Hanlin Wan

Automated patient setup and gating using cone beam computed tomography projections.

In radiation therapy, fiducial markers are often implanted near tumors and used for patient positioning and respiratory gating purposes. These markers are then used to manually align the patients by matching the markers in the cone beam computed tomography (CBCT) reconstruction to those in the planning CT. This step is time-intensive and user-dependent, and often results in a suboptimal patient setup. We propose a fully automated, robust method based on dynamic programming (DP) for segmenting radiopaque fiducial markers in CBCT projection images, which are then used to automatically optimize the treatment couch position and/or gating window bounds. The mean of the absolute 2D segmentation error of our DP algorithm is 1.3 ± 1.0 mm for 87 markers on 39 patients. Intrafraction images were acquired every 3 s during treatment at two different institutions. For gated patients from Institution A (8 patients, 40 fractions), the DP algorithm increased the delivery accuracy (96 ± 6% versus 91 ± 11%, p < 0.01) compared to the manual setup using kV fluoroscopy. For non-gated patients from Institution B (6 patients, 16 fractions), the DP algorithm performed similarly (1.5 ± 0.8 mm versus 1.6 ± 0.9 mm, p = 0.48) compared to the manual setup matching the fiducial markers in the CBCT to the mean position. Our proposed automated patient setup algorithm only takes 1-2 s to run, requires no user intervention, and performs as well as or better than the current clinical setup.

Fully automatic segmentation of arbitrarily shaped fiducial markers in cone-beam CT projections.

Radio-opaque fiducial markers of different shapes are often implanted in or near abdominal or thoracic tumors to act as surrogates for the tumor position during radiotherapy. They can be used for real-time treatment adaptation, but this requires a robust, automatic segmentation method able to handle arbitrarily shaped markers in a rotational imaging geometry such as cone-beam computed tomography (CBCT) projection images and intra-treatment images. In this study, we propose a fully automatic dynamic programming (DP) assisted template-based (TB) segmentation method. Based on an initial DP segmentation, the DPTB algorithm generates and uses a 3D marker model to create 2D templates at any projection angle. The 2D templates are used to segment the marker position as the position with highest normalized cross-correlation in a search area centered at the DP segmented position. The accuracy of the DP algorithm and the new DPTB algorithm was quantified as the 2D segmentation error (pixels) compared to a manual ground truth segmentation for 97 markers in the projection images of CBCT scans of 40 patients. Also the fraction of wrong segmentations, defined as 2D errors larger than 5 pixels, was calculated. The mean 2D segmentation error of DP was reduced from 4.1 pixels to 3.0 pixels by DPTB, while the fraction of wrong segmentations was reduced from 17.4% to 6.8%. DPTB allowed rejection of uncertain segmentations as deemed by a low normalized cross-correlation coefficient and contrast-to-noise ratio. For a rejection rate of 9.97%, the sensitivity in detecting wrong segmentations was 67% and the specificity was 94%. The accepted segmentations had a mean segmentation error of 1.8 pixels and 2.5% wrong segmentations.

Automatic online and real-time tumour motion monitoring during stereotactic liver treatments on a conventional linac by combined optical and sparse monoscopic imaging with kilovoltage x-rays (COSMIK)

The purpose of this study was to develop, validate and clinically demonstrate fully automatic tumour motion monitoring on a conventional linear accelerator by combined optical and sparse monoscopic imaging with kilovoltage x-rays (COSMIK). COSMIK combines auto-segmentation of implanted fiducial markers in cone-beam computed tomography (CBCT) projections and intra-treatment kV images with simultaneous streaming of an external motion signal. A pre-treatment CBCT is acquired with simultaneous recording of the motion of an external marker block on the abdomen. The 3-dimensional (3D) marker motion during the CBCT is estimated from the auto-segmented positions in the projections and used to optimize an external correlation model (ECM) of internal motion as a function of external motion. During treatment, the ECM estimates the internal motion from the external motion at 20 Hz. KV images are acquired every 3 s, auto-segmented, and used to update the ECM for baseline shifts between internal and external motion. The COSMIK method was validated using Calypso-recorded internal tumour motion with simultaneous camera-recorded external motion for 15 liver stereotactic body radiotherapy (SBRT) patients. The validation included phantom experiments and simulations hereof for 12 fractions and further simulations for 42 fractions. The simulations compared the accuracy of COSMIK with ECM-based monitoring without model updates and with model updates based on stereoscopic imaging as well as continuous kilovoltage intrafraction monitoring (KIM) at 10 Hz without an external signal. Clinical real-time tumour motion monitoring with COSMIK was performed offline for 14 liver SBRT patients (41 fractions) and online for one patient (two fractions). The mean 3D root-mean-square error for the four monitoring methods was 1.61 mm (COSMIK), 2.31 mm (ECM without updates), 1.49 mm (ECM with stereoscopic updates) and 0.75 mm (KIM). COSMIK is the first combined kV/optical real-time motion monitoring method used clinically online on a conventional accelerator. COSMIK gives less imaging dose than KIM and is in addition applicable when the kV imager cannot be deployed such as during non-coplanar fields.

