title: Liver 4DMRI: A retrospective image-based sorting method.
abstract: OBJECTIVE
  Four-dimensional magnetic resonance imaging (4DMRI) is an emerging technique in radiotherapy treatment planning for organ motion quantification. In this paper, the authors present a novel 4DMRI retrospective image-based sorting method, providing reduced motion artifacts than using a standard monodimensional external respiratory surrogate.
  
  
  METHODS
  Serial interleaved 2D multislice MRI data were acquired from 24 liver cases (6 volunteers + 18 patients) to test the proposed 4DMRI sorting. Image similarity based on mutual information was applied to automatically identify a stable reference phase and sort the image sequence retrospectively, without the use of additional image or surrogate data to describe breathing motion.
  
  
  RESULTS
  The image-based 4DMRI provided a smoother liver profile than that obtained from standard resorting based on an external surrogate. Reduced motion artifacts were observed in image-based 4DMRI datasets with a fitting error of the liver profile measuring 1.2 ± 0.9 mm (median ± interquartile range) vs 2.1 ± 1.7 mm of the standard method.
  
  
  CONCLUSIONS
  The authors present a novel methodology to derive a patient-specific 4DMRI model to describe organ motion due to breathing, with improved image quality in 4D reconstruction.
authors.affiliation: Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milano 20133, Italy.
authors.forename: Chiara
authors.initials: C
authors.lastname: Paganelli
authors.affiliation: Division of Radiology, Istituto Europeo di Oncologia, Milano 20133, Italy.
authors.forename: Paul
authors.initials: P
authors.lastname: Summers
authors.affiliation: Division of Radiology, Istituto Europeo di Oncologia, Milano 20133, Italy and Department of Health Sciences, Università di Milano, Milano 20133, Italy.
authors.forename: Massimo
authors.initials: M
authors.lastname: Bellomi
authors.affiliation: Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milano 20133, Italy and Bioengineering Unit, Centro Nazionale di Adroterapia Oncologica, Pavia 27100, Italy.
authors.forename: Guido
authors.initials: G
authors.lastname: Baroni
authors.affiliation: Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milano 20133, Italy and Bioengineering Unit, Centro Nazionale di Adroterapia Oncologica, Pavia 27100, Italy.
authors.forename: Marco
authors.initials: M
authors.lastname: Riboldi
mesh_terms: D066264:Datasets as Topic; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D007257:Information Theory; D008099:Liver / Q000033:anatomy & histology* / Q000473:pathology* / Q000502:physiology / Q000503:physiopathology; D008113:Liver Neoplasms / Q000473:pathology; D008279:Magnetic Resonance Imaging / Q000379:methods*; D009038:Motion; D010363:Pattern Recognition, Automated / Q000379:methods; D012119:Respiration; D055815:Young Adult