Name: Deep Learning Innovations in MRI Reconstruction and Analysis: Enhancing Image Quality for Robust Image Processing and Clinical Decision Making (en Inglés)
SKU: 66922955
Price: 102.87 USD
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Author: Chatterjee, Soumick

portada Deep Learning Innovations in MRI Reconstruction and Analysis: Enhancing Image Quality for Robust Image Processing and Clinical Decision Making (en Inglés)

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

Formato

Libro Físico

Editorial

Springer Vieweg

Autor

Chatterjee, Soumick

Año

2026

Idioma

Inglés

N° páginas

342

Encuadernación

Tapa Blanda

Dimensiones

21x14.8 cm

ISBN13

9783658507404

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

Chatterjee, Soumick (Autor) · Springer Vieweg · Tapa Blanda

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Reseña del libro "Deep Learning Innovations in MRI Reconstruction and Analysis: Enhancing Image Quality for Robust Image Processing and Clinical Decision Making (en Inglés)"

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.