Deep Learning Enhanced Contrast Source Inversion And Phase Error Based Conductivity Correction For Electrical Properties Tomography: Exploration Of Physics-Assisted Deep Learning Methodology For Magnetic Resonance Based Electrical Properties Tomography

Magnetic resonance electrical properties tomography is a type of quantitative magnetic resonance imaging that aims to reconstruct the conductivity and permittivity of biological tissue. These electrical properties of the tissue can be used to compute the specific absorption rate, to differentiate tumours from healthy tissue and for hyperthermia...

Semantically-Guided 3D Building Facade Reconstruction: A Learning-Based MVS Approach

This thesis introduces a Learned-Based Multi-View Semantic Stereo method, addressing the limitations of traditional and learned-based Multi-View Stereo (MVS) techniques in reconstructing reflective and low-textured regions, particularly prevalent in 3D models of buildings. Traditional methods lack completeness, while learned-based methods...

Deep Learning for Post-Contrast T1-Weighted Brain MRI Synthesis

Introduction: Magnetic Resonance Imaging is a commonly used technique for the initial diagnosis of gliomas. T1, T2, T2-FLAIR, and post-contrast T1 with gadolinium-based contrast agents (GBCAs) can show tumor characteristics. However, using this contrast agent poses a risk to patients with kidney failures, has environmental impact, and increases...

Infrared thermal defect identification and reconstruction of artworks using a spatiotemporal deep neural network

Assessment of cultural heritage assets is now extremely important all around the world. Non-destructive inspection is essential for preserving the integrity of the artworks while avoiding the loss of any precious materials that make it up. The use of Infrared Thermography (IRT) is an interesting concept since surface and subsurface faults can be...