8/3/2019 Zakarian Deasy Prioritized Optimization Aapm 2004

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Prioritized planning example input prescription

1) Constraint: dose to spinal cord (with 3 mm margin) < 45 Gy

2) Constraint: dose to brain stem (with 3 mm margin) < 55 Gy

3) Constraint: dose to PTV < 75 Gy.

4) Priority I goal: minimize quadratic variation in dose over the target (PTV) versus prescriptiondose of 60 Gy.

5) Priority II goal: minimize mean dose to both parotid glands.

6) Priority III goal: minimize quadratic dose in the anchor zone

Examples:

Example 1. IMRT plan with 7 equally spaced 18MV beams,Start angle 0 degrees (from patients left).Total number of PBs 3456 with 0.5 x 0.5 cm2 size at isocenterStructures: Cord, CTV5940, LP (left parotid), RP (right parotid), Anchor zone.QP Moseks optimizer

Total optimization time: 3 min (5 problem statement iterations)

Example 2. IMRT plan with 10 equally space 18MV beamsStart angle degrees (from patients left).

Total number of PBs 1986 with 1 x 1 cm2 size at isocenterStructures: Cord, CTV4950, LP (left parotid), RP (right parotid), Anchor zone.QP Moseks optimizer

Total optimization time: 4 min

Heading

Abstract

IMRT treatment planning (IMRTP) typically entails trade-offs between goals associatedwith tumor control (such as a high D95) and goals associated with low normal tissuecomplication rates (such as spinal cord dose less than 45 Gy). Previously proposedIMRT algorithms attempt to control such tradeoffs either by changing numerical weightsof terms in a single objective function, or by introducing a priori inequalities on dose ordose-volume characteristics. In both cases, the user is required to provide significantquantitative information prior to running the treatment plan. Much of this data (e.g.,dose-volume constraints) is based on estimated behavior of the best plan possible. Ourpreviously discussed proposal [1] is to prioritize the planning goals according to theirclinical significance, and to iteratively re-state and solve the treatment planning problemtaking into account the next-lower priority treatment goal at each problem re-statement.At each problem re-statement iteration, the solver improves the plan with respect to the

stated goal, without violating the higher priority goals which have been converted toconstraints. This algorithm has been implemented within our treatment planningresearch system (CERR) [2] and several plans based on realistic patient data have beenproduced. We call this approach Prioritized Radiotherapy Optimization (PRO), and weconclude based on early experience that PRO is a promising approach to making IMRTPmore straightforward and responsive to the clinical goals of the treatment planners.We are developing a fast implementation of PRO within CERR, based on efficient interiorpoint linear-quadratic programming routines.

The prioritized radiotherapy optimization (PRO) process implementedin CERR

1. Create fluence matrices (beamlet dose contribution matrices) for thestructures involved in the prescription

2. Formulate the prescription vectors for each of these structure (typically,prescribed target dose or zero dose for normal structures)

3. Construct the Hessian matrix4. Set the priority constraints5. Run linear-quadratic solver6. For the next-lower priority objective, convert current priority objective as

a constraint

AcknowledgementsThis research was supported by NCI grants R29 CA85181 and R01 90445.

CERR can be downloaded from http://deasylab.info and links. CERR is free for non-commercial research use.Use of CERR for clinical patient care is prohibited.

References

1. Deasy, J., Prioritized treatment planning for radiotherapy optimization, Proceedings of Chicago 2000 WorldCongress on Medical Physics and Biomedical Engineering (proceedings on CDROM) 2000.

2. J. O. Deasy, A. I. Blanco, and V. H. Clark CERR: A Computational Environment for RadiotherapyResearch, Med Phys 30:979-985 (2003).

3. J. O. Deasy, IMRT Software Design Goals Workshop on operations research applied to radiation therapy(ORART) http://www.isye.gatech.edu/nci-nsf.orart.2002/pdf-files/deasy.pdf (2002)

Prioritized optimization for IMRT treatment planning

Konstantin Zakarian, Joseph Deasy and James Alaly

Dept. of Radiation Oncology, Washington University, St. Louis, MO

The anchor zone technique as a part of prioritizedoptimization

Theanchorzone technique is a simple method of controlling dose characteristicsnear the edge of a target volume which still allows for fast optimization. We introducean 'anchor' structure, which is a strip region which surrounds the target at a fixedmargin. The anchor zone has a finite width (typically 1-2 cm). Between the anchorzone and the target lies the transitionzone, also typically 1 to 2 cm in width. Theanchor zone is included in the objective function inside prioritized optimization as astructure which should receive low doses. It thereby serves to control the dose falloffbehavior outside and near the target. Typically, it is a low priority goal as it is meantmerely to ensure smooth and rapid dose falloff. An extra benefit is that is tends tosmooth beam weights as well.

Figure 2 Magnified (4:1) images oftransverse slice: (a) - IMRT plan with'anchor' optimization and 1 cm transitionzone; (b) - IMRT plan with 'anchor'optimization and 1.5 cm transition zone.The thinner transition zone (1 cm) is toosmall.

Figure 1 (a) transverse slice for IMRT plan with uniformbeam weights (all weights equal to 1); (b)transverse slice forIMRT plan with 'anchor' optimization and 1 cm transition zone;(c) transverse slice for IMRT plan 'anchor' optimization and1.5 cm transition zone.

CERR screen shots for prioritized optimization example 1

Prioritized optimization test for the head and heckIMRT plan with 7 equally spaced beams (example1)

DVHs: PTV red (prescription 60 Gy), spinalcord - purple, left parotid - magenta, right parotid

cyan.

CERR screen shots for Prioritized Optimization example 2

Prioritized optimization test for head and heckIMRT plan with 10 equally spaced beams(example 2)

DVHs: PTV dark green, spinalcord - red, left parotid - light green,right parotid, brown.

Two more CERR Prioritized Optimization Examples

IMRT head and neck plan for 7 equally anglespaced 18MV beams

IMRT head and neck plan for 9 equally anglespaced 18MV beams

Summary and conclusions

The formulation of IMRTP presented here has several advantages: Prioritized optimization allows physicians and other planners to state the

problem in clinically meaningful terms. Lower priority goals do not adversely affect higher priority goals. Conflicts

between goals are well-controlled. The best performance of each goal is determined before converting thatgoal to a constraint for lower priority goal problem statement iterations.Therefore, the user does not need to know the best performance of anyindividual plan metric before using it as a plan goal. In particular, prioritizedoptimization does not require guesses of what the appropriate dose-volumeconstraints may be.

Prioritized optimization allows for the use of dose-based objectives in placeof radiobiologically-based objectives, as each objective function is notcompeting with another term representing a dissimilar outcome endpoint.

The linear-quadratic formulation is an efficient way to formulate prioritizedoptimization, with single problem statement iterations requiring only 30seconds to one minute, and total problem solutions are returned in less than 5minutes.

We hypothesize that prioritized radiotherapy optimization (PRO) could enable astraightforward and nearly automated approach to IMRTP.

As the linear-quadratic solver, we used the Mosek's quadratic programming(QP) optimization toolbox to test IMRT plans on PIV 2.6 GHz. IMRT plans werecreated and calculated inside CERR. The Mosek's routines allow for linear orquadratic terms in either the objective function or the constraints. This allowsus to convert objective functions to inequality constraints based on the bestperformance of the objective function during the priority iteration.

The goal is to spare the parotid glandswithout compromising the target dosedistribution.

Note the lack of ad hoc parameters orweights.

(Deasy, 2002)