Scatter Correction
Scatter correction estimates and removes the X-ray photons that are deflected inside the object and reach the detector off their straight path. Scatter adds a smooth background to the projections, which causes cupping, streaks, lower contrast and inaccurate CT numbers. Its share of the signal grows with cone angle and object size, so it is a major problem in flat-panel cone-beam CT.
Hardware methods reduce scatter before it is measured (anti-scatter grids, air gaps, collimation) or modulate the primary beam so that the two can be separated (primary modulation, beam-stop arrays). Software methods estimate scatter and subtract it: analytical kernel models, Monte Carlo simulation of photon transport and, since about 2018, deep networks trained to reproduce Monte Carlo estimates in real time.
It matters wherever quantitative accuracy matters: adaptive radiotherapy on CBCT, dose calculation, dual-energy imaging and industrial metrology.
Browse all 11 Scatter Correction papers in the searchable directory
Start here
If you are new to this topic, these are good first reads, chosen by the maintainers.
- A General Framework and Review of Scatter Correction Methods in X-Ray Cone-Beam CT: Part 1 — Scatter Compensation Approaches (Med Phys, 2011)
Part 1 of the standard review of CBCT scatter correction, covering how an estimate is compensated. - A General Framework and Review of Scatter Correction Methods in Cone-Beam CT: Part 2 — Scatter Estimation Approaches (Med Phys, 2011)
Part 2 of the same review, covering how scatter is estimated in the first place. - Cone-Beam Computed Tomography with a Flat-Panel Imager: Magnitude and Effects of X-Ray Scatter (Med Phys, 2001)
Measures how much scatter flat-panel CBCT actually records and what it does to contrast. - Scatter Correction Method for X-Ray CT Using Primary Modulation: Theory and Preliminary Results (TMI, 2006)
Primary modulation, a hardware-software method that separates scatter from the primary signal in frequency. - Deep Scatter Estimation (DSE): Feasibility of Using a Deep Convolutional Neural Network for Scatter Estimation in X-Ray Computed Tomography (SPIE MI, 2018)
Deep scatter estimation, the network trained to reproduce Monte Carlo scatter estimates quickly.
Papers
All 11 papers, newest first.
Browse all 11 Scatter Correction papers in the searchable directory
Datasets, benchmarks and toolkits
Entries from the directory whose tasks or tags match this topic.
- COBRA2026: a large-scale multicenter pelvic cone-beam computed tomography projection dataset (official page) · Dataset — Projection + image. 867 pelvic radiotherapy CBCT exams from six European centres (Elekta and Varian systems), with raw projections, acquisition geometry, clinical CBCT and planning CT.
- CBCT Scatter Correction Tool · Toolkit — One of the very few open tools for cone-beam CT scatter estimation and correction; distributed as a prebuilt Windows executable, with no source in the repository.
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)
- 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)