Parameter selection in prostate IMRT Renzhi Lu, Richard J. Radke 1 , Andrew Jackson 2 Rensselaer Polytechnic Institute 1 ,Memorial Sloan-Kettering Cancer Center 2.
Renzhi Lu, Richard J. Radke1 , Andrew Jackson2
Rensselaer Polytechnic Institute1,Memorial Sloan-Kettering Cancer Center2
This work was supported in part by CenSSIS, the Center for Subsurface Sensing and Imaging Systems, under the Engineering Research Centers Program of the National Science Foundation (Award Number EEC-9986821).
Current intensity modulated radiotherapy (IMRT) planning arrives at intensity plans via optimization algorithms that make compromises between competing clinical objectives. However, such compromises are not easy to specify in terms of the parameters defining the IMRT objective function. Planners currently adjust the optimization parameters in trial-and-error way to get a clinically acceptable plan. To circumvent this procedure in prostate IMRT, we present a novel approach that can automatically produce optimization parameters that will meet the clinical objectives given a contoured CT scan of the patient. Based on training data, we identified key combinations of parameters to which the IMRT optimization is sensitive. By searching in this reduced parameter space, we find the combination of the important parameters that lead to the 'best' plan. The starting point of the search is obtained from machine learning, i.e., we learn the relationship between these parameters and the patient's geometry from training data. Our initial experiments indicate that we can automatically determine the plan parameters that satisfy the clinical constraints within 5 minutes, a task that may take experienced human planner several hours.
• Hunt et al gave specified procedure for changes to be made in optimization parameters given specific deficits in plans.
• Radke et al built active shape model and appearance model for prostate, bladder and rectum in prostate IMRT.
Challenges and significance
The bottleneck of current IMRT system is not optimization, but the back-and-forth procedure for adjusting optimization parameters. Circumventing or minimizing this procedure would save many person-hours of effort.However, high-dimensionality of parameter space and significant degeneracy make the problem difficult.
1. Problem description
Fig. 1 Prostate IMRT: visualization of beams and structures
Input : Settings for 5 beams. Contours for 6 structures.
Optimization : .Given a parameter
setting P ,find the best radiation intensities I.
Adjustment : Change the parameters P , redo optimization.
Objective : Dose D in target is as prescribed,
in normal tissues is minimized.
2. System overview
Fig. 2 Overview of our approach
3. Score function for parameter global optimization
Multiple clinical rules are incorporated into a single
score function to evaluate the plan from a given parameter set.
Rule 1: PTV max dose<111. Rule 2: Rectum max dose<99,
… Rule i: …
4. Dimensionality reduction in parameter space
We identify 6 important parameters which most affect the resultant dose evaluation. Search in this combination of sensitive set instead of the whole parameter space.
Fig. 3 Examples of sensitive set. PTV V95(left) and PTV Dmax(right) as a function of the PTV Dmax weight and Dmin weight. The relationship is also controlled by Rectum max dose, Rectum DVH dose and Bladder max dose.
5. Direct search algorithm for parameters
• Pattern search • Powell method
• Simplex search
6. Machine learning for initialization
6.1 Geometric modeling of structures:Re-sample the contours and build PCA model for joint structures.
Fig. 4 Examples of contour re-sampling and PCA modeling
6.2 Machine learning for parameter set :
Input features: Weights of PCA model for a given geometry
Output : Parameters for IMRT optimization