Deep Learning Innovations in MRI Reconstruction and Analysis : Enhancing Image Quality for Robust Image Processing and Clinical Decision Making

Soumick Chatterjee

ISBN 10: 3658507403 / ISBN 13: 9783658507404

Published by Springer, 2026

About this Item

Druck auf Anfrage Neuware - Printed after ordering - 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.

Seller Inventory # 9783658507404

Bibliographic Details

• Title: Deep Learning Innovations in MRI Reconstruction and Analysis : Enhancing Image Quality for Robust Image Processing and Clinical Decision Making
• Publisher: Springer
• Publication Date: 2026
• Language: English
• ISBN 10: 3658507403
• ISBN 13: 9783658507404
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 210W x 148H x 27D millimeters 210 millimeters width by 148 millimeters height by 27 millimeters depth
• Weight: 620 grams

About this title

Synopsis

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.

About the Author

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.