Computed Tomography Reconstruction Algorithm Using Markov Random Field Model
Abstract
X-ray computed tomography (CT) reveals the materials' internal structures non-destructively from a tilt series of projected images. Filtered back projection (FBP) is a widely-adopted reconstruction algorithm in CT owing to its small computational cost. Under low-dose or sparse-view conditions, however, FBP often amplifies noise, severely degrading the reconstructed images. In this study, we evaluated the performance of a Bayesian CT reconstruction algorithm based on the Markov random field model under such adverse conditions. Through simulations, we demonstrated that the proposed algorithm shows higher reconstruction performance than FBP under both low-dose and sparse-view conditions. The hyperparameters are estimated by minimizing the Bayesian free energy, enabling adaptive reconstruction that reflects the noise characteristics of the observed projection data. These results suggest that the proposed algorithm can broaden the applicability of CT to dose-sensitive applications and time-constrained measurements, where only limited observed projection data are available.
Versions
Related
- Towards 3D fully randomized frequency-domain reconstruction of the speed of sound in breast ultrasound computed tomography
- Full-field fluorescence computed tomography (F3CT) using a calibrated virtual cone-beam pinhole geometry
- Neural-network-based material identification in photon-counting computed tomography using region-of-interest spectral features
- Contrast-enhanced micro-computed tomography visualization of adhesively bonded CFRP/aluminum tubular joints
- CNN-based algorithm for improving the quality of Single-Shot Holographic Tomography
BibTeX
@misc{shimomiya2026computed,
title = {{Computed Tomography Reconstruction Algorithm Using Markov Random Field Model}},
author = {Taiga Shimomiya and Taichi Kusumi and Masayuki Uesugi and Akihisa Takeuchi and Yuki Sada and Hayaru Shouno and Masato Okada},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2605.11637},
eprint = {2605.11637},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2605.11637},
}
RIS
TY - UNPB
TI - Computed Tomography Reconstruction Algorithm Using Markov Random Field Model
AU - Taiga Shimomiya
AU - Taichi Kusumi
AU - Masayuki Uesugi
AU - Akihisa Takeuchi
AU - Yuki Sada
AU - Hayaru Shouno
AU - Masato Okada
PY - 2026
DA - 2026/09/18
PB - arXiv
DO - 10.48550/arxiv.2605.11637
UR - https://arxiv.org/abs/2605.11637
AB - X-ray computed tomography (CT) reveals the materials' internal structures non-destructively from a tilt series of projected images. Filtered back projection (FBP) is a widely-adopted reconstruction algorithm in CT owing to its small computational cost. Under low-dose or sparse-view conditions, however, FBP often amplifies noise, severely degrading the reconstructed images. In this study, we evaluated the performance of a Bayesian CT reconstruction algorithm based on the Markov random field model under such adverse conditions. Through simulations, we demonstrated that the proposed algorithm shows higher reconstruction performance than FBP under both low-dose and sparse-view conditions. The hyperparameters are estimated by minimizing the Bayesian free energy, enabling adaptive reconstruction that reflects the noise characteristics of the observed projection data. These results suggest that the proposed algorithm can broaden the applicability of CT to dose-sensitive applications and time-constrained measurements, where only limited observed projection data are available.
ER -