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Updated: Jul 17, 2025

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
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Accelerating 4D image reconstruction for magnetic resonance-guided radiotherapy.

Bastien Lecoeur1,2, Marco Barbone1,2, Jessica Gough3

  • 1Joint Department of Physics at The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, 15 Cotswold Rd, London SM2 5NG, United Kingdom.

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|September 4, 2023
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Summary

High-performance computing significantly reduces 4D-MRI reconstruction times for MR-guided radiotherapy. This advancement enables faster image acquisition, improving treatment adaptation for patients undergoing radiation therapy.

Keywords:
4D-MRIHigh-performance computingIntrafraction motionMR-guided RadiotherapyMR-integrated Proton Therapy

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Area of Science:

  • Medical Physics
  • Radiotherapy Technology
  • Image Reconstruction

Background:

  • Physiological motion complicates radiation dose delivery in external beam and particle therapy.
  • Four-dimensional magnetic resonance imaging (4D-MRI) offers excellent soft-tissue contrast for tracking intra-fractional motion.
  • Current 4D-MRI reconstruction times are too long for online treatment adaptation in MR-guided radiotherapy.

Purpose of the Study:

  • To accelerate 4D-MRI reconstruction times using high-performance computing (HPC).
  • To enable the use of 4D-MRI for online adaptive workflows in MR-guided radiotherapy.
  • To explore the application of accelerated 4D-MRI in particle therapy.

Main Methods:

  • Developed fast, parallelized, open-source implementations of the extra-dimensional golden-angle radial sparse parallel algorithm for CPU and GPU architectures.
  • Scanned four patients with pancreatic adenocarcinoma using a radial stack-of-stars gradient echo sequence on a 1.5T MR-Linac.
  • Assessed reconstruction time, image quality (SSIM), and scalability across different architectures and binning strategies.

Main Results:

  • Reconstructed 4D-MRI images were highly consistent with the reference implementation (SSIM > 0.99).
  • CPU + GPU implementation achieved reconstruction times of 60 ± 1 seconds, over 17 times faster than the reference.
  • Hyper-scaling with multiple GPUs further reduced reconstruction times, demonstrating efficient parallelization.

Conclusions:

  • High-performance computing methods can significantly reduce respiratory-resolved 4D-MRI reconstruction times.
  • Accelerated 4D-MRI is feasible for online workflows in MR-guided radiotherapy.
  • This technology holds potential for improving precision in particle therapy by accounting for motion.