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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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Related Experiment Video

Updated: May 1, 2026

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

Centiloid values from deep learning-based CT parcellation: a valid alternative to freesurfer.

Yeo Jun Yoon¹, Seungbeom Seo¹, Sangwon Lee²

¹Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Republic of Korea.

Alzheimer'S Research & Therapy

|October 1, 2025

Summary

A new deep learning CT pipeline offers accurate amyloid-beta quantification for Alzheimer's disease, serving as a reliable MRI-free alternative to standard Centiloid scales.

Keywords:

Alzheimer’s disease Amyloid imaging Centiloid Florbetaben

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Area of Science:

Neuroimaging
Radiology
Artificial Intelligence

Background:

Amyloid PET/CT is crucial for Alzheimer's disease (AD) amyloid-beta (Aβ) quantification using the Centiloid (CL) scale.
MRI-based CL pipelines present challenges including cost, contraindications, and patient burden.
A deep learning CT parcellation pipeline was developed as an alternative.

Purpose of the Study:

To develop and evaluate a deep learning-based CT parcellation pipeline for Centiloid (CL) scale calibration.
To assess its performance as an MRI-free alternative to standard CL quantification pipelines.

Main Methods:

306 participants (23 young controls, 283 patients) underwent 18F-florbetaben (FBB) PET/CT and MRI.
CT images were used with a deep learning pipeline, and results were compared to FreeSurfer (FS) and standard pipelines.
Regression, effect size, variance, and ROC analyses were employed for comparison.

Main Results:

The CT parcellation pipeline demonstrated high concordance with FS (R² = 0.99) for reliable CL quantification.
Pipelines showed similar variance in young controls and effect sizes between young controls and ADCI.
ROC analyses confirmed comparable accuracy and CL thresholds, supporting CT parcellation as a viable alternative.

Conclusions:

The CT parcellation pipeline provides accurate CL quantification, comparable to MRI-based methods.
It serves as a reliable MRI-free alternative for amyloid-beta deposition assessment in Alzheimer's disease.
Sequential CT and PET acquisition in PET/CT enhances spatial alignment and quantification reliability.