SU-C-BRA-01: Interactive Auto-Segmentation for Bowel in Online Adaptive MRI-Guided Radiation Therapy by Using a Multi-Region Labeling Algorithm

PURPOSE In MRI-guided online adaptive radiation therapy, re-contouring of bowel is time-consuming and can impact the overall time of patients on table. The study aims to auto-segment bowel on volumetric MR images by using an interactive multi-region labeling algorithm. METHODS 5 Patients with locally advanced pancreatic cancer underwent fractionated radiotherapy (18-25 fractions each, total 118 fractions) on an MRI-guided radiation therapy system with a 0.35 Tesla magnet and three Co-60 sources. At each fraction, a volumetric MR image of the patient was acquired when the patient was in the treatment position. An interactive two-dimensional multi-region labeling technique based on graph cut solver was applied on several typical MRI images to segment the large bowel and small bowel, followed by a shape based contour interpolation for generating entire bowel contours along all image slices. The resulted contours were compared with the physicians manual contouring by using metrics of Dice coefficient and Hausdorff distance. RESULTS Image data sets from the first 5 fractions of each patient were selected (total of 25 image data sets) for the segmentation test. The algorithm segmented the large and small bowel effectively and efficiently. All bowel segments were successfully identified, auto-contoured and matched with manual contours. The time cost by the algorithm for each image slice was within 30 seconds. For large bowel, the calculated Dice coefficients and Hausdorff distances (mean±std) were 0.77±0.07 and 13.13±5.01mm, respectively; for small bowel, the corresponding metrics were 0.73±0.08and 14.15±4.72mm, respectively. CONCLUSION The preliminary results demonstrated the potential of the proposed algorithm in auto-segmenting large and small bowel on low field MRI images in MRI-guided adaptive radiation therapy. Further work will be focused on improving its segmentation accuracy and lessening human interaction.

TU‐C‐213CD‐03: Fiducial Marker Motion Tracking Using Dynamic Programming

Purpose: To develop a novel algorithm based on a global optimization strategy ‐ dynamic programming (DP) ‐ to track the motion of fiducial markers due to respiratory motion in cone beam CT projections. Methods: A two‐step iterative algorithm was used to track the marker locations. First, search windows were constructed to limit the search space. Second, DP was used to minimize a cost function involving the marker location, trajectory, and intensity. This algorithm was tested on 11 human data sets with varying number (1–8), type (Visicoils, embolization coils, stents, surgical clips), size (0.1–2 cm), and shape of markers. Markers often overlapped with other markers or background objects. The algorithm was also compared to the traditional template matching algorithms. Results: The 11 data sets contained 7144 projection images. The algorithm correctly identified the markers in 99.15% of the images. Of the images where the algorithm failed, the average error was 0.72+−0.39 mm. The algorithm takes about 0.1 seconds per marker per data set to run on a 1.6 GHz computer. Compared to template matching, which succeeded in only one of 11 data sets, our algorithm is more robust, faster, and requires no a priori knowledge about the markers. Conclusions: Our work provides superior tracking capabilities of fiducial markers in cone beam CT projections. It can be used in clinical settings to aid radiation therapists in the patient setup process and gating window selection, decreasing the patient setup time and radiation exposure. Research sponsored in part by Varian Medical Systems Funded in part by Varian Medical Systems