title: New Imaging Frontiers in Cardiology: Fast and Quantitative Maps from Raw Data.
abstract: Among the novelties in the field of cardiovascular imaging, the construction of quantitative maps in a fast and efficient way is one of the most interesting aspects of the clinical research. Quantitative parametric maps are typically obtained by post processing dynamic images, that is, sets of images usually acquired in different temporal intervals, where several images with different contrasts are obtained. Magnetic resonance imaging, and emission tomography (positron emission and single photon emission) are the imaging techniques best suited for the formation of quantitative maps.
  
  
  
  In this review article we present several methods that can be used for obtaining parametric maps, in a fast way, starting from the acquired raw data. We describe both methods commonly used in clinical research, and more innovative methods that build maps directly from the raw data, without going through the image reconstruction.
  
  
  
  We briefly described recently developed methods in magnetic resonance imaging that accelerate further the MR raw data generation, based on appropriate sub-sampling of k-space; then, we described recently developed methods for generating MR parametric maps. With regard to the emission tomography techniques, we gave an overview of both conventional methods, and more recently developed direct estimation algorithms for parametric image reconstruction from dynamic positron emission tomography data.
  
  
  
  We have provided an overview of the possible approaches that can be followed to realize useful parametric maps from imaging raw data. We moved from the conventional approaches to more recent and efficient methods for accelerating the raw data generation and the of parametric maps formation.
authors.affiliation: CNR Institute of Clinical Physiology, Pisa. Italy.
authors.forename: Maria Filomena
authors.initials: MF
authors.lastname: Santarelli
authors.affiliation: Dipartimento di Ingegneria dell'Informazione, University of Pisa, Pisa. Italy.
authors.forename: Nicola
authors.initials: N
authors.lastname: Vanello
authors.affiliation: Dipartimento di Ingegneria dell'Informazione, University of Pisa, Pisa. Italy.
authors.forename: Michele
authors.initials: M
authors.lastname: Scipioni
authors.affiliation: Philips Medical Systems, Best. Netherlands.
authors.forename: Giuseppe
authors.initials: G
authors.lastname: Valvano
authors.affiliation: Dipartimento di Ingegneria dell'Informazione, University of Pisa, Pisa. Italy.
authors.forename: Luigi
authors.initials: L
authors.lastname: Landini
mesh_terms: D002309:Cardiology / Q000379:methods / Q000639:trends*; D002318:Cardiovascular Diseases / Q000000981:diagnostic imaging* / Q000503:physiopathology; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods / Q000639:trends*; D019028:Magnetic Resonance Imaging, Cine / Q000379:methods / Q000639:trends*; D049268:Positron-Emission Tomography / Q000379:methods / Q000639:trends*; D013223:Statistics as Topic / Q000379:methods / Q000639:trends*
keywords: 4D flow MRI.; Magnetic resonance imaging; cardiac BOLD; cardiac fiber tracking; dynamic imaging; emission tomography; kinetic parameters; parametric maps