03690nam a22005775i 4500001001800000003000900018005001700027007001500044008004100059020003700100024003500137050001000172072001700182072002300199072001600222100009200238245011900330250001800449264007200467300006700539336002600606337002600632338003600658347002400694490004100718505048300759520102301242650003902265650002802304650002902332650002802361650004902389650002302438650004202461650004702503650002902550650002702579650007502606650003902681700008502720710003402805773002602839776003602865776003602901776003602937830004802973856004603021912001403067912001403081999001703095978-981-97-6370-2DE-He21320251030112912.0cr nn 008mamaa240825s2024    si |    s    |||| 0|eng d  a97898197637029978-981-97-6370-27 a10.1007/978-981-97-6370-22doi 4aQC762 7aPNFR2bicssc 7aSCI0740002bisacsh 7aPNFR2thema1 aAwojoyogbe, Bamidele O.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut98288210aDigital Molecular Magnetic Resonance Imagingh[electronic resource] /cby Bamidele O. Awojoyogbe, Michael O. Dada.  a1st ed. 2024. 1aSingapore :bSpringer Nature Singapore :bImprint: Springer,c2024.  aXXV, 348 p. 171 illus., 134 illus. in color.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext filebPDF2rda1 aSeries in BioEngineering,x2196-887X0 aGeneral 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.  aThis 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. 0aNuclear magnetic resonance.982883 0aBiomedical engineering. 0aMachine learning.955059 0aCancerxImaging.969161 0aNeural networks (Computer science) .964231 0aBiophysics.96623914aMagnetic Resonance (NMR, EPR).98288424aBiomedical Engineering and Bioengineering.24aMachine Learning.95505924aCancer Imaging.96916324aMathematical Models of Cognitive Processes and Neural Networks.96586024aBioanalysis and Bioimaging.9685651 aDada, Michael O.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut9828852 aSpringerLink (Online service)0 tSpringer Nature eBook08iPrinted edition:z978981976369608iPrinted edition:z978981976371908iPrinted edition:z9789819763726 0aSeries in BioEngineering,x2196-887X97354640uhttps://doi.org/10.1007/978-981-97-6370-2  aZDB-2-PHA  aZDB-2-SXP  c62733d62733