Petr Jordan

SU-E-J-208: Fast and Accurate Auto-Segmentation of Abdominal Organs at Risk for Online Adaptive Radiotherapy

PURPOSE Various studies have demonstrated that online adaptive radiotherapy by real-time re-optimization of the treatment plan can improve organs-at-risk (OARs) sparing in the abdominal region. Its clinical implementation, however, requires fast and accurate auto-segmentation of OARs in CT scans acquired just before each treatment fraction. Autosegmentation is particularly challenging in the abdominal region due to the frequently observed large deformations. We present a clinical validation of a new auto-segmentation method that uses fully automated non-rigid registration for propagating abdominal OAR contours from planning to daily treatment CT scans. METHODS OARs were manually contoured by an expert panel to obtain ground truth contours for repeat CT scans (3 per patient) of 10 patients. For the non-rigid alignment, we used a new non-rigid registration method that estimates the deformation field by optimizing local normalized correlation coefficient with smoothness regularization. This field was used to propagate planning contours to repeat CTs. To quantify the performance of the auto-segmentation, we compared the propagated and ground truth contours using two widely used metrics- Dice coefficient (Dc) and Hausdorff distance (Hd). The proposed method was benchmarked against translation and rigid alignment based auto-segmentation. RESULTS For all organs, the auto-segmentation performed better than the baseline (translation) with an average processing time of 15 s per fraction CT. The overall improvements ranged from 2% (heart) to 32% (pancreas) in Dc, and 27% (heart) to 62% (spinal cord) in Hd. For liver, kidneys, gall bladder, stomach, spinal cord and heart, Dc above 0.85 was achieved. Duodenum and pancreas were the most challenging organs with both showing relatively larger spreads and medians of 0.79 and 2.1 mm for Dc and Hd, respectively. CONCLUSION Based on the achieved accuracy and computational time we conclude that the investigated auto-segmentation method overcomes an important hurdle to the clinical implementation of online adaptive radiotherapy. Partial funding for this work was provided by Accuray Incorporated as part of a research collaboration with Erasmus MC Cancer Institute.

SU-GG-J-24: Retrospective Clinical Data Analysis of Fiducial-Free Lung Tracking

Purpose: To present the algorithmic approach used in the Xsight® LungTracking System (XLT; Accuray Incorporated, Sunnyvale, CA) of the CyberKnife® RoboticRadiosurgery System (Accuray Incorporated) and to quantify the expected proportion of lungradiosurgery candidates suitable for fiducial‐free motion‐compensated treatment using orthogonal kV imaging.Method and Materials: The XLT system was recently enhanced with the goal of increasing the proportion of lung lesions that can be tracked in orthogonal kV x‐ray image pairs without fiducials. These enhancements include digitally reconstructedradiographs generated from local tumor neighborhoods, an automatic preferred projection epipolar constraint, tumor template matching allowing for in‐plane rotations, and automatic x‐ray image enhancement. An extensive multi‐institutional (M=5) cohort of patients (N=103) was retrospectively analyzed to quantify fiducial‐free target localization performance for lungtumors spanning a broad range of anatomical locations and sizes (largest dimension ranged from 10 to 100 mm). This analysis evaluated 7,565 x‐ray image pairs to quantify the localization performance of the XLT system in comparison to fiducial localization as a gold standard. Clinical cases were categorized as “fiducial‐free candidates” when XLT localization satisfied preset accuracy, quality assurance and detection confidence metrics in over 75% of the tested image pairs. Results: A total of 57 out of 103 cases (55.3%) were found to be suitable candidates for fiducial‐free treatment. The site‐specific fiducial‐free candidate ratios ranged from 40% to 78% of all lungradiosurgery candidates, reflecting variability in patient population, tumor locations and sizes (algorithm works best for tumors larger than 15 mm in diameter), and x‐ray imaging technique. Conclusion: This study demonstrates that fiducial‐free localization and motion compensation can be achieved in over half of lungradiosurgery candidates using the recently enhanced XLT system, while maintaining tracking accuracy comparable to that obtained using implanted fiducial markers. Conflict of Interest: The authors are employed by Accuray Incorporated.

Fast and robust adaptation of organs-at-risk delineations from planning scans to match daily anatomy in pre-treatment scans for online-adaptive radiotherapy of abdominal tumors

PURPOSE To validate a novel deformable image registration (DIR) method for online adaptation of planning organ-at-risk (OAR) delineations to match daily anatomy during hypo-fractionated RT of abdominal tumors. MATERIALS AND METHODS For 20 liver cancer patients, planning OAR delineations were adapted to daily anatomy using the DIR on corresponding repeat CTs. The DIRs accuracy was evaluated for the entire cohort by comparing adapted and expert-drawn OAR delineations using geometric (Dice Similarity Coefficient (DSC), Modified Hausdorff Distance (MHD) and Mean Surface Error (MSE)) and dosimetric (Dmax and Dmean) measures. RESULTS For all OARs, DIR achieved average DSC, MHD and MSE of 86%, 2.1 mm, and 1.7 mm, respectively, within 20 s for each repeat CT. Compared to the baseline (translations), the average improvements ranged from 2% (in heart) to 24% (in spinal cord) in DSC, and 25% (in heart) to 44% (in right kidney) in MHD and MSE. Furthermore, differences in dose statistics (Dmax, Dmean and D2%) using delineations from an expert and the proposed DIR were found to be statistically insignificant (p > 0.01). CONCLUSION The validated DIR showed potential for online-adaptive radiotherapy of abdominal tumors as it achieved considerably high geometric and dosimetric correspondences with the expert-drawn OAR delineations, albeit in a fraction of time required by experts.

Feasibility of real‐time motion management with helical tomotherapy

PURPOSE This study investigates the potential application of image-based motion tracking and real-time motion correction to a helical tomotherapy system. METHODS A kV x-ray imaging system was added to a helical tomotherapy system, mounted 90 degrees offset from the MV treatment beam, and an optical camera system was mounted above the foot of the couch. This experimental system tracks target motion by acquiring an x-ray image every few seconds during gantry rotation. For respiratory (periodic) motion, software correlates internal target positions visible in the x-ray images with marker positions detected continuously by the camera, and generates an internal-external correlation model to continuously determine the target position in three-dimensions (3D). Motion correction is performed by continuously updating jaw positions and MLC leaf patterns to reshape (effectively re-pointing) the treatment beam to follow the 3D target motion. For motion due to processes other than respiration (e.g., digestion), no correlation model is used - instead, target tracking is achieved with the periodically acquired x-ray images, without correlating with a continuous camera signal. RESULTS The systems ability to correct for respiratory motion was demonstrated using a helical treatment plan delivered to a small (10 mm diameter) target. The phantom was moved following a breathing trace with an amplitude of 15 mm. Film measurements of delivered dose without motion, with motion, and with motion correction were acquired. Without motion correction, dose differences within the target of up to 30% were observed. With motion correction enabled, dose differences in the moving target were less than 2%. Nonrespiratory system performance was demonstrated using a helical treatment plan for a 55 mm diameter target following a prostate motion trace with up to 14 mm of motion. Without motion correction, dose differences up to 16% and shifts of greater than 5 mm were observed. Motion correction reduced these to less than a 6% dose difference and shifts of less than 2 mm. CONCLUSIONS Real-time motion tracking and correction is technically feasible on a helical tomotherapy system. In one experiment, dose differences due to respiratory motion were greatly reduced. Dose differences due to nonrespiratory motion were also reduced, although not as much as in the respiratory case due to less frequent tracking updates. In both cases, beam-on time was not increased by motion correction, since the system tracks and corrects for motion simultaneously with treatment delivery.