title: Models and methods for analyzing DCE-MRI: a review.
abstract: OBJECTIVE
  To present a review of most commonly used techniques to analyze dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), discusses their strengths and weaknesses, and outlines recent clinical applications of findings from these approaches.
  
  
  METHODS
  DCE-MRI allows for noninvasive quantitative analysis of contrast agent (CA) transient in soft tissues. Thus, it is an important and well-established tool to reveal microvasculature and perfusion in various clinical applications. In the last three decades, a host of nonparametric and parametric models and methods have been developed in order to quantify the CA's perfusion into tissue and estimate perfusion-related parameters (indexes) from signal- or concentration-time curves. These indexes are widely used in various clinical applications for the detection, characterization, and therapy monitoring of different diseases.
  
  
  RESULTS
  Promising theoretical findings and experimental results for the reviewed models and techniques in a variety of clinical applications suggest that DCE-MRI is a clinically relevant imaging modality, which can be used for early diagnosis of different diseases, such as breast and prostate cancer, renal rejection, and liver tumors.
  
  
  CONCLUSIONS
  Both nonparametric and parametric approaches for DCE-MRI analysis possess the ability to quantify tissue perfusion.
authors.affiliation: BioImaging Laboratory, Department of Bioengineering, University of Louisville, Louisville, Kentucky 40292 and Electronics and Communication Engineering Department, Mansoura University, Mansoura 35516, Egypt.
authors.forename: Fahmi
authors.initials: F
authors.lastname: Khalifa
authors.affiliation: BioImaging Laboratory, Department of Bioengineering, University of Louisville, Louisville, Kentucky 40292.
authors.forename: Ahmed
authors.initials: A
authors.lastname: Soliman
authors.affiliation: BioImaging Laboratory, Department of Bioengineering, University of Louisville, Louisville, Kentucky 40292.
authors.forename: Ayman
authors.initials: A
authors.lastname: El-Baz
authors.affiliation: Radiology Department, Urology and Nephrology Center, Mansoura University, Mansoura 35516, Egypt.
authors.forename: Mohamed
authors.initials: M
authors.lastname: Abou El-Ghar
authors.affiliation: Radiology Department, Urology and Nephrology Center, Mansoura University, Mansoura 35516, Egypt.
authors.forename: Tarek
authors.initials: T
authors.lastname: El-Diasty
authors.affiliation: Department of Computer Science, University of Auckland, Auckland 1142, New Zealand.
authors.forename: Georgy
authors.initials: G
authors.lastname: Gimel'farb
authors.affiliation: Kidney Transplantation-Kidney Disease Center, University of Louisville, Louisville, Kentucky 40202.
authors.forename: Rosemary
authors.initials: R
authors.lastname: Ouseph
authors.affiliation: Kidney Transplantation-Kidney Disease Center, University of Louisville, Louisville, Kentucky 40202.
authors.forename: Amy C
authors.initials: AC
authors.lastname: Dwyer
mesh_terms: D055592:Biophysical Phenomena; D001943:Breast Neoplasms / Q000175:diagnosis; D003287:Contrast Media; D003936:Diagnosis, Computer-Assisted / Q000379:methods* / Q000706:statistics & numerical data; D005260:Female; D006801:Humans; D007674:Kidney Diseases / Q000175:diagnosis; D008279:Magnetic Resonance Imaging / Q000379:methods* / Q000706:statistics & numerical data; D008297:Male; D008962:Models, Theoretical; D017202:Myocardial Ischemia / Q000175:diagnosis; D011471:Prostatic Neoplasms / Q000175:diagnosis; D018709:Statistics, Nonparametric
chemical_list: D003287:Contrast Media