-oriented GPU iterative reconstruction framework that exposes the projection–correction–backprojection pipeline all the way down to the individual CUDA kernels, so non-ideal acquisition effects can be folded into the operators…
Guides
Guides that cover this topic.
Other topics
- Surveys & Reviews (24)
- Classical & Iterative Reconstruction (71)
- Sparse-View CT (157)
- Limited-Angle Tomography (46)
- Low-Dose CT Denoising (69)
- Metal Artifact Reduction (23)
- Spectral & Dual-Energy CT (19)
- Cone-Beam CT (77)
- Dynamic & 4D CT (24)
- Scatter Correction (11)
- Deep Unrolling & Model-Based Learning (68)
- Self-Supervised & Untrained Methods (35)
- Diffusion & Generative Priors (68)
- Implicit Neural Representations (25)
- Gaussian Splatting (26)