Subpopulation-based correspondence modelling for improved respiratory motion estimation in the presence of inter-fraction motion variations. (26th June 2017)

• Record Type: Journal Article
• Authors: Wilms, Matthias; Werner, René; Yamamoto, Tokihiro; Handels, Heinz; Ehrhardt, Jan
• Is Part Of: Physics in medicine & biology. Volume 62:Number 14(2017:Jul.)
• Page Start: 5823
• Page End: 5839
• Publication Date: 2017-06-26
• Subjects: motion modelling -- motion variability -- radiation therapy -- spectral clustering; Biophysics -- Periodicals; Medical physics -- Periodicals; 610.153
• DOI: 10.1088/1361-6560/aa70cc
• Languages: English
• ISSNs: 0031-9155

Abstract

Abstract: Correspondence modelling between low-dimensional breathing signals and internal organ motion is a prerequisite for application of advanced techniques in radiotherapy of moving targets. Patient-specific correspondence models can, for example, be built prior to treatment based on a planning 4D CT and simultaneously acquired breathing signals. Reliability of pre-treatment-built models depends, however, on the degree of patient-specific inter-fraction motion variations. This study investigates whether motion estimation accuracy in the presence of inter-fraction motion variations can be improved using correspondence models that incorporate motion information from different patients. The underlying assumption is that inter-patient motion variations resemble patient-specific inter-fraction motion variations for subpopulations of patients with similar breathing characteristics. The hypothesis is tested by integrating a sparse manifold clustering approach into a regression-based correspondence modelling framework that allows for automated identification of patient subpopulations. The evaluation is based on a total of 73 lung 4D CT data sets, including two cohorts of patients with repeat 4D CT scans (cohort 1: 14 patients; cohort 2: ten patients). The results are consistent for both cohorts: The subpopulation-based modelling approach outperforms general population modelling (models built on all data sets available) as well as pre-treatment-built models trained on only the patient-specific motion information. The results thereby support the hypothesis and illustrate the potential of subpopulation-based correspondence modelling.