The design and evaluation of a 2D dose verification system for intensity modulated radiotherapy

The usage of intensity modulated radiotherapy (IMRT) treatments necessitates a significant amount of patient-specific quality assurance (QA). This research has investigated the precision and accuracy of Kodak EDR2 film measurements for IMRT verifications, the use of comparisons between 2D dose calculations and measurements to improve treatment plan beam models, and the dosimetric impact of delivery errors. New measurement techniques and software were developed and used clinically at M. D. Anderson Cancer Center. The software implemented two new dose comparison parameters, the 2D normalized agreement test (NAT) and the scalar NAT index. A single-film calibration technique using multileaf collimator (MLC) delivery was developed. EDR2 film's optical density response was found to be sensitive to several factors: radiation time, length of time between exposure and processing, and phantom material. Precision of EDR2 film measurements was found to be better than 1%. For IMRT verification, EDR2 film measurements agreed with ion chamber results to 2%/2mm accuracy for single-beam fluence map verifications and to 5%/2mm for transverse plane measurements of complete plan dose distributions. The same system was used to quantitatively optimize the radiation field offset and MLC transmission beam modeling parameters for Varian MLCs. While scalar dose comparison metrics can work well for optimization purposes, the influence of external parameters on the dose discrepancies must be minimized. The ability of 2D verifications to detect delivery errors was tested with simulated data. The dosimetric characteristics of delivery errors were compared to patient-specific clinical IMRT verifications. For the clinical verifications, the NAT index and percent of pixels failing the gamma index were exponentially distributed and dependent upon the measurement phantom but not the treatment site. Delivery errors affecting all beams in the treatment plan were flagged by the NAT index, although delivery errors impacting only one beam could not be differentiated from routine clinical verification discrepancies. Clinical use of this system will flag outliers, allow physicists to examine their causes, and perhaps improve the level of agreement between radiation dose distribution measurements and calculations. The principles used to design and evaluate this system are extensible to future multidimensional dose measurements and comparisons. ^

Estimation of the incidence of a rare genetic disease through a two-tier mutation survey.

Recent attempts to detect mutations involving single base changes or small deletions that are specific to genetic diseases provide an opportunity to develop a two-tier mutation-screening program through which incidence of rare genetic disorders and gene carriers may be precisely estimated. A two-tier survey consists of mutation screening in a sample of patients with specific genetic disorders and in a second sample of newborns from the same population in which mutation frequency is evaluated. We provide the statistical basis for evaluating the incidence of affected and gene carriers in such two-tier mutation-screening surveys, from which the precision of the estimates is derived. Sample-size requirements of such two-tier mutation-screening surveys are evaluated. Considering examples of cystic fibrosis (CF) and medium-chain acyl-CoA dehydrogenase deficiency (MCAD), the two most frequent autosomal recessive disease in Caucasian populations and the two most frequent mutations (delta F508 and G985) that occur on these disease allele-bearing chromosomes, we show that, with 50-100 patients and a 20-fold larger sample of newborns screened for these mutations, the incidence of such diseases and their gene carriers in a population may be quite reliably estimated. The theory developed here is also applicable to rare autosomal dominant diseases for which disease-specific mutations are found.

Development of automated image analysis tools for verification of radiotherapy field accuracy with an electronic portal imaging device

The successful management of cancer with radiation relies on the accurate deposition of a prescribed dose to a prescribed anatomical volume within the patient. Treatment set-up errors are inevitable because the alignment of field shaping devices with the patient must be repeated daily up to eighty times during the course of a fractionated radiotherapy treatment. With the invention of electronic portal imaging devices (EPIDs), patient's portal images can be visualized daily in real-time after only a small fraction of the radiation dose has been delivered to each treatment field. However, the accuracy of human visual evaluation of low-contrast portal images has been found to be inadequate. The goal of this research is to develop automated image analysis tools to detect both treatment field shape errors and patient anatomy placement errors with an EPID. A moments method has been developed to align treatment field images to compensate for lack of repositioning precision of the image detector. A figure of merit has also been established to verify the shape and rotation of the treatment fields. Following proper alignment of treatment field boundaries, a cross-correlation method has been developed to detect shifts of the patient's anatomy relative to the treatment field boundary. Phantom studies showed that the moments method aligned the radiation fields to within 0.5mm of translation and 0.5$\sp\circ$ of rotation and that the cross-correlation method aligned anatomical structures inside the radiation field to within 1 mm of translation and 1$\sp\circ$ of rotation. A new procedure of generating and using digitally reconstructed radiographs (DRRs) at megavoltage energies as reference images was also investigated. The procedure allowed a direct comparison between a designed treatment portal and the actual patient setup positions detected by an EPID. Phantom studies confirmed the feasibility of the methodology. Both the moments method and the cross-correlation technique were implemented within an experimental radiotherapy picture archival and communication system (RT-PACS) and were used clinically to evaluate the setup variability of two groups of cancer patients treated with and without an alpha-cradle immobilization aid. The tools developed in this project have proven to be very effective and have played an important role in detecting patient alignment errors and field-shape errors in treatment fields formed by a multileaf collimator (MLC). ^

Estimating the mean and standard deviation of concentration distributions using censored samples

