title: Ultrafast compartmentalized relaxation time mapping with linear algebraic modeling.
abstract: OBJECTIVE
  To dramatically accelerate compartmental-average longitudinal (T1) and transverse (T2) relaxation measurements using the minimal-acquisition linear algebraic modeling (SLAM) method, and to validate it in phantoms and humans.
  
  
  METHODS
  Relaxation times were imaged at 3 Tesla in phantoms, in the abdomens of six volunteers, and in six brain tumor patients using standard inversion recovery and multi-spin-echo sequences. k-space was fully sampled to provide reference T1and T2measurements, and SLAM was performed using a limited set of phase encodes from central k-space. Anatomical compartments were segmented on scout images post-acquisition, and SLAM reconstruction was implemented using two algorithms. Compartment-average T1and T2measurements were determined retroactively from fully sampled data sets, and proactively from SLAM data sets at acceleration factors of up to 16. Values were compared with reference measurements. The compartment's localization properties were analyzed using the discrete spatial response function.
  
  
  RESULTS
  At 16-fold acceleration, compartment-average SLAM T1measurements agreed with the full k-space compartment-average results to within 0.0% ± 0.7%, 1.4% ± 3.4%, and 0.5% ± 2.9% for phantom, abdominal, and brain T1measurements, respectively. The corresponding T2measurements agreed within 0.2% ± 1.9%, 0.9% ± 7.9%, and 0.4% ± 5.8%, respectively.
  
  
  CONCLUSIONS
  SLAM can dramatically accelerate relaxation time measurements when compartmental or lesion-average values can suffice, or when standard relaxometry is precluded by scan-time limitations. Magn Reson Med 79:286-297, 2018. © 2017 International Society for Magnetic Resonance in Medicine.
authors.affiliation: Division of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, Maryland, USA.
authors.forename: Yi
authors.identifier: http://orcid.org/0000-0001-8738-1851
authors.initials: Y
authors.lastname: Zhang
authors.affiliation: Division of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, Maryland, USA.|Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, Maryland, USA.
authors.forename: Xiaoyang
authors.initials: X
authors.lastname: Liu
authors.affiliation: Division of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, Maryland, USA.|F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA.
authors.forename: Jinyuan
authors.initials: J
authors.lastname: Zhou
authors.affiliation: Division of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, Maryland, USA.
authors.forename: Paul A
authors.initials: PA
authors.lastname: Bottomley
mesh_terms: D000005:Abdomen / Q000000981:diagnostic imaging; D000054:Acceleration; D000328:Adult; D000368:Aged; D000465:Algorithms; D001921:Brain / Q000000981:diagnostic imaging*; D001932:Brain Neoplasms / Q000000981:diagnostic imaging*; D003287:Contrast Media; D064368:Healthy Volunteers; D006801:Humans; D007091:Image Processing, Computer-Assisted; D016014:Linear Models; D008279:Magnetic Resonance Imaging; D008297:Male; D008875:Middle Aged; D019047:Phantoms, Imaging; D015203:Reproducibility of Results
chemical_list: D003287:Contrast Media
keywords: abdomen, brain tumors; discrete spatial response function (dSRF); fast imaging; relaxation times (T1 and T2); spectroscopy with linear algebraic modeling (SLAM)