title: Atlas-based analysis of 4D flow CMR: automated vessel segmentation and flow quantification.
abstract: BACKGROUND
  Flow volume quantification in the great thoracic vessels is used in the assessment of several cardiovascular diseases. Clinically, it is often based on semi-automatic segmentation of a vessel throughout the cardiac cycle in 2D cine phase-contrast Cardiovascular Magnetic Resonance (CMR) images. Three-dimensional (3D), time-resolved phase-contrast CMR with three-directional velocity encoding (4D flow CMR) permits assessment of net flow volumes and flow patterns retrospectively at any location in a time-resolved 3D volume. However, analysis of these datasets can be demanding. The aim of this study is to develop and evaluate a fully automatic method for segmentation and analysis of 4D flow CMR data of the great thoracic vessels.
  
  
  METHODS
  The proposed method utilizes atlas-based segmentation to segment the great thoracic vessels in systole, and registration between different time frames of the cardiac cycle in order to segment these vessels over time. Additionally, net flow volumes are calculated automatically at locations of interest. The method was applied on 4D flow CMR datasets obtained from 11 healthy volunteers and 10 patients with heart failure. Evaluation of the method was performed visually, and by comparison of net flow volumes in the ascending aorta obtained automatically (using the proposed method), and semi-automatically. Further evaluation was done by comparison of net flow volumes obtained automatically at different locations in the aorta, pulmonary artery, and caval veins.
  
  
  RESULTS
  Visual evaluation of the generated segmentations resulted in good outcomes for all the major vessels in all but one dataset. The comparison between automatically and semi-automatically obtained net flow volumes in the ascending aorta resulted in very high correlation (r (2)=0.926). Moreover, comparison of the net flow volumes obtained automatically in other vessel locations also produced high correlations where expected: pulmonary trunk vs. proximal ascending aorta (r (2)=0.955), pulmonary trunk vs. pulmonary branches (r (2)=0.808), and pulmonary trunk vs. caval veins (r (2)=0.906).
  
  
  CONCLUSIONS
  The proposed method allows for automatic analysis of 4D flow CMR data, including vessel segmentation, assessment of flow volumes at locations of interest, and 4D flow visualization. This constitutes an important step towards facilitating the clinical utility of 4D flow CMR.
authors.affiliation: Division of Cardiovascular Medicine, Department of Medical and Health Sciences, Linköping University, Linköping, Sweden. mariana.bustamante@liu.se.|Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden. mariana.bustamante@liu.se.
authors.forename: Mariana
authors.initials: M
authors.lastname: Bustamante
authors.affiliation: Division of Cardiovascular Medicine, Department of Medical and Health Sciences, Linköping University, Linköping, Sweden. sven.petersson@liu.se.
authors.forename: Sven
authors.initials: S
authors.lastname: Petersson
authors.affiliation: Division of Cardiovascular Medicine, Department of Medical and Health Sciences, Linköping University, Linköping, Sweden. jonatan.eriksson@liu.se.
authors.forename: Jonatan
authors.initials: J
authors.lastname: Eriksson
authors.affiliation: Division of Cardiovascular Medicine, Department of Medical and Health Sciences, Linköping University, Linköping, Sweden. urban.alehagen@liu.se.|Department of Cardiology, Department of Medical and Health Sciences, Linköping University, Linköping, Sweden. urban.alehagen@liu.se.
authors.forename: Urban
authors.initials: U
authors.lastname: Alehagen
authors.affiliation: Division of Cardiovascular Medicine, Department of Medical and Health Sciences, Linköping University, Linköping, Sweden. petter.dyverfeldt@liu.se.|Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden. petter.dyverfeldt@liu.se.
authors.forename: Petter
authors.initials: P
authors.lastname: Dyverfeldt
authors.affiliation: Division of Cardiovascular Medicine, Department of Medical and Health Sciences, Linköping University, Linköping, Sweden. carljohan.carlhall@liu.se.|Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden. carljohan.carlhall@liu.se.|Department of Clinical Physiology, Department of Medical and Health Sciences, Linköping University, Linköping, Sweden. carljohan.carlhall@liu.se.
authors.forename: Carl-Johan
authors.initials: CJ
authors.lastname: Carlhäll
authors.affiliation: Division of Cardiovascular Medicine, Department of Medical and Health Sciences, Linköping University, Linköping, Sweden. tino.ebbers@liu.se.|Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden. tino.ebbers@liu.se.|Division of Media and Information Technology, Department of Science and Technology/Swedish e-Science Research Center (SeRC), Linköping University, Linköping, Sweden. tino.ebbers@liu.se.
authors.forename: Tino
authors.initials: T
authors.lastname: Ebbers
mesh_terms: D000328:Adult; D000368:Aged; D001011:Aorta / Q000503:physiopathology*; D001331:Automation; D001783:Blood Flow Velocity; D016022:Case-Control Studies; D003287:Contrast Media; D006333:Heart Failure / Q000175:diagnosis* / Q000503:physiopathology; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D019028:Magnetic Resonance Imaging, Cine / Q000379:methods*; D008875:Middle Aged; D055420:Perfusion Imaging / Q000379:methods*; D011237:Predictive Value of Tests; D011651:Pulmonary Artery / Q000503:physiopathology*; D012039:Regional Blood Flow; D014682:Vena Cava, Inferior / Q000503:physiopathology*; D014683:Vena Cava, Superior / Q000503:physiopathology*
chemical_list: D003287:Contrast Media