Beschreibung
High-resolution magnetic resonance imaging (MRI) is clinically vital but inherently slow. Accelerating acquisition via undersampling introduces artefacts, whereas long scans risk motion blur; traditional solutions, such as compressed sensing, often fail under such heavy corruption. Consequently, this thesis investigates deep learning methods to correct these artefacts. It develops pipelines for the reconstruction of undersampled (Cartesian and radial) and motion-corrupted data, and for super-resolution, whilst exploring the integration of prior knowledge and complex-valued convolutions. Beyond visual diagnostics, the thesis examines the impact of reconstruction on automated image processing. It proposes and evaluates pipelines for classification, segmentation (supervised and weakly/semi-supervised), anomaly detection, and registration. Validated on brain tumour and vessel tasks, the study demonstrates that the proposed deep learning-based reconstruction effectively supports both clinical inspection and robust automated decision-making systems.
Autorenporträt
Dr Soumick Chatterjee is a postdoctoral researcher at Human Technopole in Milan, Italy. He is also a lecturer in AI for medical imaging at Otto von Guericke University Magdeburg, Germany, where he completed his PhD. His primary area of research focuses on machine learning, specifically deep learning, and its applications in medical imaging and genetics.
Herstellerkennzeichnung:
Springer Vieweg in Springer Science + Business Media
Abraham-Lincoln-Straße 46
65189 Wiesbaden
DE
E-Mail: juergen.hartmann@springer.com
