Dynamic & 4D CT
Dynamic and 4D CT reconstructs objects that change during the acquisition, producing a time series of images instead of a single static volume. The change may be periodic, such as breathing and the heartbeat in respiratory-correlated 4D-CBCT and cardiac CT, or aperiodic, as in contrast-agent perfusion and in-situ materials experiments.
The difficulty is that each time point sees only a small subset of the projections. Phase-binned 4D-CBCT, for example, reconstructs each breathing phase from roughly a tenth of the views, which gives strong streaks; ignoring the motion instead blurs the image.
Approaches include respiratory sorting with prior-image constraints (PICCS) and spatio-temporal regularisation, motion-compensated reconstruction that estimates a deformation field, and per-scan continuous-time representations such as implicit neural fields with motion warping and 4D Gaussian splatting. The SPARE challenge provides a common benchmark for sparse-view 4D-CBCT.
Browse all 24 Dynamic & 4D CT papers in the searchable directory
Start here
If you are new to this topic, these are good first reads, chosen by the maintainers.
- Respiratory Correlated Cone Beam CT (Med Phys, 2005)
Respiratory-correlated CBCT, the starting point of phase-binned 4D-CBCT. - Prior Image Constrained Compressed Sensing (PICCS): A Method to Accurately Reconstruct Dynamic CT Images from Highly Undersampled Projection Data Sets (Med Phys, 2008)
Prior image constrained compressed sensing, which reconstructs each time frame from very few views using a prior from all of them. - SPARE: Sparse-view reconstruction challenge for 4D cone-beam CT from a 1-min scan (Med Phys, 2019)
The SPARE challenge paper, which compares sparse-view 4D-CBCT methods on a common one-minute-scan benchmark. - Dynamic CT Reconstruction From Limited Views With Implicit Neural Representations (ICCV, 2021)
Implicit neural representations with a parametric motion field for limited-view 4D-CT, fitted without training data. - X2-Gaussian: 4D Radiative Gaussian Splatting for Continuous-Time Tomographic Reconstruction (ICCV, 2025 · code)
Continuous-time 4D CT with dynamic Gaussian splatting that learns the breathing motion from the projections, with code.
Papers
All 24 papers, newest first.
Browse all 24 Dynamic & 4D CT papers in the searchable directory
Datasets, benchmarks and toolkits
Entries from the directory whose tasks or tags match this topic.
- SPARE 4D-CBCT (official page) · Dataset — Projection + image. Thoracic 4D cone-beam CT for breathing-motion-resolved reconstruction; the reference benchmark for dynamic CBCT.
- SPARE Challenge (official page) · Benchmark — Thoracic 4D cone-beam CT reconstruction from a one-minute scan. The first widely adopted benchmark for breathing-resolved dynamic CBCT; run by the Image X Institute at the University of Sydney.
- TomoPhantom · Toolkit — Analytical 2D/3D/4D phantom generation plus forward projection for controlled reconstruction experiments. Also listed under Learning.
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)
- Scatter Correction (11)
- Geometry Calibration & Motion Compensation (28)
- Deep Unrolling & Model-Based Learning (68)
- Self-Supervised & Untrained Methods (35)
- Diffusion & Generative Priors (68)
- Implicit Neural Representations (25)
- Gaussian Splatting (26)