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Just-in-time learning enables online training of deep neural networks (DNNs) for real-time X-ray tomography. This approach overcomes data limitations by leveraging continuous experimental data for DNN training during experiments.

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Area of Science:

  • Scientific Imaging
  • Machine Learning
  • X-ray Tomography

Background:

  • Real-time X-ray tomography pipelines, like RECAST3D, enable rapid visualization of dynamic experiments.
  • Deep Neural Networks (DNNs) offer faster processing for image analysis, potentially optimizing experiments.
  • Integrating DNNs is challenging due to the need for pre-existing, representative training data, which is often unavailable in scientific settings.

Purpose of the Study:

  • To develop an online deep neural network (DNN) training strategy for real-time X-ray tomography.
  • To overcome the challenge of unavailable pre-experiment training data in dynamic experimental environments.
  • To enable the integration of DNNs for real-time image processing and analysis within tomography pipelines.

Main Methods:

  • Introduced 'just-in-time learning,' an online DNN training strategy.
  • Leveraged the spatio-temporal continuity of consecutive tomographic reconstructions for training.
  • Implemented and studied the approach using self-supervised Noise2Inverse denoising with real-world X-ray data.

Main Results:

  • Demonstrated the feasibility of training and deploying small DNNs during an experiment.
  • Successfully applied the just-in-time learning strategy to X-ray tomography data.
  • Showcased the potential to simplify real-time steering and optimization of dynamic experiments.

Conclusions:

  • Just-in-time learning effectively addresses the challenge of data scarcity for DNNs in real-time tomography.
  • This online training strategy allows for adaptive DNN deployment during experiments.
  • The approach enhances the utility of real-time X-ray tomography by enabling integrated DNN-based analysis.