Multi-region Algorithm Research Articles

A multi-region algorithm for N-TH coupling calculation and its application to nuclear thermal propulsion reactor

Physical field mapping problem is generally faced in multi-physics coupling calculations, and multi-region (usually divided by different physical properties) coupling further increases the problem’s difficulty. The multi-region algorithm can achieve the solid–fluid conjugate heat transfer for the multi-region Neutronic and Thermal-Hydraulic (N-TH) coupling calculation. This paper proposed a multi-region algorithm for solving neutronic fields and further coupled it with multi-region heat transfer in OpenFOAM. The multi-region algorithm solves the neutron transport equation region-by-region and establishes a new coupling boundary condition to eliminate the obstacles to communication between adjacent regions. The multi-region algorithm is developed in OpenFOAM as a neutron transport solver and verified by the 2D-IAEA, 2D-TWIGL, and the 3D-TAKEDA benchmarks. The multi-region neutron transport solver and the original Conjugate Heat Transfer (CHT) solver are coupled to establish a multi-physics coupling platform. Hence, the multi-physics solver can provide pin-by-pin neutronics and thermal hydraulics results for multiple regions (including solid or fluid regions) based on the same set of grids. The geometric adaptability and parallel performance of the coupling platform are superior, which depends on the inherent characteristics of the OpenFOAM platform. The coupling platform is used to perform N-TH coupling calculations for the Nuclear Thermal Propulsion (NTP) reactor's Low Enriched Uranium (LEU) assemblies, demonstrating the coupling calculation capability of the multi-region algorithm for complex reactor geometry systems. The calculation results show intense N-TH coupling effects in the NTP assemblies, which can provide an essential reference for nuclear thermal propulsion reactor design.

Real-time Tracking of Markerless Lung Tumors During SBRT With Continuous MV Imaging

Purpose/Objective(s): Accurate treatment verification is of particular importance when delivering high doses per fraction, as in lung SBRT. In this study, we characterize and validate the performance of a real-time markerless lung tumor-tracking algorithm, using continuous MV portal imaging. MV portal imaging is advantageous, as beam’s-eye-view, images allow motion capture in both directions of steep dose fall-off, and the patient is not exposed to additional imaging dose. Furthermore, a markerless tracking system avoids the risk associated with implanting fiducial markers. Materials/Methods: We retrospectively analyzed the delivery of 9 lung SBRT treatments carried out at our institution. Radiation therapy was delivered with a 6 MV beam in 3 fractions to 54 Gy or in 5 fractions to 60 Gy, using 9-11 beams, including non-coplanar geometries. Treatments were monitored by continuous acquisition of portal images with a frame rate of 2 Hz. Soft tissue motion was tracked on the portal images with a multi-region algorithm developed for this purpose. For each field and each fraction a set of landmark candidates was automatically defined on the first image. The tracker then identified this set on each subsequent image by means of a similarity measure and calculated a centroid trajectory. In this way, the images were analyzed in a prospective manner to simulate in-treatment real-time analysis. The first 40 images of 54 fractions from 34 beam angles were used for comparison with expert human examiners. To minimize bias, the images were presented to the examiner in random sequence and each image was shown 3 times. Tracking error in a realistic phantom was determined as a baseline. Computation time was measured for real-time processing of the images. Results: In-treatment EPID images were successfully acquired for 9 lung SBRT patients with 3 or 5 fractions each (19,942 portal images), including non-coplanar geometries. By comparing with the manual tracking, the RMS tracking accuracy of the automatic marker-less algorithm was found to be (2.1 1.7) mm. Excluding the worst 9 series (out of 54) reduced the RMSE to (1.5 1.1) mm. If we define tracking success as having a RMSE <3 mm, the success rate in our data was 86%. Without any tracking, the error would have been (3.9 2.7) mm. For the phantom study, the tracking error was <1 mm at all times, with a RMSE of (0.8 0.2) mm. The latency of the current implementation is 70-80 ms. Conclusions: In order to verify geometric fidelity during lung SBRT treatments at our institution, we have developed and tested a robust, prospective, real-time algorithm to track lung tumors on portal image sequences without the use of fiducial markers or user intervention. This algorithm can be implemented as a clinical tool for treatment verification, to derive delivered dose, or to drive a real-time motion compensation protocol. Author Disclosure: J. Rottmann: None. Y. Yue: None. A. Chen: None. D. Kozono: None. R. Mak: None. F. Hacker: None. R.I. Berbeco: None.

A multi-region algorithm for markerless beam's-eye view lung tumor tracking

Methods that allow online lung tumor tracking during radiotherapy are desirable for a variety of applications that have the potential to vastly improve treatment accuracy, dose conformity and sparing of healthy tissue. Several publications have proposed the use of an on-board kV x-ray imager to assess the tumor location during treatment. However, there is some concern that this strategy may expose the patient to a significant amount of additional dose over the course of a typical radiotherapy treatment. In this paper we present an algorithm that utilizes the on-board portal imager of the treatment machine to track lung tumors. This does not expose the patient to additional dose, but is somewhat more challenging as the quality of portal images is inferior when compared to kV x-ray images. To quantify the performance of the proposed algorithm we retrospectively applied it to portal image sequences retrieved from a dynamic chest phantom study and an SBRT treatment performed at our institution. The results were compared to manual tracking by an expert. For the phantom data the tracking error was found to be smaller than 1 mm and for the patient data smaller than 2 mm, which was in the same range as the uncertainty of the gold standard.

TU‐E‐204B‐03: A Multiregion Algorithm for Robust Lung Tumor Tracking with Portal Image Sequences

Purpose: Methods for online lung tumor tracking during radio‐therapy are desirable for a variety of applications that have the potential to vastly improve treatment accuracy, dose conformity and sparing of healthy tissue. Using the MV beam and portal imager for this purpose is preferable as there is no additional imaging dose and the output is the location of the target in the beam's‐eye‐view. Method and Materials: We present a multiregion algorithm that utilizes the on‐board portal imager of the treatment machine to track lung tumors without implanted fiducial markers. Multiple regions with unique image structures are automatically identified and optimized based on an iterative procedure. Each region is tracked independently throughout the treatment, making the algorithm robust to target deformations, rotations and partial occlusions. To quantify the performance, the proposed algorithm was retrospectively applied to portal image sequences from a dynamic chest phantom study as well as an SBRT treatment performed at our institution. The results were compared to manual tracking by an expert. Results: For the phantom data the tracking error was found to be smaller than 1mm and for the patient data smaller than 2mm, which was in the same range as the uncertainty of the reference. All optimized regions were able to be tracked on all images in both phantom and clinical sequences. The algorithm allows tracking of 15 regions at 2fps, the rate at which the images are clinically acquired. Conclusion: We have developed and tested a robust algorithm to track lung tumors on portal image sequences without the use of fiducial markers or user intervention. The algorithm has the potential to be implemented in real time and is not dependent on prior information of the exact tumor motion range during treatment (unlike classification algorithms which generally require this). Conflict of Interest: Varian Medical Systems Inc.