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Computed Tomography

Computed Tomography

Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...

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Updated: Mar 6, 2026

A Postoperative Evaluation Guideline for Computer-Assisted Reconstruction of the Mandible

A Postoperative Evaluation Guideline for Computer-Assisted Reconstruction of the Mandible

Published on: January 28, 2020

Developing a metric for bone union in mandibular reconstruction using quantitative CT.

Niels Krintel Petersen¹, Timothy Manzie², Charlotte Kenny³

¹Department of Otorhinolaryngology, Head and Neck Surgery, Aarhus University Hospital, Denmark.

Journal of Stomatology, Oral and Maxillofacial Surgery

|March 4, 2026

Summary

This summary is machine-generated.

Quantitative computed tomography (CT) Hounsfield unit (HU) measurements show promise for assessing bone healing after mandibular reconstruction. Machine learning models achieved high accuracy in predicting bone union, supporting further validation.

Keywords:

Bone union assessment Fibula free flap reconstruction Hounsfield unit analysis Machine learning in imaging Mandibular reconstruction Oral cavity cancer Quantitative computed tomography

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A Postoperative Evaluation Guideline for Computer-Assisted Reconstruction of the Mandible

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Assessment of Bone Fracture Healing Using Micro-Computed Tomography

Assessment of Bone Fracture Healing Using Micro-Computed Tomography

Published on: December 9, 2022

Area of Science:

Oral and Maxillofacial Surgery
Radiology
Biomedical Engineering

Background:

Objective quantification of bone union is crucial for evaluating mandibular reconstruction outcomes.
Current assessment methods are primarily semi-quantitative, limiting precision.

Purpose of the Study:

To assess the feasibility of using quantitative computed tomography (CT)-derived Hounsfield unit (HU) measurements to characterize bone union after fibula free flap mandibular reconstruction.
To explore the utility of machine learning algorithms in conjunction with HU measurements for improved bone union assessment.

Main Methods:

Retrospective analysis of CT scans from mandibulectomy patients who underwent fibula free flap reconstruction.
Quantitative HU measurements were obtained from bone at osteotomy sites.
Logistic regression and random forest models were developed for predicting bone union, with performance evaluated using AUC and calibration metrics.

Main Results:

Buccal HU measurements demonstrated strong predictive capability for bone union.
Random forest models achieved high area under the curve (AUC) values for predicting union (0.86) and complete union (0.92).
Multiclass models showed good discrimination for non-union and complete union, though performance for partial union was limited.

Conclusions:

CT-derived HU values are feasible for quantifying bone union after mandibular reconstruction.
This approach, particularly with machine learning, offers a promising avenue for objective assessment of bone healing.
Further validation in larger, multicenter studies is recommended.