Dense feature-based motion estimation in MV fluoroscopy during dynamic tumor tracking treatment: preliminary study on reduced aperture and partial occlusion handling - Serpa, Marco; Bert, Christoph (Physics in Medicine and Biology, 2020)
Quality assurance solutions to complement available motion compensation technologies are central for their safe routine implementation and success of treatment. This work presents a dense feature-based method for soft-tissue tumor motion estimation in megavoltage (MV) beam's-eye-view (BEV) projections for potential intra-treatment monitoring during dynamic tumor tracking (DTT). Dense sampling and matching principles were employed to track a gridded set of features landmarks (FLs) in MV-BEV projections and estimate tumor motion, capable to overcome reduced field aperture and partial occlusion challenges. The algorithm's performance was evaluated by retrospectively applying it to fluoroscopic sequences acquired at similar to 2 frames s(-1) (fps) for a dynamic phantom and two lung stereotactic body radiation therapy (SBRT) patients treated with DTT on the Vero SBRT system. First, a field-specific train image is initialized by sampling the tumor region at, S, pixel intervals on a grid using a representative frame from a stream of query frames. Sampled FLs are locally characterized in the form of descriptor vectors and geometric attributes representing the target. For motion tracking, subsequent query frames are likewise sampled, corresponding feature descriptors determined, and then patch-wise matched to the training set based on their descriptors and geometric relationships. FLs with high correspondence are pruned and used to estimate tumor displacement. In scenarios of partial occlusions, position is estimated from the set of correctly (visible) FLs on past observations. Reconstructed trajectories were benchmarked against ground-truth manual tracking using the root-mean-square (RMS) as a metric of positional accuracy. A total of 19 fluoroscopy sequences were analyzed. This included scenarios of field aperture obstruction during three-dimensional conformal, as well as step-and-shoot intensity modulated radiotherapy (IMRT) delivery assisted with DTT. The algorithm resolved target motion satisfactorily. The RMS was <1.2 mm and <1.8 mm for the phantom and the clinical dataset, respectively. Dense tracking showed promising results to overcome localization challenges at the field penumbra and partial obstruction by multi-leaf collimator (MLC). Motion retrieval was possible in similar to 66% of the control points studied. In addition to MLC obstruction, changes in the external/internal breathing dynamics and baseline drifts were a major source of estimation bias. Dense feature-based tracking is a viable alternative. The algorithm is rotation-/scale-invariant and robust to photometric changes. Tracking multiple features may help overcome partial occlusion challenges by the MLC. This in turn opens up new possibilities for motion detection and intra-treatment monitoring during IMRT and potentially VMAT.
CRIS-Team WoS Importer:2021-01-15

Megavoltage intrafraction monitoring and position uncertainty in gimbaled markerless dynamic tumor tracking treatment of lung tumors - Serpa, Marco; Brandt, Tobias; Spohn, Simon K.B.; Rimner, Andreas; Bert, Christoph (Medical Physics, 2025)
Background: The clinical realization of markerless dynamic tumor tracking (MLDTT) has prompted a new paradigm shift to intrafraction imaging-based quality assurance (QA). During MLDTT treatment using a gimbaled accelerator, the megavoltage (MV) imager serves as an independent system to leverage real-time intrafraction monitoring. Soft-tissue feature tracking has shown promise for tumor localization in confined MV projections, but studies demonstrating its application in clinical MLDTT treatments are scarse. Purpose: To validate MV image-based dense soft-tissue feature tracking for intrafraction position monitoring of lung tumors during MLDTT stereotactic body radiotherapy (SBRT), and report on the resolved geometric uncertainty. Methods: Ten non-small cell lung cancer (NSCLC) patients underwent MLDTT-SBRT using a commercial gimbal-based system. During treatment, beam's-eye-view (BEV) projection images were captured at ∼3 frames s−1 (fps) using the electronic portal imaging device (EPID). MV sequences were streamed to a research workstation and processed off-line using a purpose-built algorithm, the soft-tissue feature tracker (SoFT). Both the tumor and dynamic field aperture position were automatically extracted in the pan and tilt directions of the gimbaled x-ray head, corresponding to the in-plane lateral and longitudinal direction of the imager, and compared to ground truth manual tracking. The success, percentage of fields producing an output, and performance of MV tracking under the presence/absence of anatomy-related obstruction and multi-leaf collimator (MLC) occlusion were quantified, including three-dimensional conformal (3DCRT) and step-and-shoot intensity modulated radiotherapy (IMRT) deliveries. In addition, the geometric uncertainty of MLDTT treatment was estimated as the difference between field aperture and target center position in the BEV. The standard deviation of systematic (Σ) and random (σ) errors were determined. Results: MV tracking was successful for 89.7% of (unmodulated) 3DCRT fields, as well as 82.4% of (modulated) control points (CPs) and subfields (SFs) for IMRT and field-in-field 3DCRT deliveries. The MV tracking accuracy was dependent on the traversed anatomy, tumor visibility, and occlusion by the MLC. The mean MV tracking accuracy was 1.2 mm (pan) and 1.4 mm (tilt), and a resultant 2D accuracy of 1.8 mm. The MV tracking performance within 2 mm was observed in 92.1% (pan) and 86.6% (tilt), respectively. The mean aperture-target positional uncertainty smaller than 3 mm/5 mm was observed in 94.4/97.9% (pan) and 89.6/96.7% (tilt) of the time. The group Σ and σ were 0.5 mm/0.8 mm (pan), and 0.7 mm/1.2 mm (tilt), compared to 0.3 mm/0.5 mm (pan), and 0.6 mm/0.9 mm (tilt) based on the manual ground truth. Conclusion: MV imaging coupled with the soft-tissue feature tracker algorithm constitutes a valuable non-invasive method for independent intrafraction surveillance. Tracking multiple features has shown the potential to improve position estimation, notwithstanding obstruction, and occlusion challenges, facilitating the quantification of the geometric uncertainty of MLDTT treatment.
CRIS-Team Scopus Importer:2025-04-18
Keywords: intrafraction monitoring; markerless dynamic tumor tracking; megavoltage tracking