03897nam a22006255i 4500 978-981-97-6370-2 DE-He213 20251030112912.0 cr nn 008mamaa 240825s2024 si | s |||| 0|eng d 9789819763702 978-981-97-6370-2 10.1007/978-981-97-6370-2 doi QC762 PNFR bicssc SCI074000 bisacsh PNFR thema Awojoyogbe, Bamidele O. author. aut http://id.loc.gov/vocabulary/relators/aut 82882 Digital Molecular Magnetic Resonance Imaging [electronic resource] / by Bamidele O. Awojoyogbe, Michael O. Dada. 1st ed. 2024. Singapore : Springer Nature Singapore : Imprint: Springer, 2024. XXV, 348 p. 171 illus., 134 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Series in BioEngineering, 2196-887X 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. 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. Nuclear magnetic resonance. 82883 Biomedical engineering. Machine learning. 55059 Cancer Imaging. 69161 Neural networks (Computer science) . 64231 Biophysics. 66239 Magnetic Resonance (NMR, EPR). 82884 Biomedical Engineering and Bioengineering. Machine Learning. 55059 Cancer Imaging. 69163 Mathematical Models of Cognitive Processes and Neural Networks. 65860 Bioanalysis and Bioimaging. 68565 Dada, Michael O. author. aut http://id.loc.gov/vocabulary/relators/aut 82885 SpringerLink (Online service) Springer Nature eBook Printed edition: 9789819763696 Printed edition: 9789819763719 Printed edition: 9789819763726 Series in BioEngineering, 2196-887X 73546 https://doi.org/10.1007/978-981-97-6370-2 ZDB-2-PHA ZDB-2-SXP 62733 62733