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Multi-resolution extended-volume model for iterative reconstruction in cone beam CT.

Razieh Azizi¹, Ville-Veikko Wettenhovi^1,2, Kati Niinimäki²

¹Department of Technical Physics, University of Eastern Finland, Kuopio, Finland.

Physics in Medicine and Biology

|April 24, 2026

Summary

This study introduces a multi-resolution extended reconstruction volume (MR-ERV) to eliminate cone beam computed tomography (CBCT) artifacts from truncated data. The MR-ERV model enables accurate Hounsfield Unit (HU) recovery within the field of view (FOV) efficiently.

Keywords:

cone beam computed tomography image reconstruction local tomography model-based iterative reconstruction multi-resolution

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Area of Science:

Medical Imaging
Computational Imaging
Image Reconstruction

Background:

Cone beam computed tomography (CBCT) image reconstruction faces artifacts due to truncated projection data from limited detector fields of view (FOV).
Reconstructing only within the acquired FOV leads to artifacts from out-of-FOV tissue attenuation.
Extending the high-resolution reconstruction volume to fully enclose anatomy significantly increases computational complexity for iterative methods.

Purpose of the Study:

To propose a computationally efficient multi-resolution reconstruction model to eliminate out-of-FOV artifacts in CBCT.
To enable accurate recovery of Hounsfield Unit (HU) values within the FOV despite data truncation.
To reduce the computational burden associated with large reconstruction volumes.

Main Methods:

A multi-resolution extended reconstruction volume (MR-ERV) approach was developed, using coarser voxel representations for extension volumes beyond the FOV.
The model was augmented with projection extrapolation to further reduce out-of-FOV artifacts.
Model-based iterative reconstruction minimization, specifically non-negativity constrained least-squares estimation, was employed using a Primal-Dual Hybrid Gradient (PDHG) algorithm.

Main Results:

The MR-ERV model effectively removed out-of-FOV reconstruction artifacts.
Accurate HU values within the FOV were achieved when the extended volume enclosed the imaged body in the transaxial direction.
The approach demonstrated computational efficiency compared to extending high-resolution volumes.

Conclusions:

The proposed MR-ERV model offers an effective solution for mitigating CBCT reconstruction artifacts caused by truncated data.
This method enables accurate HU quantification within the FOV without prohibitive computational cost.
The MR-ERV model serves as a valuable platform for efficient and accurate model-based iterative reconstruction of CBCT data.