Verification of helical tomotherapy delivery using autoassociative kernel regressiona)

Authors

  • Seibert Rebecca M.,

    1. Department of Nuclear Engineering, The University of Tennessee, Knoxville, Tennessee 37996 and Department of Radiation Oncology, Thompson Cancer Survival Center, Knoxville, Tennessee 37916
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    • c)

      Author to whom correspondence should be addressed. Electronic mail: rseiber1@utk.edu

  • Ramsey Chester R.,

    1. Department of Nuclear Engineering, The University of Tennessee, Knoxville, Tennessee 37996 and Department of Radiation Oncology, Thompson Cancer Survival Center, Knoxville, Tennessee 37916
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  • Garvey Dustin R.,

    1. Department of Nuclear Engineering, The University of Tennessee, Knoxville, Tennessee 37996
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  • Hines J. Wesley,

    1. Department of Nuclear Engineering, The University of Tennessee, Knoxville, Tennessee 37996
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  • Robison Ben H.,

    1. Department of Nuclear Engineering, The University of Tennessee, Knoxville, Tennessee 37996 and Department of Radiation Oncology, Thompson Cancer Survival Center, Knoxville, Tennessee 37916
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  • Outten Samuel S.

    1. Department of Radiation Oncology, Thompson Cancer Survival Center, Knoxville, Tennessee 37916
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  • a)

    This work was presented at the 48th Annual meeting of the American Association of Physicists in Medicine.

Abstract

Quality assurance (QA) is a topic of major concern in the field of intensity modulated radiation therapy (IMRT). The standard of practice for IMRT is to perform QA testing for individual patients to verify that the dose distribution will be delivered to the patient. The purpose of this study was to develop a new technique that could eventually be used to automatically evaluate helical tomotherapy treatments during delivery using exit detector data. This technique uses an autoassociative kernel regression (AAKR) model to detect errors in tomotherapy delivery. AAKR is a novel nonparametric model that is known to predict a group of correct sensor values when supplied a group of sensor values that is usually corrupted or contains faults such as machine failure. This modeling scheme is especially suited for the problem of monitoring the fluence values found in the exit detector data because it is able to learn the complex detector data relationships. This scheme still applies when detector data are summed over many frames with a low temporal resolution and a variable beam attenuation resulting from patient movement. Delivery sequences from three archived patients (prostate, lung, and head and neck) were used in this study. Each delivery sequence was modified by reducing the opening time for random individual multileaf collimator (MLC) leaves by random amounts. The error and error-free treatments were delivered with different phantoms in the path of the beam. Multiple autoassociative kernel regression (AAKR) models were developed and tested by the investigators using combinations of the stored exit detector data sets from each delivery. The models proved robust and were able to predict the correct or error-free values for a projection, which had a single MLC leaf decrease its opening time by less than 10msec. The model also was able to determine machine output errors. The average uncertainty value for the unfaulted projections ranged from 0.4% to 1.8% of the detector signal. The low model uncertainty indicates that the AAKR model is extremely accurate in its predictions and also suggests that the model may be able to detect errors that cause the fluence to change by less than 2%. However, additional evaluation of the AAKR technique is needed to determine the minimum detectable error threshold from the compressed helical tomotherapy detector data. Further research also needs to explore applying this technique to electronic portal imaging detector data.

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