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Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
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Related Experiment Video

Updated: Jul 15, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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Unsupervised knowledge-transfer for learned image reconstruction.

Riccardo Barbano1, Željko Kereta1, Andreas Hauptmann1,2

  • 1Department of Computer Science, University College London, Gower Street, London WC1E 6BT, United Kingdom.

Inverse Problems
|September 25, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces an unsupervised deep learning method for medical image reconstruction, overcoming the need for extensive paired data. The novel Bayesian framework enhances reconstruction quality and provides uncertainty information, particularly for varied data distributions.

Keywords:
Bayesian deep learningcomputed tomographyimage reconstructionpretrainingunsupervised learning

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Area of Science:

  • Medical imaging
  • Deep learning
  • Computational imaging

Background:

  • Deep learning image reconstruction shows promise but requires large paired datasets, often unavailable in medical imaging.
  • Existing methods struggle with data distribution shifts, impacting reconstruction quality and reliability.

Purpose of the Study:

  • To develop a novel unsupervised knowledge-transfer paradigm for learned image reconstruction within a Bayesian framework.
  • To enable accurate reconstruction from limited or varied medical imaging data while providing uncertainty quantification.

Main Methods:

  • A two-phase training approach: initial training on simulated data, followed by unsupervised fine-tuning on realistic data.
  • Utilizing a Bayesian framework to incorporate prior knowledge and generate predictive uncertainty maps.
  • Experimental validation on low-dose and sparse-view computed tomography datasets.

Main Results:

  • The proposed unsupervised method achieves competitive performance against state-of-the-art supervised and unsupervised techniques.
  • Significant improvements in visual and quantitative metrics (PSNR, SSIM) were observed, especially for data with different distributions than the training set.
  • The framework successfully provides predictive uncertainty information for reconstructed images.

Conclusions:

  • The developed unsupervised Bayesian framework effectively addresses the data scarcity issue in deep learning-based medical image reconstruction.
  • This approach offers robust and accurate reconstructions, even with domain shifts, and quantifies uncertainty, enhancing clinical utility.