Awojoyogbe, Bamidele O. creator author. aut http://id.loc.gov/vocabulary/relators/aut Dada, Michael O. author. aut http://id.loc.gov/vocabulary/relators/aut SpringerLink (Online service) text si 2024 1st ed. 2024. monographic eng

electronic

electronic resource

access XXV, 348 p. 171 illus., 134 illus. in color. online resource. 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. 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. by Bamidele O. Awojoyogbe, Michael O. Dada. Nuclear magnetic resonance Biomedical engineering Machine learning Cancer Imaging Neural networks (Computer science)  Biophysics Magnetic Resonance (NMR, EPR) Biomedical Engineering and Bioengineering Machine Learning Cancer Imaging Mathematical Models of Cognitive Processes and Neural Networks Bioanalysis and Bioimaging QC762 9789819763702 https://doi.org/10.1007/978-981-97-6370-2 https://doi.org/10.1007/978-981-97-6370-2 240825 20251030112912.0 978-981-97-6370-2