Image preprocessing to improve the accuracy and robustness of mutual-information-based automatic image registration in proton therapy

Purpose: We propose a method that potentially improves the outcome of mutual-information-based automatic image registration by using the contrast enhancement filter (CEF).Methods: Seventy-six pairs of two-dimensional X-ray images and digitally reconstructed radiographs for 20 head and neck and nine lung cancer patients were analyzed retrospectively. Automatic image registration was performed using the mutual-information-based algorithm in VeriSuite (R). Images were preprocessed using the CEF in VeriSuite (R). The correction vector for translation and rotation error was calculated and manual image registration was compared with automatic image registration, with and without CEF. In addition, the normalized mutual information (NMI) distribution between two-dimensional images was compared, with and without CEF.Results: In the correction vector comparison between manual and automatic image registration, the average differences in translation error were < 1 mm in most cases in the head and neck region. The average differences in rotation error were 0.71 and 0.16 degrees without and with CEF, respectively, in the head and neck region; they were 2.67 and 1.64 degrees, respectively, in the chest region. When used with oblique projection, the average rotation error was 0.39 degrees with CEF. CEF improved the NMI by 17.9 % in head and neck images and 18.2 % in chest images.Conclusions: CEF preprocessing improved the NMI and registration accuracy of mutual-information-based automatic image registration on the medical images. The proposed method achieved accuracy equivalent to that achieved by experienced therapists and it will significantly contribute to the standardization of image registration quality.

Automated summary

The study proposes a method to improve mutual-information-based automatic image registration by using a contrast enhancement filter (CEF). The results show that using CEF preprocessing can enhance the normalized mutual information (NMI) and registration accuracy, achieving similar accuracy to experienced therapists.