Biomedical subjects

S M Morrill

Publications and source records attributed to S M Morrill.

10 recordsLinked to original sources

PubMed2003

MLC quality assurance techniques for IMRT applications.

Intensity modulated radiotherapy (IMRT) requires extensive knowledge of multileaf collimator (MLC) leaf positioning accuracy, precision, and long-term reproducibility. We have developed a technique to efficiently measure the absolute position of each MLC leaf, over the range of leaf positions utilized in IMRT, based on dosimetric information. A single radiographic film was exposed to 6 MV x-rays for twelve exposures: one open field with a radio-opaque marker tray present, and eleven fields (1 x 28 cm strips via 1 cm gaps between opposed leaf pairs) separated by 2 cm center to center. The process was repeated while varying direction of leaf travel; each film was digitized using a commercial film dosimetry system. The digital images were manipulated to remove translation and rotation of the film data with respect to the collimator coordinate system by extraction of radiation dose profiles perpendicular to the MLC leaf motion and measuring the center of the x-ray leakage between leaves. Radiation dose profiles in the direction of leaf motion were acquired through the center of each leaf pair (leaves 2-28), which provided leaf position information every 2 cm with 0.2 mm precision. Nine separate leaf reproducibility studies over a 90 day period which evaluated 600 measurement points on each film show 0.3 mm precision for 95% confidence, while hysteresis studies show 0.5 mm precision. Absolute leaf position error measurements demonstrated a radial dependence, with a maximum of 1.5 mm at 16.4 cm from central axis, due to rotational error at calibration. Recalibration of the MLC leaves based utilizing this tool yields absolute leaf position measurements where 91.5% of all leaves/positions were within 0.5 mm, with a mean error of 0.1 mm and a maximum error less than 1.0 mm.

Equipment Design↗

PubMed2003

MLC dosimetric characteristics for small field and IMRT applications.

The objective of this work was to measure the performance characteristics of a double-focus multileaf collimator (MLC) for intensity modulated radiation therapy (IMRT), specifically the variation in penumbra and leakage for narrow fields as a function of field position over a 20x27 cm space available for segmented MLC IMRT. Measurements were made with 6 MV x rays through a MLC containing 29 leaf pairs (27 pairs of 1 cm width), and EDR2 film at 10 cm depth in solid water at 100 cm SAD. Films were digitized with 0.17 mm resolution and converted to dose. Interleaf and intraleaf transmission were measured along 11 vertical profile locations. Leaf-end transmission was measured along horizontal profiles for each of 9 different leaf abutments, traveling over a 20 cm range. In-plane penumbra measurements were made through a single leaf retracted, for 7 different leaves. Cross-plane penumbra (leaf-end) measurements were made for all 27 leaf pairs, where the 1 cm field width was placed in 11 different off-axis positions (20 cm range). Interleaf leakage (range 1.0%-1.5%), intraleaf transmission (range 0.6%-0.8%), and leaf-end transmission (range 0.8%-2.7%) were consistent for all leaf pairs at a given abutment position. The penumbra for these 1-cm-wide fields was measured to be 0.36 cm+/-0.03 cm for 99% of the measurements. In conclusion, the penumbra and leakage of the double-focus MLC were remarkably consistent for the range of leaf positions studied, producing dosimetric characteristics that are well suited for IMRT segments where opposing leaf pairs are often separated by 10 mm or less.

Equipment Failure Analysis↗

PubMed1995

Comparison of simulated annealing algorithms for conformal therapy treatment planning.

PURPOSE: The efficiency of four fast simulated annealing algorithms for optimizing conformal radiation therapy treatment plans was studied and the resulting plans were compared with each other and to optimized conventional plans. METHODS AND MATERIALS: Four algorithms were selected on the basis of their reported successes in solving other minimization problems: fast simulated annealing with a Cauchy generating function, fast simulated annealing with a Lorentzian generating function, variable step size generalized simulated annealing (VSGSA), and very fast simulated reannealing (VFSR). They were tested on six clinical cases using a multiple beam coplanar conformal treatment technique. Relative beam weights were computed that maximized the minimum tumor dose subject to dose-volume constraints on normal organ doses. Following some initial tuning of the annealing parameters, each algorithm was applied identically to each test case. Optimization tests were run using different random number sequences and different numbers of iterations. RESULTS: The VSGSA algorithm consistently produced the best results. Using long run times, it generated plans with the highest minimum tumor dose in five of the six cases. For the short run times, the VSGSA solutions averaged larger minimum tumor doses than those of the other algorithms for all six patients, with increases ranging from 0.4 to 5.9 Gy. For three of the patients, the conformal plan gave a clinically significant increase in the minimum tumor dose over the conventional plan, ranging from 8.2 to 13.0 Gy. In two other cases, there was little difference between the two treatment approaches. For one case, the optimized conventional plan was much better than the conformal plan because the conventional beam arrangement included wedges, which offset the multiple beam advantage of the conformal plans. CONCLUSIONS: For equal computing times of both long and short duration, the VSGSA algorithm consistently produced conformal plans that were superior to those produced by the other algorithms. The simple conformal technique used in this study showed a significant potential advantage in the treatment of abdominal tumors. In three of the cases, the conformal plans showed clinically important increases in tumor dose over optimized conventional plans.

Abdominal Neoplasms↗

PubMed1995

Very fast simulated reannealing in radiation therapy treatment plan optimization.

PURPOSE: Very Fast Simulated Reannealing is a relatively new (1989) and sophisticated algorithm for simulated annealing applications. It offers the advantages of annealing methods while requiring shorter execution times. The purpose of this investigation was to adapt Very Fast Simulated Reannealing to conformal treatment planning optimization. METHODS AND MATERIALS: We used Very Fast Simulated Reannealing to optimize treatments for three clinical cases with two different cost functions. The first cost function was linear (minimum target dose) with nonlinear dose-volume normal tissue constraints. The second cost function (probability of uncomplicated local control) was a weighted product of normal tissue complication probabilities and the tumor control probability. RESULTS: For the cost functions used in this study, the Very Fast Simulated Reannealing algorithm achieved results within 5-10% of the final solution (100,000 iterations) after 1000 iterations and within 3-5% of the final solution after 5000-10000 iterations. These solutions were superior to those produced by a conventional treatment plan based on an analysis of the resulting dose-volume histograms. However, this technique is a stochastic method and results vary in a statistical manner. Successive solutions may differ by up to 10%. CONCLUSION: Very Fast Simulated Reannealing, with modifications, is suitable for radiation therapy treatment planning optimization. It produced results within 3-10% of the optimal solution, produced using another optimization algorithm (Mixed Integer Programming), in clinically useful execution times.

Algorithms↗

PubMed1994

Tissue heterogeneity effects in treatment plan optimization.

PURPOSE: There is general agreement that tissue density correction factors improve the accuracy of dose calculations. However, there is disagreement over the proper heterogeneity correction algorithm and a lack of clinical experience in using them. Therefore, there has not been widespread implementation of density correction factors into clinical practice. Furthermore, the introduction of optimized conformal therapy leads to new and radically different treatment techniques outside the clinical experience of the physician. It is essential that the effects of tissue density corrections are understood so that these types of treatments can be safely delivered. METHODS AND MATERIALS: In this paper, we investigate the effect of tissue density corrections on optimized conformal type treatment planning in the thorax region. Specifically, we study the effects on treatment plans optimized without type treatment planning in the thorax region. Specifically, we study the effects on treatment plans optimized without tissue density corrections, when those corrections are applied to the resulting dose distributions. These effects are compared for two different conformal techniques. RESULTS: This study indicates that failure to include tissue density correction factors results in an increased dose of approximately 5-15%. This is consistent with published studies using conventional treatment techniques. Additionally, the high-dose region of the dose distribution expands laterally into the uninvolved lung and other normal structures. The use of dose-volume histograms to compare these distributions demonstrates that treatment plans optimized without tissue density corrections lead to an increased dose to uninvolved normal structures. This increase in dose often violates the constraints used to determine the optimal solution. CONCLUSIONS: The neglect of tissue density correction factors can result in a 5-15% increase in the delivered dose. In addition, suboptimal dose distributions are produced. To benefit from the advantages of optimized conformal therapy in the thorax, tissue density correction factors should be used.

Carcinoma↗

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↗