The markerless lung target tracking AAPM Grand Challenge (MATCH) results

PURPOSE

Lung stereotactic ablative body radiotherapy (SABR) is a radiation therapy success story with level 1 evidence demonstrating its efficacy. To provide real-time respiratory motion management for lung SABR, several commercial and preclinical markerless lung target tracking (MLTT) approaches have been developed. However, these approaches have yet to be benchmarked using a common measurement methodology. This knowledge gap motivated the MArkerless lung target Tracking CHallenge (MATCH). The aim was to localize lung targets accurately and precisely in a retrospective in silico study and a prospective experimental study.

METHODS

MATCH was an American Association of Physicists in Medicine sponsored Grand Challenge. Common materials for the in silico and experimental studies were the experiment setup including an anthropomorphic thorax phantom with two targets within the lungs, and a lung SABR planning protocol. The phantom was moved rigidly with patient-measured lung target motion traces, which also acted as ground truth motion. In the retrospective in silico study a volumetric modulated arc therapy treatment was simulated and a dataset consisting of treatment planning data and intra-treatment kilovoltage (kV) and megavoltage (MV) images for four blinded lung motion traces was provided to the participants. The participants used their MLTT approach to localize the moving target based on the dataset. In the experimental study, the participants received the phantom experiment setup and five patient-measured lung motion traces. The participants used their MLTT approach to localize the moving target during an experimental SABR phantom treatment. The challenge was open to any participant, and participants could complete either one or both parts of the challenge. For both the in silico and experimental studies the MLTT results were analyzed and ranked using the prospectively defined metric of the percentage of the tracked target position being within 2 mm of the ground truth.

RESULTS

A total of 30 institutions registered and 15 result submissions were received, four for the in silico study and 11 for the experimental study. The participating MLTT approaches were: Accuray CyberKnife (2), Accuray Radixact (2), BrainLab Vero, C-RAD, and preclinical MLTT (5) on a conventional linear accelerator (Varian TrueBeam). For the in silico study the percentage of the 3D tracking error within 2 mm ranged from 50% to 92%. For the experimental study, the percentage of the 3D tracking error within 2 mm ranged from 39% to 96%.

CONCLUSIONS

A common methodology for measuring the accuracy of MLTT approaches has been developed and used to benchmark preclinical and commercial approaches retrospectively and prospectively. Several MLTT approaches were able to track the target with sub-millimeter accuracy and precision. The study outcome paves the way for broader clinical implementation of MLTT. MATCH is live, with datasets and analysis software being available online at https://www.aapm.org/GrandChallenge/MATCH/ to support future research.

Deep Learning model for markerless tracking in spinal SBRT

Stereotactic Body Radiation Therapy (SBRT), alternatively termed Stereotactic ABlative Radiotherapy (SABR) or Stereotactic RadioSurgery (SRS), delivers high dose with a sub-millimeter accuracy. It requires meticulous precautions on positioning, as sharp dose gradients near critical neighboring structures (e.g. the spinal cord for spinal tumor treatment) are an important clinical objective to avoid complications such as radiation myelopathy, compression fractures, or radiculopathy. To allow for dose escalation within the target without compromising the dose to critical structures, proper immobilization needs to be combined with (internal) motion monitoring. Metallic fiducials, as applied in prostate, liver or pancreas treatments, are not suitable in clinical practice for spine SBRT. However, the latest advances in Deep Learning (DL) allow for fast localization of the vertebrae as landmarks. Acquiring projection images during treatment delivery allows for instant 2D position verification as well as sequential (delayed) 3D position verification when incorporated in a Digital TomoSynthesis (DTS) or Cone Beam Computed Tomography (CBCT). Upgrading to an instant 3D position verification system could be envisioned with a stereoscopic kilovoltage (kV) imaging setup. This paper describes a fast DL landmark detection model for vertebra (trained in-house) and evaluates its accuracy to detect 2D motion of the vertebrae with the help of projection images acquired during treatment. The introduced motion consists of both translational and rotational variations, which are detected by the DL model with a sub-millimeter accuracy.

Detection and localization of radiotherapy targets by template matching

Radio opaque fiducials are implanted in tumors for the purpose of tracking the target motion using X-ray projections during radiation therapy dose delivery. In this paper we describe and evaluate a novel method based on template matching for detection and localization of arbitrary shaped fiducials. Segmentation methods are not adequate for these fiducials because their appearance in online X-ray projections can vary greatly as a function of imaging angle. The algorithm is based on using the planning CT image to generate templates that correspond to the imaging angles of the online images. We demonstrate successful tracking of complex shape fiducials in clinical images of lung and abdomen. We also validate the algorithm by comparing the results with a segmentation approach for one case in which the fiducials could be tracked by both methods. We also show how by adaptive thresholding of the match scores, we can control the false detection rate.