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Related Concept Videos

Computed Tomography01:10

Computed Tomography

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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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Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

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DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...
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Monte Carlo Dose Calculation Using MRI Based Synthetic CT Generated by Fully Convolutional Neural Network for Gamma

Jiankui Yuan1, Elisha Fredman1, Jian-Yue Jin1

  • 1114516University Hospitals Cleveland Medical Center, Cleveland, USA.

Technology in Cancer Research & Treatment
|October 11, 2021
PubMed
Summary

This study developed a deep learning method to create synthetic CT (sCT) from MRI for Gamma Knife stereotactic radiosurgery. The synthetic CT showed high accuracy, with minimal impact on radiation dose calculations.

Keywords:
Gamma knifedeep learningradiosurgerysynthetic CT

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Area of Science:

  • Medical Physics
  • Radiosurgery Technology
  • Artificial Intelligence in Medicine

Background:

  • Stereotactic radiosurgery (SRS) typically relies on CT scans for accurate radiation dose calculation.
  • Integrating MRI data with deep learning offers a potential alternative for generating synthetic CT (sCT) images.
  • This approach could enhance treatment planning for Gamma Knife (GK) SRS by leveraging the superior soft-tissue contrast of MRI.

Purpose of the Study:

  • To evaluate the dosimetric accuracy of synthetic CT (sCT) images generated using a deep learning algorithm from MRI data for Gamma Knife (GK) stereotactic radiosurgery (SRS).
  • To assess the performance of a deep convolutional neural network in translating MR images to sCT.
  • To quantify the impact of sCT on radiation dose calculations compared to conventional CT scans.

Main Methods:

  • A deep convolutional neural network (encoder-decoder architecture) was trained to generate sCT from T1-weighted MR images.
  • Thirty patients undergoing GK SRS were retrospectively analyzed, with MR and CT datasets preprocessed and normalized.
  • Monte Carlo (MC) simulations were performed for dose calculations using both true CT and generated sCT images to assess dosimetric accuracy.

Main Results:

  • The deep learning model achieved a Mean Absolute Error (MAE) of 86.6 ± 34.1 HU and a Mean Squared Error (MSE) of 160.9 ± 32.8.
  • A mean Dice similarity coefficient of 0.82 ± 0.05 was obtained for regions with HU > 200.
  • Dosimetric analysis showed a minimal difference of 1.1% in the D95 dose-volume parameter when using sCT with a CT-to-density table, compared to 4.9% without it.

Conclusions:

  • Deep learning-based sCT generation from MR images is a viable method for GK SRS.
  • The generated sCT images demonstrate high fidelity and accuracy, suitable for treatment planning.
  • Utilizing sCT with an appropriate CT-to-density conversion significantly preserves dosimetric accuracy in SRS, potentially reducing the need for CT scans.