Digital Molecular Magnetic Resonance Imaging

2024
ISBN: 978-981-97-6369-6
Verlag: Springer Nature Singapore

Buch, Englisch, 348 Seiten, HC runder Rücken kaschiert, Format (B × H): 160 mm x 241 mm, Gewicht: 797 g

Reihe: Series in BioEngineering

This book pushes the limits of conventional MRI visualization methods by completely changing the medical imaging landscape and leads to innovations that will help patients and healthcare providers alike. It enhances the capabilities of MRI anatomical visualization to a level that has never before been possible for researchers and clinicians. The computational and digital algorithms developed can enable a more thorough understanding of the intricate structures found within the human body, surpassing the constraints of traditional 2D methods. The Physics-informed Neural Networks as presented can enhance three-dimensional rendering for deeper understanding of the spatial relationships and subtle abnormalities of anatomical features and sets the stage for upcoming advancements that could impact a wider range of digital heath modalities. This book opens the door to ultra-powerful digital molecular MRI powered by quantum computing that can perform calculations that would take supercomputers millions of years.

Zielgruppe

Research

Autoren/Hrsg.

Dada, Michael O.

Awojoyogbe, Bamidele O.

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Technische Wissenschaften Sonstige Technologien | Angewandte Technik Medizintechnik, Biomedizintechnik
• Naturwissenschaften Chemie Analytische Chemie Magnetresonanz
• Naturwissenschaften Physik Angewandte Physik Biophysik
• Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Angewandte Mathematik, Mathematische Modelle
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Medizintechnik, Biomedizintechnik, Medizinische Werkstoffe
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Klinische und Innere Medizin Onkologie, Krebsforschung

Inhaltsverzeichnis

General Introduction.- Physics Informed Neural Networks PINNS.- New Methodology and Modelling In Magnetic Resonance Imaging.- Physics informed Neural Network for Addressing Spatial and Temporal.- Machine Learning Model for Diagnosis of Pulmonary Arterial Hypertension.- A Convolution Neural Network for Artificial Intelligence-Based Classification of Alzheimer’s Diseases.- Physics informed Neural Networks for Nuclear Magnetic Resonance Guided Clinical Hyperthermia.

Über Autor(innen)

Dr. Awojoyogbe O. B. has Ph.D. (1997) in Med. Phy. from Federal University of Technology Nigeria in collaboration with Institute of Biomed. Eng. & Med. Informatics, ETH Zurich & Univ. of Zurich. He won the 2003 Young African Math. Medal Award by African Mathematical Union. He is a professor of Physics and associate member of International Center for Theoretical Physics (ICTP), Trieste, Italy. His research field is Theory, Dynamics & Applications of Magnetic Resonance Imaging. He is a researcher of the Centre for Vaccine & Drug Development at the Federal University of Technology Minna. He served as the deputy director, Academic Planning (2008–2011) and the director, Academic Planning (2011–2015) at his University. His current research interests are the application of computational molecular magnetic resonance imaging for digital health, Quantum Artificial Intelligence (QAI)-and in particular, its subset research area Quantum Machine Learning (QML).

Dr. Dada O. M. received his B.Tech. (Physics/Electronics with First class honours) from the Fed. Univ. of Tech., Minna, Nigeria in 2006, M.Tech. (Solid State Physics) and Ph.D. (Physics) from the same university in 2010 and 2016, respectively, under the supervision of Professor O.B. Awojoyogbe. He currently holds an academic position as a senior lecturer in the Dept. of Physics of the University. He has been involved in magnetic resonance modeling of biological systems and applications of computational molecular magnetic resonance imaging for digital health, Quantum Artificial Intelligence (QAI)-and in particular, its subset research area Quantum Machine Learning (QML).