Environmental data sets of pollutant concentrations in air, water, and soil frequently include unquantified sample values reported only as being below the analytical method detection limit. These values, referred to as censored values, should be considered in the estimation of distribution parameters as each represents some value of pollutant concentration between zero and the detection limit. Most of the currently accepted methods for estimating the population parameters of environmental data sets containing censored values rely upon the assumption of an underlying normal (or transformed normal) distribution. This assumption can result in unacceptable levels of error in parameter estimation due to the unbounded left tail of the normal distribution. With the beta distribution, which is bounded by the same range of a distribution of concentrations, $\rm\lbrack0\le x\le1\rbrack,$ parameter estimation errors resulting from improper distribution bounds are avoided. This work developed a method that uses the beta distribution to estimate population parameters from censored environmental data sets and evaluated its performance in comparison to currently accepted methods that rely upon an underlying normal (or transformed normal) distribution. Data sets were generated assuming typical values encountered in environmental pollutant evaluation for mean, standard deviation, and number of variates. For each set of model values, data sets were generated assuming that the data was distributed either normally, lognormally, or according to a beta distribution. For varying levels of censoring, two established methods of parameter estimation, regression on normal ordered statistics, and regression on lognormal ordered statistics, were used to estimate the known mean and standard deviation of each data set. The method developed for this study, employing a beta distribution assumption, was also used to estimate parameters and the relative accuracy of all three methods were compared. For data sets of all three distribution types, and for censoring levels up to 50%, the performance of the new method equaled, if not exceeded, the performance of the two established methods. Because of its robustness in parameter estimation regardless of distribution type or censoring level, the method employing the beta distribution should be considered for full development in estimating parameters for censored environmental data sets. ^

Investigation of arterial spin labeling MRI for quantitative cerebral blood flow measurement

Arterial spin labeling (ASL) is a technique for noninvasively measuring cerebral perfusion using magnetic resonance imaging. Clinical applications of ASL include functional activation studies, evaluation of the effect of pharmaceuticals on perfusion, and assessment of cerebrovascular disease, stroke, and brain tumor. The use of ASL in the clinic has been limited by poor image quality when large anatomic coverage is required and the time required for data acquisition and processing. This research sought to address these difficulties by optimizing the ASL acquisition and processing schemes. To improve data acquisition, optimal acquisition parameters were determined through simulations, phantom studies and in vivo measurements. The scan time for ASL data acquisition was limited to fifteen minutes to reduce potential subject motion. A processing scheme was implemented that rapidly produced regional cerebral blood flow (rCBF) maps with minimal user input. To provide a measure of the precision of the rCBF values produced by ASL, bootstrap analysis was performed on a representative data set. The bootstrap analysis of single gray and white matter voxels yielded a coefficient of variation of 6.7% and 29% respectively, implying that the calculated rCBF value is far more precise for gray matter than white matter. Additionally, bootstrap analysis was performed to investigate the sensitivity of the rCBF data to the input parameters and provide a quantitative comparison of several existing perfusion models. This study guided the selection of the optimum perfusion quantification model for further experiments. The optimized ASL acquisition and processing schemes were evaluated with two ASL acquisitions on each of five normal subjects. The gray-to-white matter rCBF ratios for nine of the ten acquisitions were within ±10% of 2.6 and none were statistically different from 2.6, the typical ratio produced by a variety of quantitative perfusion techniques. Overall, this work produced an ASL data acquisition and processing technique for quantitative perfusion and functional activation studies, while revealing the limitations of the technique through bootstrap analysis. ^

Assessment of the performance of 3m 3500 organic vapor monitors over extended sampling durations

The purpose of this study was to assess the accuracy and precision of airborne volatile organic compound (VOC) concentrations measured using passive air samplers (3M 3500 organic vapor monitors) over extended sampling durations (9 and 15 days). A total of forty-five organic vapor monitor samples were collected at a State of Texas air monitoring site during two different sampling periods (July/August and November 2008). The results of this study indicate that for most of the tested compounds, there was no significant difference between long-term (9 or 15 days) sample concentrations and the means of parallel consecutive short-term (3 days) sample concentrations. Biases of 9 or 15-day measurements vs. consecutive 3-day measurements showed considerable variability. Those compounds that had percent bias values of <10% are suggested as acceptable for long-term sampling (9 and 15 days). Of the twenty-one compounds examined, 10 compounds are classified as acceptable for long-term sampling; these include m,p-xylene, 1,2,4-trimethylbenzene, n-hexane, ethylbenzene, benzene, toluene, o-xylene, d-limonene, dimethylpentane and methyl tertbutyl ether. The ratio of sampling procedure variability relative to variability within days was approximately 1.89 for both sampling periods for the 3-day vs. 9-day comparisons and approximately 2.19 for both sampling periods for the 3-day vs. 15-day comparisons. Considerably higher concentrations of most VOCs were measured during the November sampling period compared to the July/August period. These differences may be a result of varying meteorological conditions during these two time periods, e.g., the differences in wind direction, and wind speed. Further studies are suggested to further evaluate the accuracy and precision of 3M 3500 organic vapor monitors over extended sampling durations. ^