Feasibility of using respiratory correlated mega voltage cone beam computed tomography to measure tumor motion

Tumor control probability reduction in gated radiotherapy of non-small cell lung cancers: a feasibility study

We studied the feasibility of evaluating tumor control probability (TCP) reductions for tumor motion beyond planned gated radiotherapy margins. Tumor motion was determined from cone‐beam CT projections acquired for patient setup, intrafraction respiratory traces, and 4D CTs for five non‐small cell lung cancer (NSCLC) patients treated with gated radiotherapy. Tumors were subdivided into 1 mm sections whose positions and doses were determined for each beam‐on time point. (The dose calculation model was verified with motion phantom measurements.) The calculated dose distributions were used to generate the treatment TCPs for each patient. The plan TCPs were calculated from the treatment planning dose distributions. The treatment TCPs were compared to the plan TCPs for various models and parameters. Calculated doses matched phantom measurements within 0.3% for up to 3 cm of motion. TCP reductions for excess motion greater than 5 mm ranged from 1.7% to 11.9%, depending on model parameters, and were as high as 48.6% for model parameters that simulated an individual patient. Repeating the worst case motion for all fractions increased TCP reductions by a factor of 2 to 3, while hypofractionation decreased these reductions by as much as a factor of 3. Treatment motion exceeding gating margins by more than 5 mm can lead to considerable TCP reductions. Appropriate margins for excess motion are recommended, unless applying daily tumor motion verification and adjusting the gating window.

PACS numbers: 87.55.dk, 87.57.Q‐

A new method of diaphragm apex motion detection from 2D projection images of mega-voltage cone beam CT

To present a new method of estimating 3D positions of the ipsi-lateral hemi-diaphragm apex (IHDA) from 2D projection images of mega-voltage cone beam CT (MVCBCT). The detection framework reconstructs a 3D volume from all the 2D projection images. An initial estimated 3D IHDA position is determined in this volume based on an imaging processing pipeline, including Otsu thresholding, connected component labeling and template matching. This initial position is then projected onto each 2D projection image to create a region of interest (ROI). To accurately detect the IHDA position in 2D projection space, two methods, dynamic Hough transform (DHT) and a tracking approach based on a joint probability density function (PDF) are developed. Both methods utilize a double-parabola model to fit the 2D diaphragm boundary. The 3D IHDA motion in the superior-inferior (SI) direction is estimated from the initial static 3D position and the detected 2D positions in projection space. The two Hough-based detection methods are tested on 35 MVCBCT scans from 15 patients. The detection is compared to manually identified IHDA positions in 2D projection space by three clinicians. An average and standard deviation of 4.252 ± 3.354 and 2.485 ± 1.750 mm was achieved for DHT and tracking-based approaches respectively, compared with the inter-expert variance among three experts of 1.822 ± 1.106 mm. Based on the results of the scans, the PDF tracking-based approach appears more robust than the DHT. The combination of the automatic ROI localization and the tracking-based approach is a quicker and more accurate method of extracting 3D IHDA motion from 2D projection images.