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Monte Carlo Dose Calculation Using MRI Based Synthetic CT Generated by Fully Convolutional Neural Network for Gamma Knife Radiosurgery. | LitMetric

AI Article Synopsis

  • This study investigates the dosimetric effects of synthetic computed tomography (sCT) generated from magnetic resonance (MR) images using a deep learning algorithm, specifically for Gamma Knife stereotactic radiosurgery (SRS).
  • The research involved 30 patients who underwent treatment using both T1-weighted MR images and conventional CT scans, with a deep convolutional neural network employed to convert MR images to sCT.
  • Results showed that the method achieved a mean absolute error (MAE) of 86.6, with a close similarity coefficient and minimal differences in dose measurements, indicating the potential accuracy of using deep learning-generated sCT for radiation treatment planning.

Article Abstract

The aim of this work is to study the dosimetric effect from generated synthetic computed tomography (sCT) from magnetic resonance (MR) images using a deep learning algorithm for Gamma Knife (GK) stereotactic radiosurgery (SRS). The Monte Carlo (MC) method is used for dose calculations. Thirty patients were retrospectively selected with our institution IRB's approval. All patients were treated with GK SRS based on T1-weighted MR images and also underwent conventional external beam treatment with a CT scan. Image datasets were preprocessed with registration and were normalized to obtain similar intensity for the pairs of MR and CT images. A deep convolutional neural network arranged in an encoder-decoder fashion was used to learn the direct mapping from MR to the corresponding CT. A number of metrics including the voxel-wise mean error (ME) and mean absolute error (MAE) were used for evaluating the difference between generated sCT and the true CT. To study the dosimetric accuracy, MC simulations were performed based on the true CT and sCT using the same treatment parameters. The method produced an MAE of 86.6 ± 34.1 Hundsfield units (HU) and a mean squared error (MSE) of 160.9 ± 32.8. The mean Dice similarity coefficient was 0.82 ± 0.05 for HU > 200. The difference for dose-volume parameter D95 between the ground true dose and the dose calculated with sCT was 1.1% if a synthetic CT-to-density table was used, and 4.9% compared with the calculations based on the water-brain phantom.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8504229PMC
http://dx.doi.org/10.1177/15330338211046433DOI Listing

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