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SU‐C‐BRF‐03: PCA Modeling of Anatomical Changes During Head and Neck Radiation Therapy
Author(s) -
Chetvertkov M,
Kim J,
Siddiqui F,
Kumarasiri A,
Chetty I,
Gordon J
Publication year - 2014
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.4889723
Subject(s) - voxel , nuclear medicine , eigenvalues and eigenvectors , head and neck cancer , proton therapy , radiation therapy , principal component analysis , cone beam computed tomography , head and neck , mathematics , dosimetry , medicine , radiology , statistics , physics , surgery , computed tomography , quantum mechanics
Purpose: To develop principal component analysis (PCA) models from daily cone beam CTs (CBCTs) of head and neck (H&N) patients that could be used prospectively in adaptive radiation therapy (ART). Methods: : For 7 H&N patients, Pinnacle Treatment Planning System (Philips Healthcare) was used to retrospectively deformably register daily CBCTs to the planning CT. The number N of CBCTs per treatment course ranged from 14 to 22. For each patient a PCA model was built from the deformation vector fields (DVFs), after first subtracting the mean DVF, producing N eigen‐DVFs (EDVFs). It was hypothesized that EDVFs with large eigenvalues represent the major anatomical deformations during the course of treatment, and that it is feasible to relate each EDVF to a clinically meaningful systematic or random change in anatomy, such as weight loss, neck flexion, etc. Results: DVFs contained on the order of 3×87×87×58=1.3 million scalar values (3 times the number of voxels in the registered volume). The top 3 eigenvalues accounted for ∼90% of variance. Anatomical changes corresponding to an EDVF were evaluated by generating a synthetic DVF, and applying that DVF to the CT to produce a synthetic CBCT. For all patients, the EDVF for the largest eigenvalue was interpreted to model weight loss. The EDVF for other eigenvalues appeared to represented quasi‐random fraction‐to‐fraction changes. Conclusion: The leading EDVFs from single‐patient PCA models have tentatively been identified with weight loss changes during treatment. Other EDVFs are tentatively identified as quasi‐random inter‐fraction changes. Clean separation of systematic and random components may require further work. This work is expected to facilitate development of population‐based PCA models that can be used to prospectively identify significant anatomical changes, such as weight loss, early in treatment, triggering replanning where beneficial.