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

Abstract

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

A simulated comparison of lung tumor target verification using stereoscopic tomosynthesis or radiography

“…Similar software is already being developed in radiography for markerless tumor tracking and could potentially be adapted to tomosynthesis. 24 Stereoscopic tomosynthesis could be used in several possible ways to improve motion management. In a gated treatment, a kV tomosynthesis snapshot could be acquired immediately before or after a gating window to ensure that the GTV is still contained within the planning treatment volume.…”

“…The development of this software presents different challenges that are outside the scope of the work, but without such software a clinician would need to continuously monitor the treatment and manually decide when adjustments are necessary. Similar software is already being developed in radiography for markerless tumor tracking and could potentially be adapted to tomosynthesis 24 …”

Feasibility study of deep learning‐based markerless real‐time lung tumor tracking with orthogonal X‐ray projection images

“…7 To overcome the shortcomings of the markerimplanted approach,markerless RTTT is required.Markerless RTTT is defined as a real-time tumor positioning system in which no exogenous materials are implanted into the patient body to assist the image-guiding process. 10 Without the implantation of markers, the total treatment duration can be shortened, and patients will be free from potential risks. With the markerless approach, metal artifacts in pCT and marker-induced errors can be efficiently eliminated.…”

Effect of different noise reduction techniques and template matching parameters on markerless tumor tracking using dual‐energy imaging

“…4,[6][7][8][9][10] One of these approaches is dual-energy (DE) subtraction imaging that improves the contrast of lung tumors by removing bone and has been shown to increase the accuracy of MTT. [8][9][10][11][12][13] However, this improved tumor contrast comes at the cost of increased image noise, 13 which may decrease the accuracy of MTT. To regain the benefits of bone removal, several noise reduction techniques have been investigated for DE imaging primarily in the diagnostic setting.…”

“…For these cases, various techniques have been investigated to increase the visibility of lung tumors 4,6–10 . One of these approaches is dual‐energy (DE) subtraction imaging that improves the contrast of lung tumors by removing bone and has been shown to increase the accuracy of MTT 8–13 . However, this improved tumor contrast comes at the cost of increased image noise, 13 which may decrease the accuracy of MTT.…”