Inverse Problems and Imaging: Theory and Applications, 2nd Edition

Dear Colleagues,

Inverse problems and imaging are two closely related and quickly emerging research fields that play a crucial role in many areas, such as medical imaging, nondestructive testing, remote sensing, or geophysics. Driven by the developments of novel imaging modalities and new application areas, theoretical and practical challenges arise that range from pure to applied mathematics and to engineering. For example, because of dose reduction in CT, only incomplete data might be available for reconstruction leading to a substantial information loss. Additionally, objects might move during examination leading to so-called motion artifacts and corrupting the reconstruction quality. To understand the impact of such (incomplete or corrupted) data sets, new theoretical results have been derived that take into account the specific data acquisition protocols. Moreover, new models are developed that capture the data generation process more reliably. Moreover, novel reconstruction techniques are developed that are tailored to specific imaging scenarios. Recently, data-driven approaches that are based on machine learning techniques have also been employed and analyzed in the context of inverse problems. Another important development is given by so-called multimodality or multispectral imaging. All these examples constitute only a small fraction of modern challenges and research directions in inverse problems and imaging.

This Special Issue aims at bringing together original research articles that reflect recent advances in the field of inverse problems and imaging, including a broad range of imaging modalities, mathematics, and applications.

Prof. Dr. Jürgen Frikel
Guest Editor

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• inverse problems
• regularization
• imaging
• image reconstruction
• image analysis
• tomography
• tomographic reconstruction
• feature reconstruction
• multimodality imaging
• multienergy imaging
• ultrasound imaging
• optical imaging
• photoacoustic tomography
• magnetic resonance tomography
• magnetic particle imaging
• artifact reduction
• dynamic inverse problems
• dynamic tomography
• motion artifacts
• deep learning in imaging