Validation of a CT-based motion model with in-situ fluoroscopy for lung surface deformation estimation

Abstract

Many surrogate-based motion models (SMMs), proposed to guide motion management in radiotherapy, are constructed by correlating motion of an external surrogate and internal anatomy during CT-simulation. Changes in this correlation define model break down. We validate a methodology that incorporates fluoroscopic images (FL) acquired during treatment for SMM construction and update. Under a prospective IRB, 4DCT scans, VisionRT 25 surfaces, and orthogonal FLs were collected from five lung cancer patients. VisionRT surfaces and two FL timeseries were acquired pre-and post-treatment. A simulated annealing optimization scheme was used to estimate optimal lung deformations by maximizing the mutual information between digitally reconstructed radiographs (DRRs) of the SMM-estimated 3D images and FLs. Our SMM used partial-least-regression and was trained using the optimal deformations and VisionRT surfaces from the first breathing-cycle. SMM performance was evaluated 30 using the mutual information score between reference FLs and the corresponding SMM or phase-assigned 4DCT DRRs. The Hausdorff distance for contoured landmarks was used to evaluate target position estimation error. For four out of five patients, two principal components approximated lung surface deformations with submillimeter accuracy. Analysis of the mutual information score between more than 4,000 pairs of FL and DRR demonstrated that our model led to more similarity between the FL and DRR images compared to 4DCT and DRR images from 35 a model based on an a priori correlation model. Our SMM consistently displayed lower mean and 95 th percentile Hausdorff distances. For one patient, 95 th percentile Hausdorff distance was reduced by 11mm. Patient-averaged reductions in mean and 95 th percentile Hausdorff distances were 3.6mm and 7mm for right-lung, and 3.1mm and 4mm for left-lung targets. FL data were used to evaluate model performance and investigate the feasibility of model update. Despite variability in breathing, use of post-treatment FL preserved model fidelity and consistently 40 outperformed 4DCT for position estimation.

Image preprocessing to improve the accuracy and robustness of mutual-information-based automatic image registration in proton therapy

“…In that report, the NMI between 2D X-ray images and DRRs was shown to be 0.45-0.85 in five lung patients. Our analysis of nine lung patients showed that the NMI was 0.43-0.71 in orthogonal images; these findings are in agreement with [23]. Using the proposed CEF method, the NMI was 0.57-0.80 in lung patients.…”

“…The similarity between DRRs and 2D X-ray images was evaluated using NMI. Ranjbar et al, [23] assessed the similarity between fluoroscopic images and DRRs created from 4DCT using NMI. In that report, the NMI between 2D X-ray images and DRRs was shown to be 0.45-0.85 in five lung patients.…”

CT respiratory motion synthesis using joint supervised and adversarial learning

“…higher dimensional) conditioning if available. For instance, more complex respiratory patterns could be better captured through clinically available real-time entire external surface measurements by optical devices (Nazir et al 2018, Ranjbar et al 2021. Including such information in the conditioning may thus further improve the model performance, which we will investigate in future research.…”

Motion correction for separate mandibular and cranial movements in cone beam CT reconstructions

“…13,14 Periodic motions, such as those caused by breathing or heart-beat,has been a topic of lively research over the last 20 years. [15][16][17][18][19] Typically, such approaches use models, for instance surrogate motion models, to estimate motion that is otherwise not directly accessible, 19,20 instead of real yet inaccessible organ motion, for example, lung deformation estimated from the thoracoabdominal surface 19 or by measuring air flow changes by spirometry. 21,22 Inherent errors from such surrogates lie in potential miscorrelation with the internal organ motion they shall represent.…”