Cone-Beam Computed Tomography
Cone-beam CT (CBCT) uses a divergent cone-shaped X-ray beam and a flat-panel 2D detector to acquire volumetric data in a single rotation, unlike fan-beam CT which acquires slice-by-slice. The 3D Feldkamp-Davis-Kress (FDK) algorithm performs approximate filtered back-projection for cone geometry. CBCT is widely used in dental, ENT, and image-guided radiation therapy. Primary artifacts include cone-beam artifacts at large cone angles, scatter, and truncation. Sparse-view CBCT reduces scan time and dose but introduces streak artifacts.
Cone Beam Projection
Poisson
fdk
FLAT_PANEL_DETECTOR
Forward-Model Signal Chain
Each primitive represents a physical operation in the measurement process. Arrows show signal flow left to right.
R(θ) → Π(cone) → D(g, η₁)
Benchmark Variants & Leaderboards
CBCT
Cone-Beam Computed Tomography
R(θ) → Π(cone) → D(g, η₁)
Standard Leaderboard (Top 10)
| # | Method | Score | PSNR (dB) | SSIM | Trust | Source |
|---|---|---|---|---|---|---|
| 🥇 | DiffusionCBCT | 0.900 | 40.1 | 0.964 | ✓ Certified | Gao 2024 |
| 🥈 | CTFormer | 0.877 | 39.0 | 0.953 | ✓ Certified | Wang 2023 |
| 🥉 | DuDoTrans | 0.859 | 38.2 | 0.944 | ✓ Certified | Wang 2022 |
| 4 | DuDoNet | 0.834 | 37.1 | 0.932 | ✓ Certified | Lin 2019 |
| 5 | Learned Primal-Dual | 0.817 | 36.4 | 0.921 | ✓ Certified | Adler 2018 |
| 6 | Metal-AR-Net | 0.803 | 35.8 | 0.912 | ✓ Certified | Zhang 2018 |
| 7 | FBPConvNet | 0.770 | 34.5 | 0.891 | ✓ Certified | Jin 2017 |
| 8 | TV-ADMM | 0.696 | 31.2 | 0.851 | ✓ Certified | Boyd 2011 |
| 9 | FDK | 0.614 | 27.8 | 0.801 | ✓ Certified | Feldkamp 1984 |
Mismatch Parameters (4) click to expand
| Name | Symbol | Description | Nominal | Perturbed |
|---|---|---|---|---|
| center_offset | Δc | Center-of-rotation offset (pixels) | 0 | 2.0 |
| source_dist | Δd_s | Source-to-isocenter distance error (mm) | 0 | 1.0 |
| cone_angle | Δα | Cone half-angle error (deg) | 0 | 0.3 |
| detector_tilt | φ | Detector tilt (deg) | 0 | 0.5 |
Reconstruction Triad Diagnostics
The three diagnostic gates (G1, G2, G3) characterize how reconstruction quality degrades under different error sources. Each bar shows the relative attribution.
Model: cone beam projection — Mismatch modes: cone beam artifact, scatter, truncation, patient motion, ring artifact
Noise: poisson — Typical SNR: 20.0–40.0 dB
Requires: geometric calibration, flat field, scatter correction, center of rotation, detector offset
Modality Deep Dive
Principle
Cone-Beam CT uses a divergent cone-shaped X-ray beam and a 2-D flat-panel detector to acquire a volumetric CT dataset in a single rotation. Unlike multi-slice CT with a narrow fan beam, CBCT covers the full volume simultaneously, enabling faster acquisition but with increased scatter and cone-beam artifacts compared to conventional CT.
How to Build the System
Mount a flat-panel detector (typically 30×40 cm, CsI scintillator) opposite an X-ray tube on a rotating gantry or C-arm. Common implementations: dental CBCT (small FOV, 90 kVp), image-guided radiation therapy CBCT (kV source on linac gantry), and C-arm CBCT (interventional). Calibrate: geometric parameters (source-detector distances, isocenter), detector offset corrections, and scatter correction LUTs.
Common Reconstruction Algorithms
- FDK (Feldkamp-Davis-Kress) cone-beam filtered back-projection
- Iterative CBCT (SART, SIRT with cone-beam projector)
- Scatter correction (measurement-based or Monte Carlo simulation)
- Motion-compensated CBCT (4D-CBCT for respiratory motion)
- Deep-learning CBCT-to-CT synthesis for radiation therapy planning
Common Mistakes
- Severe scatter artifacts (cupping, shading) in large FOV acquisitions
- Cone-beam artifacts near the edges of the FOV (Feldkamp approximation breaks down)
- Truncation artifacts when anatomy extends outside the FOV
- Motion artifacts in thorax/abdomen from respiratory and cardiac motion
- Insufficient angular sampling causing streak artifacts
How to Avoid Mistakes
- Apply scatter correction (anti-scatter grid, software correction, or beam-blocker method)
- Limit cone angle or use exact reconstruction algorithms for large cone angles
- Use extended FOV techniques (shifted detector, multiple scans) for large anatomy
- Apply 4D-CBCT or gated acquisition for moving anatomy
- Acquire sufficient projections (≥600 for a full rotation) with uniform angular spacing
Forward-Model Mismatch Cases
- The widefield fallback produces a blurred (64,64) image, but cone-beam CT acquires a sinogram of shape (n_angles, n_detector_rows * n_detector_cols) from a 2D detector rotating around the patient — the data is a set of cone-beam projections, not a blurred image
- CBCT cone-beam geometry introduces axial cone-angle artifacts (Feldkamp approximation errors) that are absent from the widefield model — any reconstruction expecting cone-beam projection data will fail with the blurred image
How to Correct the Mismatch
- Use the CBCT operator implementing cone-beam projection (Radon transform in 3D divergent geometry) for each source-detector angle, producing the correct sinogram/projection data shape
- Reconstruct using FDK (Feldkamp-Davis-Kress) algorithm or iterative cone-beam methods (SART, ADMM) with the correct cone-beam system matrix
Experimental Setup
Varian TrueBeam / Elekta XVI / iCAT dental CBCT
512x512
360
20
150x150 px
0.4
90
8
0.3
FDK / iterative
Signal Chain Diagram
Key References
- Feldkamp et al., 'Practical cone-beam algorithm', JOSA A 1, 612-619 (1984)
Canonical Datasets
- ICASSP 2024 CBCT Challenge