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Automatic Quantitative Decision Making Metric for 4DCT Image Quality

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J Blanco Kiely

J Blanco Kiely1*, A Olszanski1 , S Both1 , D Low2 , B White1 , (1) University of Pennsylvania, Philadelphia, PA, (2) Deparment of Radiation Oncology, University of California Los Angeles, Los Angeles, CA


SU-E-J-155 (Sunday, July 12, 2015) 3:00 PM - 6:00 PM Room: Exhibit Hall

Purpose: To develop a quantitative decision making metric for automatically detecting irregular breathing using a large patient population that received phase-sorted 4DCT.

Methods: This study employed two patient cohorts. Cohort#1 contained 256 patients who received a phase-sorted 4DCT. Cohort#2 contained 86 patients who received three weekly phase-sorted 4DCT scans. A previously published technique used a single abdominal surrogate to calculate the ratio of extreme inhalation tidal volume to normal inhalation tidal volume, referred to as the κ metric. Since a single surrogate is standard for phase-sorted 4DCT in radiation oncology clinical practice, tidal volume was not quantified. Without tidal volume, the absolute κ metric could not be determined, so a relative κ (κrel) metric was defined based on the measured surrogate amplitude instead of tidal volume. Receiver operator characteristic (ROC) curves were used to quantitatively determine the optimal cutoff value (jk) and efficiency cutoff value (τk) of κrel to automatically identify irregular breathing that would reduce the image quality of phase-sorted 4DCT. Discriminatory accuracy (area under the ROC curve) of κrel was calculated by a trapezoidal numeric integration technique.

Results: The discriminatory accuracy of κrel was found to be 0.746. The key values of jk and τk were calculated to be 1.45 and 1.72 respectively. For values of κrel such that jk≤κrel≤τk, the decision to reacquire the 4DCT would be at the discretion of the physician. This accounted for only 11.9% of the patients in this study. The magnitude of κrel held consistent over 3 weeks for 73% of the patients in cohort#3.

Conclusion: The decision making metric, κrel, was shown to be an accurate classifier of irregular breathing patients in a large patient population. This work provided an automatic quantitative decision making metric to quickly and accurately assess the extent to which irregular breathing is occurring during phase-sorted 4DCT.

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