國立虎尾科技大學 |

Digital molecular magnetic resonance imaging

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Digital molecular magnetic resonance imaging/ by Bamidele O. Awojoyogbe, Michael O. Dada.
Author:	Awojoyogbe, Bamidele O.
other author:	Dada, Michael O.
Published:	Singapore :Springer Nature Singapore : : 2024.,
Description:	xxv, 348 p. :ill. (chiefly col.), digital ; : 24 cm.;
Contained By:	Springer Nature eBook
Subject:	Magnetic resonance imaging. -
Online resource:	https://doi.org/10.1007/978-981-97-6370-2
ISBN:	9789819763702

Digital molecular magnetic resonance imaging
Awojoyogbe, Bamidele O.

Digital molecular magnetic resonance imaging[electronic resource] /by Bamidele O. Awojoyogbe, Michael O. Dada. - Singapore :Springer Nature Singapore :2024. - xxv, 348 p. :ill. (chiefly col.), digital ;24 cm. - Series in bioengineering,2196-887X. - Series in bioengineering..

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.

ISBN: 9789819763702

Standard No.: 10.1007/978-981-97-6370-2doiSubjects--Topical Terms:

644374
Magnetic resonance imaging.

LC Class. No.: RC78.7.N83

Dewey Class. No.: 616.07548

Digital molecular magnetic resonance imaging
LDR:02592nam a22003495a 4500 001 1135324
003 DE-He213
005 20240825130236.0
006 m d
007 cr nn 008maaau
008 241213s2024 si s 0 eng d
020 $a 9789819763702 $q (electronic bk.)
020 $a 9789819763696 $q (paper)
024 7 $a 10.1007/978-981-97-6370-2 $2 doi
035 $a 978-981-97-6370-2
040 $a GP $c GP
041 0 $a eng
050 4 $a RC78.7.N83
072 7 $a PNFR $2 bicssc
072 7 $a SCI074000 $2 bisacsh
072 7 $a PNFR $2 thema
082 0 4 $a 616.07548 $2 23
090 $a RC78.7.N83 $b A967 2024
100 1 $a Awojoyogbe, Bamidele O. $e author. $1 https://orcid.org/0000-0003-3699-0897 $3 1355569
245 1 0 $a Digital molecular magnetic resonance imaging $h [electronic resource] / $c by Bamidele O. Awojoyogbe, Michael O. Dada.
260 $a Singapore : $c 2024. $b Springer Nature Singapore : $b Imprint: Springer,
300 $a xxv, 348 p. : $b ill. (chiefly col.), digital ; $c 24 cm.
347 $a text file $b PDF $2 rda
490 1 $a Series in bioengineering, $x 2196-887X
505 0 $a 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.
520 $a 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.
650 0 $a Magnetic resonance imaging. $3 644374
650 1 4 $a Magnetic Resonance (NMR, EPR) $3 1416952
650 2 4 $a Biomedical Engineering and Bioengineering. $3 1211019
650 2 4 $a Machine Learning. $3 1137723
650 2 4 $a Cancer Imaging. $3 679620
650 2 4 $a Mathematical Models of Cognitive Processes and Neural Networks. $3 884110
650 2 4 $a Bioanalysis and Bioimaging. $3 1387818
700 1 $a Dada, Michael O. $e author. $0 (orcid)0000-0003-4563-3867 $1 https://orcid.org/0000-0003-4563-3867 $3 1355568
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
830 0 $a Series in bioengineering. $3 1021121
856 4 0 $u https://doi.org/10.1007/978-981-97-6370-2
950 $a Physics and Astronomy (SpringerNature-11651)