title: Automated patient-specific optimization of three-dimensional double-inversion recovery magnetic resonance imaging.
abstract: OBJECTIVE
  To automatically optimize three-dimensional double-inversion recovery (3D-DIR) MRI of the brain on a patient-by-patient basis.
  
  
  METHODS
  DIR is a powerful MRI technique that allows simultaneous suppression of white matter (WM) and cerebrospinal fluid (CSF) in brain imaging. Unfortunately, the tissue suppression is not always consistent across patients. We propose patient-specific optimization of WM suppression for improved gray matter (GM)-WM contrast. Relaxation times were measured in the same scan session, and through real time processing were used for calculating DIR inversion times for maximum tissue contrast. Signal evolution during the variable-flip-angle turbo-spin-echo readout was calculated using the extended phase graph algorithm. Patient-specific optimization was examined in five healthy volunteers and two multiple sclerosis patients. Two volunteers were scanned twice for reproducibility. The contrast ratios, GM signal-to-noise ratio (SNR), and image histogram were used to assess the performance of this patient-specific approach.
  
  
  RESULTS
  Automated optimization of 3D-DIR was successfully completed in all experiments with processing time of ∼1 min. GM-WM contrast ratio tripled with the optimized DIR sequence, with only a 19% decrease in GM-CSF contrast and 30% SNR penalty.
  
  
  CONCLUSIONS
  Patient-specific optimization is feasible and significantly improves GM-WM contrast on 3D-DIR with a moderate decrease in the GM SNR.
authors.affiliation: Department of Diagnostic and Interventional Imaging, University of Texas Health Science Center at Houston, Houston, Texas, USA.
authors.forename: Refaat E
authors.initials: RE
authors.lastname: Gabr
authors.affiliation: Department of Diagnostic and Interventional Imaging, University of Texas Health Science Center at Houston, Houston, Texas, USA.
authors.forename: Xiaojun
authors.initials: X
authors.lastname: Sun
authors.affiliation: Philips Healthcare, Cleveland, Ohio, USA.
authors.forename: Amol S
authors.initials: AS
authors.lastname: Pednekar
authors.affiliation: Department of Diagnostic and Interventional Imaging, University of Texas Health Science Center at Houston, Houston, Texas, USA.
authors.forename: Ponnada A
authors.initials: PA
authors.lastname: Narayana
mesh_terms: D000328:Adult; D000465:Algorithms; D001499:Bayes Theorem; D001921:Brain / Q000033:anatomy & histology*; D005260:Female; D064368:Healthy Volunteers; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000295:instrumentation / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000295:instrumentation / Q000379:methods*; D008297:Male; D020529:Multiple Sclerosis, Relapsing-Remitting / Q000473:pathology*; D015203:Reproducibility of Results
keywords: extended phase graph; image analysis; multiple sclerosis; patient-specific optimization; personalized imaging; relaxation time