A neural network-based 2D/3D image registration quality evaluator for pediatric patient setup in external beam radiotherapy.

A neural network-based 2D/3D image registration quality evaluator for pediatric patient setup in external beam radiotherapy. J Appl Clin Med Phys. 2016;17(1):5235 Authors: Wu J, Su Z, Li Z Abstract Our purpose was to develop a neural network-based registration quality evaluator (RQE) that can improve the 2D/3D image registration robustness for pediatric patient setup in external beam radiotherapy. Orthogonal daily setup X-ray images of six pediatric patients with brain tumors receiving proton therapy treatments were retrospectively registered with their treatment planning computed tomography (CT) images. A neural network-based pattern classifier was used to determine whether a registration solution was successful based on geometric features of the similarity measure values near the point-of-solution. Supervised training and test datasets were generated by rigidly registering a pair of orthogonal daily setup X-ray images to the treatment planning CT. The best solution for each registration task was selected from 50 optimizing attempts that differed only by the randomly generated initial transformation parameters. The distance from each individual solution to the best solution in the normalized parametrical space was compared to a user-defined error tolerance to determine whether that solution was acceptable. A supervised training was then used to train the RQE. Performance of the RQE was evaluated using test dataset consisting of regi...
Source: Journal of Applied Clinical Medical Physics - Category: Physics Authors: Tags: J Appl Clin Med Phys Source Type: research