Biomedical subjects

S M Morrill

Publications and source records attributed to S M Morrill.

5 recordsLinked to original sources

PubMed1991

Treatment planning optimization using constrained simulated annealing.

A variation of simulated annealing optimization called 'constrained simulated annealing' is used with a simple annealing schedule to optimize beam weights and angles in radiation therapy treatment planning. Constrained simulated annealing is demonstrated using two contrasting objective functions which incorporate both biological response and dose-volume considerations. The first objective function maximizes the probability of a complication-free treatment (PCFT) by minimizing the normal tissue complications subject to the constraint that the entire target volume receives a prescribed minimum turmourcidal dose with a specified dose homogeneity. Probabilities of normal tissue complication are based on published normal tissue complication probability functions and computed from dose-volume histograms. The second objective function maximizes the isocentre dose subject to a set of customized normal tissue dose-volume and target volume dose homogeneity constraints (MVDL). Although the PCFT objective function gives consistently lower estimates of normal tissue complication probabilities, the ability to specify individualized dose-volume limits, and therefore the individualized probability of complication, for an individual organ makes the MDVL objective function more useful for treatment planning.

Esophageal Neoplasms↗

PubMed1990

Constrained simulated annealing for optimized radiation therapy treatment planning.

A variation of simulated annealing optimization called 'constrained simulated annealing' is used with a simple annealing schedule to automatically optimize beam weights and beam angles in radiation therapy treatment planning. This optimization technique permits the straightforward utilization of any objective function and any set of dose constraints, even those described by non-analytic functions. Constrained simulated annealing is demonstrated using an objective function which minimizes the probability of normal tissue complications subject to the constraint that the entire target volume receive a tumoricidal dose within specified maximum and minimum limits. Target volume dose constraints are determined by points located on the perimeter of the target volume. Probabilities of normal tissue complications are based on published normal tissue complication probability functions and computed from dose-volume histograms calculated on points spread throughout the normal anatomy.

Algorithms↗

PubMed1990

The influence of dose constraint point placement on optimized radiation therapy treatment planning.

To efficiently use linear and quadratic programming for treatment planning optimization on a routine basis, automated methods are needed for placing dose constraint points. We have investigated, for linear programming optimization, the minimum number of constraint points needed to achieve an acceptable approximation to the desired (ideal) solution. Seven different constraint point placement algorithms were evaluated for a given objective function. One of these algorithms was chosen for routine clinical use at our institution. This algorithm places constraint points on the perimeter of the target volume and on the perimeter and in the interior of each normal structure. Additional points are placed on the perimeter of a constant thickness buffer region surrounding the target volume. Excellent optimization results are obtained with 40-70 constraint points per treatment planning slice.

Computer Simulation↗

PubMedDate not supplied

Treatment plan optimization using linear programming.

Linear programming is a versatile mathematical tool for optimizing radiation therapy treatment plans. For planning purposes, dose constraint points, possible treatment beams, and an objective function are defined. Dose constraint points are specified in and about the target volume and normal structures with minimum and maximum dose values assigned to each point. A linear objective function is designed that defines the goal of optimization. A list of potential treatment beams is defined by energy, angle, and wedge selection. Then, linear programming calculates the relative weights of all the potential beams such that the objective function is optimized and doses to all constraint points are within the prescribed limits. Historically, linear programming has been used to improve conventional treatment techniques. It can also be used to create sophisticated, complex treatment plans suitable for delivery by computer-controlled therapy techniques.

Humans↗

PubMedDate not supplied

Optimized dynamic rotation with wedges.

Dynamic rotation is a computer-controlled therapy technique utilizing an automated multileaf collimator in which the radiation beam shape changes dynamically as the treatment machine rotates about the patient so that at each instant the beam shape matches the projected shape of the target volume. In simple dynamic rotation, the dose rate remains constant during rotation. For optimized dynamic rotation, the dose rate is varied as a function of gantry angle. Optimum dose rate at each gantry angle is computed by linear programming. Wedges can be included in the optimized dynamic rotation therapy by using additional rotations. Simple and optimized dynamic rotation treatment plans, with and without wedges, for a pancreatic tumor have been compared using optimization cost function values, normal tissue complication probabilities, and positive difference statistic values. For planning purposes, a continuous rotation is approximated by static beams at a number of gantry angles equally spaced about the patient. In theory, the quality of optimized treatment planning solutions should improve as the number of static beams increases. The addition of wedges should further improve dose distributions. For the case studied, no significant improvements were seen for more than 36 beam angles. Open and wedged optimized dynamic rotations were better than simple dynamic rotation, but wedged optimized dynamic rotation showed no definitive improvement over open beam optimized dynamic rotation.

Humans↗