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Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring
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MRI-based synthetic CT generation using semantic random forest with iterative refinement.

Yang Lei1, Joseph Harms1, Tonghe Wang1

  • 1Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, 30322, United States of America.

Physics in Medicine and Biology
|March 1, 2019
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This summary is machine-generated.

This study introduces a learning-based method to create synthetic CT (sCT) images from MRI scans, enabling MRI-only radiation therapy planning. This approach accurately generates electron density information, improving treatment accuracy and workflow.

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Area of Science:

  • Medical Imaging
  • Radiotherapy Physics
  • Machine Learning in Medicine

Background:

  • Magnetic resonance imaging (MRI) offers superior soft tissue contrast for radiation therapy planning compared to computed tomography (CT).
  • MRI-only treatment planning can reduce co-registration errors, costs, radiation exposure, and streamline clinical workflows.
  • Current limitations include reliance on CT-derived electron density data and difficulty distinguishing air/bone regions in MRI.

Purpose of the Study:

  • To develop and validate a learning-based method for generating patient-specific synthetic CT (sCT) images from anatomical MRI.
  • To enable MRI-only radiotherapy treatment planning by providing accurate electron density information.

Main Methods:

  • An auto-context model with patch-based features was integrated into a classification random forest to generate semantic information.
  • A series of regression random forests, guided by the auto-context model, were trained using semantic and anatomical features.
  • The algorithm was evaluated on 14 patient datasets with T1-weighted MRI and corresponding CT images of the brain.

Main Results:

  • The synthetic CT (sCT) generation achieved a mean absolute error (MAE) of 57.45 ± 8.45 HU, peak signal-to-noise ratio (PSNR) of 28.33 ± 1.68 dB, and normalized cross-correlation (NCC) of 0.97 ± 0.01.
  • Dose calculations on sCT showed minimal differences compared to CT: <0.2 Gy for planning target volumes (PTVs) and <0.02 Gy for organs at risk (OARs).

Conclusions:

  • The proposed learning-based method successfully generates synthetic CT images from MRI, providing accurate electron density information.
  • This technique facilitates MRI-only radiotherapy treatment planning, potentially improving accuracy and efficiency.