Command-Line Usage

General structure

The models are objects, each with a set of properties and generic functions. Before interacting with a model, it must first be instantiated, e.g.


General commands

  • qMRInfo(model) : Print the help for the ‘model’ object
  • qMRusage(model) : Print the methods of ‘model’ and examples of how to interact with them
  • qMRgenBatch(model) : Generate a batch example script for ‘model’ (will automatically download test data)

Model structure

All models have the following properties:

  • MRIinputs : Names of input data
  • voxelwise : Whether the fit is voxelwise [1] or a matrix operation [0]
  • xnames : Names of output data
  • Prot : structure containing the protocol parameters
  • buttons :
  • Options : options specific to the model (e.g. linear or non-linear fir for VFA-T1)

And functions:

  • equation:

    Compute MR signal
      Smodel = Model.equation(x)
      x: [struct] OR [vector] containing Model output parameters
  • fit:

    Fit experimental data
      FitResults =
      data: [struct] containing input data IN ORDER as in MRinuts
    NOTE: data are 1D. For 4D datasets use FitData(data,Model)
  • plotModel:

    Plot model equation (and fitting)
           Model.plotModel(obj, x)
           Model.plotModel(obj, x, data)
      x: [struct] OR [vector] containing Model output parameters
      data: [struct] containing input data in ORDER as in MRinuts

Most models have these additional functions:

  • Sim_Sensitivity_Analysis:

    Simulates sensitivity to fitted parameters:
       (1) vary fitting parameters from lower (lb) to upper (ub) bound in 10 steps
       (2) run Sim_Single_Voxel_Curve Nofruns times
       (3) Compute mean and std across runs
      SimVaryResults = Model.Sim_Sensitivity_Analysis(OptTable, Opt);
      OptTable: [struct] nominal value and range for each parameter.
         st: [vector] nominal values for output parameters
         fx: [binary vector] do not vary this parameter?
         lb: [vector] vary from lb...
         ub: [vector] up to ub
      Opt: [struct] Options of the simulation
  • Sim_Single_Voxel_Curve:

    Simulates Single Voxel curves:
       (1) use equation to generate synthetic MRI data
       (2) add rician noise
       (3) fit and plot curve
      FitResults = Model.Sim_Single_Voxel_Curve(x)
      FitResults = Model.Sim_Single_Voxel_Curve(x, Opt,display)
      x: [struct] OR [vector] containing fit results
      display: [binary] 1=display, 0=nodisplay

Please type the following to see the specific usage of the model you are interested in


Or the batch example associated with your model located here Methods available