ESTRO 36 Abstract Book

S487 ESTRO 36 2017 _______________________________________________________________________________________________

adjacent contours, 3) conformity index (CI) between adjacent contours, 4) presence of air or bone across the line of the contour, 5) presence of air or bone within 5 mm outside of the contour boundary, and 6) presence of spacing > 20 mm between adjacent contour points. The threshold values for the metrics 1-3 were calculated from the rectum contours drawn by oncology experts on 315 pelvic kVCT scans, where we used 6 mm superior- inferior contour spacing to match the slice spacing of the IG scans. The settings for metrics 4-6 were determined empirically. Our software developed in Python 2.7 analysed the DICOM RTSTRUCT and IG scan data, applied the metrics and recorded the evaluation results in a spreadsheet. A contour was marked as “error” if any of the thresholds defined in the metrics was triggered. Results

in terms of low contrast visibility. This limits the application of CBCT mainly to patient setup based on high contrast structures. We address these limitations by applying advanced preprocessing and reconstruction algorithms to improve patient setup and facilitate advanced applications like adaptive radiotherapy. Material and Methods The commercially available TrueBeam CBCT reconstruction pipeline removes scatter usi ng a kernel- based correction followed by filtered bac k-projection- based reconstruction (FDK). These reconstruction n pipeline steps are replaced by a physics-based scatter correction (pelvis only) and an iterative reconstruction. We use statistical reconstruction that takes the Poisson distribution of quantum noise into account, an d applies an edge preserving image regularization. The advanced scatter correction is based on a finite-ele ment solver (AcurosCTS) to model the behavior of photons as they pass (and scatter) through the object. Both algorit hms have been implemented on a GPU cluster pla tform, and algorithmic acceleration techniques are utilized to achieve clinically acceptable reconstruction times. The image quality improvements have been an alyzed on TrueBeam kV imaging system phantom scans, as well as on daily CBCT scans of head/neck and prostate cancer patients acquired for image-guided localization. Results Artifacts in head/neck FDK reconstructions (Fig . 1) e.g. resulting from photon starvation in the shoulder region or cone-beam are highly reduced in the iterative reconstructions. The iterative reconstruction s show enhanced soft tissue definition providing better cl arity for boundary definition (see the level 2 lymph node located in the contoured region of the axial view, Fig. 1). The advanced scatter correction applied for pelvis scans removes residual scatter artifacts, increasing the mean homogeneity from 78.2 HU ± 18.0 HU to 20.9 HU ± 10.9 HU within the bladder region of 9 daily CBCT scans of typical prostate patients. Iterative reconstruction provides further benefit by reducing image noise as well as eliminating streak and cone-beam artifacts, thereby significantly improving soft-tissue visualization, as noted in the clinical pelvis CBCT scan (Fig. 2). The noise level was reduced to 45% of the original value. Conclusion Statistical reconstruction in combination with advanced scatter correction substantially improves CBCT image quality by enabling removal of artifacts caused by remaining scatter, projection noise, photon starvation, and cone-beam angle. These artifact reductions improve soft tissue definition that is necessary for accurate visualization, contouring, dose calculation, and deformable image registration in clinical practice. The presented improvements are expected to facilitate soft tissue-based patient setup. Promise has been demonstrated for new applications, such as adaptive

The automatic evaluation of 11519 contours for 33 patients took 6 minutes on a computer with 8 GB RAM and 1.6 GHz Intel Xeon CPU. The evaluation results were compared to the errors recorded by a human observer, and confusion matrices were calculated. The mean error prevalence in the observer evaluation was 0.29 ± 0.1. Our algorithm achieved a mean sensitivity of 0.84 ± 0.1 (range [0.58 – 1.0]) and a mean specificity of 0.88 ± 0.1 (range [0.51 – 1.0]). One patient data set totalling 339 slices was evaluated with a sensitivity and specificity of 1.0. Conclusion Metric-based evaluation of rectum contours is a feasible alternative to evaluation of contours by a human observer. It provides an unbiased contour classification and detects over 80% of typical errors in the contours. The method can be used to assess the performance of automated contouring tools and to aid the development of improved contouring software. PO-0893 Improving CBCT image quality for daily image guidance of patients with head/neck and prostate cancer I. Chetty 1 , P. Paysan 2 , F. Siddiqui 1 , M. Weihua 1 , M. Brehm 2 , P. Messmer 2 , A. Maslowski 3 , A. Wang 3 , D. Seghers 2 , P. Munro 2 1 Henry Ford Health System, Radiation Oncology, Detroit, USA 2 Varian Medical Systems Imaging Laboratory GmbH, Image Enhancement and Reconstruction, Baden-Daettwil, Switzerland 3 Varian Medical Systems- Inc., Oncology Systems, Palo Alto, USA Purpose or Objective Image quality of on-board CBCT imaging in radiation therapy generally falls short of diagnostic CT in particular