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Related Experiment Video

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Deep Neural Networks for Image-Based Dietary Assessment
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Simulation-based deep artifact correction with Convolutional Neural Networks for limited angle artifacts.

Alena-Kathrin Schnurr1, Khanlian Chung1, Tom Russ1

  • 1Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Theodor Kutzer Ufer 1-3, 68167 Mannheim, Germany.

Zeitschrift Fur Medizinische Physik
|February 18, 2019
PubMed
Summary
This summary is machine-generated.

Deep Artifact Correction (DAC) reduces artifacts in circular tomosynthesis (cTS) 3D imaging. This AI-driven method enhances image quality for low-dose interventions, improving radiation safety.

Keywords:
CBCTConvolutional Neural NetworksLimited angle artifactsNon-conventional scan trajectoriesSimulation-based deep learning

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Radiology
  • Interventional Radiology

Background:

  • Non-conventional scan trajectories, like circular tomosynthesis (cTS), offer low-dose 3D imaging for interventions, enhancing radiation protection.
  • cTS produces anisotropic image quality, with artifacts in planes perpendicular to the preferred focus plane due to limited angular data.
  • Reducing these artifacts is crucial for improving image quality while maintaining low-dose benefits in interventional procedures.

Purpose of the Study:

  • To investigate the efficacy of Deep Artifact Correction (DAC) in mitigating limited angle artifacts in circular tomosynthesis (cTS).
  • To evaluate the performance of U-Net-based and 3D-ResNet deep learning models for artifact reduction in cTS imaging.
  • To compare the image quality of DAC-corrected cTS with conventional methods like cone beam CT (CBCT).

Main Methods:

  • Trained three U-Net-based networks and one 3D-ResNet using simulated cTS data from digital phantoms.
  • The networks were trained to estimate correction maps to transform cTS images towards the ground truth phantom.
  • Evaluated artifact reduction and image quality on simulated and real cTS scans, including those with objects dissimilar to training data.

Main Results:

  • U-Net-corrected cTS achieved a 59.35% reduction in Root Mean Squared Error (RMSE) compared to standard cTS, reaching 124.24 HU.
  • The image quality of the corrected cTS was comparable to simulated cone beam CT (CBCT).
  • DAC effectively mitigated artifacts even in scans of objects significantly different from the training data, showing 45.18% and 26.4% error reduction on real cTS scans using 3D-ResNet compared to high-dose CBCT.

Conclusions:

  • Simulation-based Deep Artifact Correction (DAC) successfully mitigates limited angle artifacts in circular tomosynthesis (cTS).
  • Deep learning approaches, particularly U-Net architectures, can significantly enhance cTS image quality, achieving results similar to CBCT at lower doses.
  • DAC demonstrates robustness in artifact correction for diverse object types, offering a promising solution for low-dose interventional 3D imaging.