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Showing 1 to 5 of 5 for “"Medical image reconstruction"”.

  1. Deep learning-based medical image reconstruction for multi-contrast magnetic resonance imaging

    … ultimately achieve better performance. However, medical images have different properties compared to natural images, and straightforward applications of deep networks for these tasks may not be feasible in clinical settings. Therefore, in this thesis, we propose deep learning-based frameworks …

    cambridge Repository record for Deep learning-based medical image reconstruction for multi-contrast magnetic resonance imaging (opens in a new tab)

  2. Robust and Efficient AI-models for Medical Image Reconstruction, Segmentation, and Multimodal Knowledge Distillation

    … this work is the development of strategies for medical image reconstruction, semantic segmentation, multimodal knowledge integration, and adversarial robustness. I propose Teach-Former, a multi-teacher knowledge distillation framework that enables lightweight models to absorb rich spatial and …

    unr Repository record for Robust and Efficient AI-models for Medical Image Reconstruction, Segmentation, and Multimodal Knowledge Distillation (opens in a new tab)

  3. Selection of Step Size for Total Variation Minimization in CT

    <p>Medical image reconstruction by total variation minimization is a newly developed area in computed tomography (CT). In compressed sensing literature, it hasbeen shown that signals with sparse representations in an orthonormal basis may be reconstructed via l1-minimization. Furthermore, if an …

    gsu Repository record for Selection of Step Size for Total Variation Minimization in CT (opens in a new tab)

  4. Electrical impedance tomography for internal radiation therapy

    … to improve the spatial resolution of EIT images.

    uoit Repository record for Electrical impedance tomography for internal radiation therapy (opens in a new tab)

  5. Development of stopping rule methods for the MLEM and OSEM algorithms used in PET image reconstruction

    … methods for the MLEM and OSEM algorithms used in image reconstruction positron emission tomography (PET). The development of the stopping rules is based on the study of the properties of both algorithms. Analyzing their mathematical expressions, it can be observed that the pixel updating …

    patras-thes Repository record for Development of stopping rule methods for the MLEM and OSEM algorithms used in PET image reconstruction (opens in a new tab)