A virtual phantom library for the quantification of deformable image registration uncertainties in patients with cancers of the head and neck

Authors


Abstract

Purpose:

Deformable image registration (DIR) is being used increasingly in various clinical applications. However, the underlying uncertainties of DIR are not well-understood and a comprehensive methodology has not been developed for assessing a range of interfraction anatomic changes during head and neck cancer radiotherapy. This study describes the development of a library of clinically relevant virtual phantoms for the purpose of aiding clinicians in the QA of DIR software. These phantoms will also be available to the community for the independent study and comparison of other DIR algorithms and processes.

Methods:

Each phantom was derived from a pair of kVCT volumetric image sets. The first images were acquired of head and neck cancer patients prior to the start-of-treatment and the second were acquired near the end-of-treatment. A research algorithm was used to autosegment and deform the start-of-treatment (SOT) images according to a biomechanical model. This algorithm allowed the user to adjust the head position, mandible position, and weight loss in the neck region of the SOT images to resemble the end-of-treatment (EOT) images. A human-guided thin-plate splines algorithm was then used to iteratively apply further deformations to the images with the objective of matching the EOT anatomy as closely as possible. The deformations from each algorithm were combined into a single deformation vector field (DVF) and a simulated end-of-treatment (SEOT) image dataset was generated from that DVF. Artificial noise was added to the SEOT images and these images, along with the original SOT images, created a virtual phantom where the underlying “ground-truth” DVF is known. Images from ten patients were deformed in this fashion to create ten clinically relevant virtual phantoms. The virtual phantoms were evaluated to identify unrealistic DVFs using the normalized cross correlation (NCC) and the determinant of the Jacobian matrix. A commercial deformation algorithm was applied to the virtual phantoms to show how they may be used to generate estimates of DIR uncertainty.

Results:

The NCC showed that the simulated phantom images had greater similarity to the actual EOT images than the images from which they were derived, supporting the clinical relevance of the synthetic deformation maps. Calculation of the Jacobian of the “ground-truth” DVFs resulted in only positive values. As an example, mean error statistics are presented for all phantoms for the brainstem, cord, mandible, left parotid, and right parotid.

Conclusions:

It is essential that DIR algorithms be evaluated using a range of possible clinical scenarios for each treatment site. This work introduces a library of virtual phantoms intended to resemble real cases for interfraction head and neck DIR that may be used to estimate and compare the uncertainty of any DIR algorithm.

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