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Magnetic Resonance Imaging

Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...

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Related Experiment Video

Updated: Jul 1, 2025

Targeting Neuronal Fiber Tracts for Deep Brain Stimulation Therapy Using Interactive, Patient-Specific Models

Targeting Neuronal Fiber Tracts for Deep Brain Stimulation Therapy Using Interactive, Patient-Specific Models

Published on: August 12, 2018

"sCT-Feasibility" - a feasibility study for deep learning-based MRI-only brain radiotherapy.

Johanna Grigo^1,2, Juliane Szkitsak^1,2, Daniel Höfler^1,2

¹Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Universitätsstraße 27, DE- 91054, Erlangen, Germany.

Radiation Oncology (London, England)

|March 8, 2024

Summary

This study explores a new MRI-only radiotherapy workflow for brain tumors, using AI to create synthetic CT scans. This approach aims to improve accuracy and patient safety by eliminating the need for traditional CT scans.

Keywords:

Artificial intelligence Deep learning MRI MRI-only workflow MRonly Radiotherapy Stereotactic radiotherapy Synthetic CT sCT

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Published on: March 24, 2022

Area of Science:

Medical Imaging
Radiation Oncology
Artificial Intelligence in Medicine

Background:

Radiotherapy (RT) for brain malignancies traditionally uses both CT and MRI, requiring image registration that introduces errors.
Magnetic Resonance Imaging (MRI) offers superior soft tissue contrast compared to Computed Tomography (CT) without increasing patient radiation dose.

Purpose of the Study:

To investigate the clinical feasibility of a deep learning-based, MRI-only workflow for brain radiotherapy.
To eliminate registration uncertainties between CT and MRI by generating synthetic CT (sCT) from MRI data.

Main Methods:

Recruited 54 patients undergoing brain radiotherapy with stereotactic mask immobilization.
Reconstructed synthetic CT (sCT) from MRI DIXON-sequence using a deep learning solution for radiotherapy planning.
Implemented quality assurance measures, including image-guided radiotherapy (IGRT) and dosimetric comparisons between sCT and CT plans.

Main Results:

The study aims to establish a brain MRI-only workflow.
It will identify risks and necessary quality assurance (QA) mechanisms for safe integration of deep learning-based sCT.
Comparative parameters between sCT and standard CT workflows will be investigated.

Conclusions:

While MRI offers better soft tissue contrast, MRI-only workflows are not yet widely adopted.
This study holistically assesses the feasibility and safety of a deep learning-based MRI-only radiotherapy workflow.