@article{1438670,
      recid = {1438670},
      author = {Dada, Michael O., and Awojoyogbe, Bamidele O.,},
      title = {Computational molecular magnetic resonance imaging for  neuro-oncology /},
      pages = {1 online resource},
      abstract = {Based on the analytical methods and the computer programs  presented in this book, all that may be needed to perform  MRI tissue diagnosis is the availability of relaxometric  data and simple computer program proficiency. These  programs are easy to use, highly interactive and the data  processing is fast and unambiguous. Laboratories (with or  without sophisticated facilities) can perform computational  magnetic resonance diagnosis with only T1 and T2 relaxation  data. The results have motivated the use of data to produce  data-driven predictions required for machine learning,  artificial intelligence (AI) and deep learning for  multidisciplinary and interdisciplinary research.  Consequently, this book is intended to be very useful for  students, scientists, engineers, the medial personnel and  researchers who are interested in developing new concepts  for deeper appreciation of computational magnetic Resonance  Imaging for medical diagnosis, prognosis, therapy and  management of tissue diseases.},
      url = {http://library.usi.edu/record/1438670},
      doi = {https://doi.org/10.1007/978-3-030-76728-0},
}