Development and prospective in‐patient proof‐of‐concept validation of a surface photogrammetry + CT‐based volumetric motion model for lung radiotherapy. Issue 12 (25th October 2019)

• Record Type: Journal Article
• Authors: Ranjbar, M.; Sabouri, P.; Mossahebi, S.; Leiser, D.; Foote, M.; Zhang, J.; Lasio, G.; Joshi, S.; Sawant, A.
• Is Part Of: Medical physics. Volume 46:Issue 12(2019)
• Page Start: 5407
• Page End: 5420
• Publication Date: 2019-10-25
• Subjects: in-patient validation -- lung radiotherapy -- motion management -- real-time -- surface-guided -- volumetric motion model; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1002/mp.13824
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: We develop and validate a motion model that uses real‐time surface photogrammetry acquired concurrently with four‐dimensional computed tomography (4DCT) to estimate respiration‐induced changes within the entire irradiated volume, over arbitrarily many respiratory cycles. Methods: A research, couch‐mounted, VisionRT (VRT) system was used to acquire optical surface data (15 Hz, ROI = 15 × 20 cm 2 ) from the thoraco‐abdominal surface of a consented lung SBRT patient, concurrently with their standard‐of‐care 4DCT. The end‐exhalation phase from the 4DCT was regarded as reference and for each remaining phase, deformation vector fields (DVFs) with respect to the reference phase were computed. To reduce dimensionality, the first two principal components (PCs) of the matrix of nine DVFs were calculated. In parallel, ten phase‐averaged VRT surfaces were created. Surface DVFs and corresponding PCs were computed. A principal least squares regression was used to relate the PCs of surface DVF to those of volume DVFs, establishing a relationship between time‐varying surface and the underlying time‐varying volume. Proof‐of‐concept validation was performed during each treatment fraction by concurrently acquiring 30 s time series of real‐time surface data and "ground truth" kV fluoroscopic data (FL). A ray‐tracing algorithm was used to create a digitally reconstructed fluorograph (DRF), and motion trajectories of high‐contrast, soft‐tissue, anatomical features in the DRF were compared with those from kV FL. Results: For five of the six fluoroscopic acquisition sessions, the model out‐performed 4DCT in predicting contour Dice coefficient with respect to fluoroscopy‐derived contours. Similarly, the model exhibited a marked improvement over 4DCT for patch positions on the diaphragm. Model patch position errors varied from 5 to −15 mm while 4DCT errors ranged between 5 and −22.4 mm. For one fluoroscopic acquisition, a marked change in the a priori internal–external correlation resulted in model errors comparable to those of 4DCT. Conclusions: We described the development and a proof‐of‐concept validation for a volumetric motion model that uses surface photogrammetry to correlate the time‐varying thoraco‐abdominal surface to the time‐varying internal thoraco‐abdominal volume. These early results indicate that the proposed approach can result in a marked improvement over 4DCT. While limited by the duration of the fluoroscopic acquisitions as well as the resolution of the acquired images, the DRF‐based proof‐of‐concept technique developed here is model‐agnostic, and therefore, has the potential to be used as an in‐patient validation tool for other volumetric motion models.