Understanding the Performance of Control Limit-Based Monitoring of Respiratory Surrogate Tumor Motion Models
K Malinowski1,2, T Diwanji1, T McAvoy2, W D'Souza1,2*, (1) University of Maryland School of Medicine, Baltimore, MD, (2) University of Maryland, College Park, MDTU-G-BRA-8 Tuesday 4:30:00 PM - 6:00:00 PM Room: Ballroom A
Purpose: To develop an understanding of mechanisms underlying the performance of a control-limit-based monitoring technique for detecting errors in respiratory surrogate tumor displacement models.
Methods: Five lung cancer patients underwent 13 dynamic magnetic resonance imaging sessions on a 1.5 T scanner using a TrueFISP sequence (200 images, 5 sagittal slices, 8mm slice thickness, interleaved acquisition, TE 1.29 msec, TR 2.57 ms, 60° flip angle, matrix 176x256 matrix, in-plane spatial resolution 1.6-2.2 mm each direction, 1028 bandwidth). Tumors were localized in the images at 0.4 Hz for 500 sec. Five respiratory surrogates affixed to the abdomen were optically tracked during imaging. Surrogate-based tumor motion models were created by applying partial-least-squares regression to the first 30 sec of data. Hotelling's statistic and the input variable squared-prediction-error for each subsequent sample were compared to training data-based control limits to predict errors >3mm. The experiment was repeated in tumor motion and respiratory surrogate signal simulations that isolated measurement precision, period variations, amplitude variations, end-exhale variations, tumor drift, and gross patient motion. Sampling rates of 0.1-30 Hz (0.1-0.4 Hz for patient data) were evaluated.
Results: For patient data sampled at 0.4 Hz and 0.1 Hz and 95% sensitivity, specificities were 8% and 17%. In comprehensive simulations tuned to 95% sensitivity, specificity increased from 33% at 0.1 Hz to 69% at 30 Hz. With measurement noise or tumor drifts alone, specificities were 99-100% and sensitivities were 0-1%. Gross patient motion was detected with sensitivity of 100% and specificity of 97%. For end-exhale variations, sensitivity was 97%, and specificity was 57%. Respiratory cycle amplitude and period variations had no effect on monitoring performance.
Conclusions: Contributors to control-limit-based monitoring performance included sampling rate, end-exhale variations, measurement noise, and tumor drifts but not patient motion, period variations, or amplitude variations. Simulation results were qualitatively in agreement with patient results.