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Imaging Studies I: CT and MRI

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Computed Tomography (CT) scan:
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Related Experiment Video

Updated: Apr 6, 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

Estimating CT Image From MRI Data Using Structured Random Forest and Auto-Context Model.

Tri Huynh, Yaozong Gao, Jiayin Kang

IEEE Transactions on Medical Imaging

|August 5, 2015

Summary

This summary is machine-generated.

This study introduces a learning-based method to generate Computed Tomography (CT) images from Magnetic Resonance (MR) images, crucial for Positron Emission Tomography (PET) attenuation correction without extra radiation exposure.

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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Area of Science:

Medical Imaging
Machine Learning
Radiotherapy

Background:

Computed Tomography (CT) is vital for diagnosis and radiotherapy, but involves radiation exposure.
CT images are essential for Positron Emission Tomography (PET) attenuation correction (AC).
New PET-MR scanners lack CT, necessitating CT image estimation from MR images for AC.

Purpose of the Study:

To develop a robust learning-based method for estimating CT images from corresponding MR images.
To enable accurate PET attenuation correction using only MR data.
To reduce radiation dose by minimizing CT acquisition.

Main Methods:

A learning-based approach partitions MR images into patches.
Structured random forests with ensemble models predict CT patches.
Innovatively crafted features and auto-context models refine predictions.
Rigid-body alignment integrates spatial information, avoiding deformable registration issues.

Main Results:

The method accurately predicts CT images from MR images for human brain and prostate datasets.
Effective performance is demonstrated even with significant anatomical shape variations.
The proposed method outperforms two existing state-of-the-art techniques.

Conclusions:

The developed method reliably estimates CT images from MR images.
This approach offers a viable solution for PET AC in hybrid PET-MR systems.
It holds potential for dose reduction in medical imaging protocols.