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Related Concept Videos

Magnetic Resonance Imaging01:24

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...
Imaging Studies IV: Magnetic Resonance Imaging01:27

Imaging Studies IV: Magnetic Resonance Imaging

Introduction:Magnetic Resonance Imaging, or MRI, can include a specialized imaging technique of the urinary system known as Magnetic Resonance Urography (MRU). This radiation-free technique uses strong magnetic fields and radio waves to produce detailed images with the help of a computer. MRU is particularly effective for visualizing fluid-filled structures like the kidneys, ureters, and bladder.Applications of MRI in the Genitourinary SystemKidneys and Ureters: MRI detects tumors, cysts,...

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Dose Prediction Deep Learning-Based Model for VMAT of Prostate Cancer Applying Magnetic Resonance Image (MRI) in

Hossein Taheri1,2, Mohammadbagher Tavakoli1, Khadijeh Mousavi2

  • 1Student Research Committee, Isfahan University of Medical Sciences, Isfahan, Iran.

Advanced Biomedical Research
|March 23, 2026
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Summary

Deep learning models, CycleGAN and U-net, offer accurate dose prediction for prostate cancer radiotherapy using MRI data. CycleGAN demonstrated superior accuracy in predicting dose distributions for volumetric arc therapy (VMAT) on a Versa HD linac.

Keywords:
Deep learningMRIradiotherapysCT

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Area of Science:

  • Medical Physics
  • Artificial Intelligence in Medicine
  • Radiotherapy Technology

Background:

  • Radiotherapy (RT) for prostate cancer relies on accurate dose calculation by treatment planning systems (TPS).
  • Uncertainties in TPS dose distribution can impact RT outcomes.
  • Volumetric Arc Therapy (VMAT) is a common RT technique for prostate cancer.

Purpose of the Study:

  • To design a deep learning-based dose prediction model for prostate cancer VMAT.
  • To apply MRI data for dose prediction using a Versa HD linear accelerator (linac).
  • To evaluate the performance of CycleGAN and U-net deep learning frameworks for synthetic CT (sCT) generation and dose prediction.

Main Methods:

  • MRI data from 45 prostate cancer patients undergoing VMAT were utilized.
  • Cycle-consistent GAN (CycleGAN) and U-net deep learning frameworks were employed to generate synthetic CT (sCT) images from MR images.
  • Predicted doses from CycleGAN, U-net, and Monaco TPS were compared.

Main Results:

  • CycleGAN-generated sCT images showed clearer boundaries compared to U-net.
  • Gamma passing rates for CycleGAN and U-net exceeded 97% and 90%, respectively.
  • Both deep learning models proved effective for dose prediction.

Conclusions:

  • Deep learning models (CycleGAN, U-net) are viable alternatives for VMAT dose prediction in Versa HD linac.
  • CycleGAN appears to offer higher accuracy than U-net for this application.
  • Accurate dose prediction is crucial for optimizing RT outcomes in prostate cancer treatment.