TRiP98 optimize command

optimize / iterations() graceiterations(1) H2Obased CTbased  events() debug
           bio          singly         eps(1E-3) geps(2E-3) myfac(0.5) 
           phys         matchonly      doseweightfactor()  
           field(*)     nopreopt    


Handles optimization of dose with respect to particle numbers.


Maximum number of iterations. Remembered. Startup default is 5.
Number of initial "grace" iterations for which tests on relative change of chi-square are suspended. Startup default is 1.
Optimize using biological dose. Remembered.
Optimize using physical dose. Remembered.
Optimization principle.
H2Obased selects separate optimization of single fields, with fast, but less accurate algorithms, based on a H2O-equivalent grid. This is the default.
CTbased selects optimization based on the CT grid. Mandatory for simultaneous optimization of multiple fields.
Separate optimization of multiple fields, i.e. combination of two separate optimization calls into one. Note, however, that the target dose is still partitioned between the multiple fields.
Skips H2Obased pre-optimization so CTbased is invoked with zero particle number as startup value. Use it with care.
Optimize using only a simple peak-to-dose matching algorithm. Otherwise a conjugate gradient method is used to equilibrate peaks and valleys. This applies to H2Obased optimizations only.
For optimizations based on gradients of the objective function: include also contributions from raster points with zero particle count. By default, such points are ignored (except for nopreopt). As a side-effect, this permanently excludes raster positions from the optimization one their particle numbers are below the minparticle() threshold. grad4zero counteracts this, if desired.
Selects algorithm for dose calculation. Same choices as for dose cube calculation:
Selects algorithm for biological effect calculation. Note, however, that the default choice is valid only for doses up to some 10GyE, but is considerably faster.
Selects algorithm for dose optimization. The selection is affects CTbased optimizations, as of version 1001d H2Obased too. Choices are As it turns out, "Fletcher-Reeves" is usually the fastest converging one.
BFGS comes second, however, it needs considerably more RAM.
Not yet implemented.
Minimum relative change of chi-square. If the actual change is below, the iteration is aborted.
Startup default is 1E-3.
Controls relative contribution of raster points to dose voxels. Contributions less than geps are omitted, thereby reducing memory and time consumption. Should not be raised above some 1E-3, otherwise optimized and real dose distributions will differ.
Startup default is 2E-3.
Controls search stepsize for gradient based optimization algorithms for the RBE-weighted (biological) dose. The default value is 0.5, designed for total biological target doses about 3GyE. For lower doses (0.5..1GyE) or high RBE, lower values of myfac might be appropriate. There is some builtin stepsize "autopilot", but if the results aren't satisfactory, one might manually choose lower values, e.g. down to 0.1 to 0.25.
As of version 1001D, this parameter applies for H2Obased optimizations as well.
The prescribed dose is multiplied with this factor to introduce sort of an "experimental uncertainty", which enters as a weight in the chi-square evaluation. Startup default is 2.5E-2, corresponding to half of the deviation between measured and calculated absorbed dose allowed by authorities.
List of field id's to be optimized.
Debug switch. Lots of output!



  1. opt / field(*) bio
    Optimize all defined fields using the biologically effective dose. Fields are optimized separately with "classic" algorithms
  2. opt / field(*) ctbased bio dosealg(ap) optalg(fr) bioalg(ld) geps(2E-3) eps(1e-2) iter(500)
    Optimize all defined fields simultaneously (IMRT-style), with "fast" biological calculations and the Fletcher-Reeves algorithm.

Last updated:
$Id: trip98cmdopt.html,v 1.11 2012/06/01 18:48:43 kraemer Exp $