Encrypted login | home

Program Information

PCA Modeling of Anatomical Changes During Head and Neck Radiation Therapy

no image available
M Chetvertkov

M Chetvertkov*, J Kim , F Siddiqui , A Kumarasiri , I Chetty , J Gordon , Henry Ford Health System, Detroit, MI


SU-C-BRF-3 Sunday 1:00PM - 1:55PM Room: Ballroom F

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 3x87x87x58=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.

Contact Email: