Interior, ROI & Truncation

Truncation happens when the object is wider than the X-ray beam or the detector, so every projection misses part of it. It is deliberate in region-of-interest (ROI) and interior tomography, which irradiate only the region that matters to reduce dose, and unavoidable when a large patient or a small dental or C-arm detector leaves part of the body outside the field of view.

Filtered back-projection needs complete projections, so truncated data produce bright rims and low-frequency shading across the image. The interior problem, reconstructing a region from rays that pass through it only, has no unique solution in general, but becomes solvable with a little prior knowledge such as a known sub-region. Practical methods extrapolate the missing projection data, use differentiated backprojection, or regularise iteratively.

Learned methods now extend the field of view of truncated scans, reduce truncation artifacts in cone-beam and dental CT, and reconstruct interior regions directly, with diffusion priors and implicit neural representations among the recent tools.

Browse all 12 Interior, ROI & Truncation papers in the searchable directory

Start here

If you are new to this topic, these are good first reads, chosen by the maintainers.

• Tiny a priori knowledge solves the interior problem in computed tomography (PMB, 2008)
  Shows that a small piece of prior knowledge inside the region of interest makes the interior problem uniquely solvable.
• One Network to Solve All ROIs: Deep Learning CT for Any ROI using Differentiated Backprojection (Med Phys, 2019)
  An early network for interior CT that reconstructs any region of interest through differentiated backprojection.
• Deep-Interior: A new pathway to interior tomographic image reconstruction via a weighted backprojection and deep learning (Med Phys, 2024)
  Interior reconstruction through a weighted backprojection, with a network recovering the image of the region.
• Deep learning-based extended field of view computed tomography image reconstruction: influence of network design on image estimation outside the scan field of view (Biomed. Phys. Eng. Express, 2022)
  Extends the reconstructed field of view beyond the scanner's when the patient is wider than the beam.
• Truncation artifact reduction in cone-beam computed tomography scans using conditional generative diffusion models (BMC Med Imaging, 2026)
  A recent diffusion-based method for truncation artifacts in cone-beam CT.

Papers

All 12 papers, newest first.

Browse all 12 Interior, ROI & Truncation papers in the searchable directory

Other topics

• Surveys & Reviews (34)
• Classical & Iterative Reconstruction (89)
• Sparse-View CT (209)
• Limited-Angle Tomography (66)
• Low-Dose CT Denoising (130)
• Metal Artifact Reduction (60)
• Spectral & Dual-Energy CT (46)
• Cone-Beam CT (104)
• Helical & Multislice CT (8)
• Dynamic & 4D CT (31)
• Scatter Correction (12)
• Geometry Calibration & Motion Compensation (29)
• Deep Unrolling & Model-Based Learning (80)
• Self-Supervised & Untrained Methods (51)
• Diffusion & Generative Priors (78)
• Implicit Neural Representations (31)
• Gaussian Splatting (29)