Abstract
BackgroundThe aim of this work is to assess the impact of using different deformable registration (DR) algorithms on the quality of cone-beam CT (CBCT) correction with histogram matching (HM).Methods and materialsData sets containing planning CT (pCT) and CBCT images for ten patients with prostate cancer were used. Each pCT image was registered to its corresponding CBCT image using one rigid registration algorithm with mutual information similarity metric (RR-MI) and three DR algorithms with normalized correlation coefficient, mutual information and normalized mutual information (DR-NCC, DR-MI and DR-NMI, respectively). Then, the HM was performed between deformed pCT and CBCT in order to correct the distribution of the Hounsfield Units (HU) in CBCT images.ResultsThe visual assessment showed that the absolute difference between corrected CBCT and deformed pCT was reduced after correction with HM except for soft tissue-air and soft-tissue-bone interfaces due to the improper registration. Furthermore, volumes comparison in terms of average HU error showed that using DR-NCC algorithm with HM yielded the lowest error values of about 55.95 ± 10.43 HU compared to DR-MI and DR-NMI for which the errors were 58.60 ± 10.35 and 56.58 ± 10.51 HU, respectively. Tissue class’s comparison by the mean absolute error (MAE) plots confirmed the performance of DR-NCC algorithm to produce corrected CBCT images with lowest values of MAE even in regions where the misalignment is more pronounced. It was also found that the used method had successfully improved the spatial uniformity in the CBCT images by reducing the root mean squared difference (RMSD) between the pCT and CBCT in fat and muscle from 57 and 25 HU to 8HU, respectively.ConclusionThe choice of an accurate DR algorithm before performing the HM leads to an accurate correction of CBCT images. The results suggest that applying DR process based on NCC similarity metric reduces significantly the uncertainties in CBCT images and generates images in good agreement with pCT.
Highlights
The aim of this work is to assess the impact of using different deformable registration (DR) algorithms on the quality of cone-beam CT (CBCT) correction with histogram matching (HM)
The visual assessment showed that the absolute difference between corrected CBCT and deformed planning CT (pCT) was reduced after correction with HM except for soft tissue-air and soft-tissue-bone interfaces due to the improper registration
Volumes comparison in terms of average Hounsfield Units (HU) error showed that using DR-Normalized Correlation Coefficient (NCC) algorithm with HM yielded the lowest error values of about 55.95 ± 10.43 HU compared to DR-Mutual Information (MI) and DR-Normalized Mutual Information (NMI) for which the errors were 58.60 ± 10.35 and 56.58 ± 10.51 HU, respectively
Summary
The aim of this work is to assess the impact of using different deformable registration (DR) algorithms on the quality of cone-beam CT (CBCT) correction with histogram matching (HM). On board cone-beam CT, integrated into linear accelerators was frequently used for image guidance of radiotherapy It allowed the verification and the correction of patient’s setup during the course of treatment in three dimensions with sufficient soft tissue contrast and low patient dose [1,2,3,4]. The development of CBCT systems in terms of images acquisition, rapidity and improved image quality has underlined the question of using CBCT images for adaptive radiation therapy (ART) This technique aims to adapt the treatment planning with patient anatomy modification throughout the entire treatment; it is mainly based on three complex and consuming time processes: acquisition of daily CBCT images for making decision if the re-planning is necessary by comparing them to the CT images, the second process concerns the acquisition of new pCT images and the delineation of volumes of interest to provide a base for the last process which is the dose re-calculation [6]. The preposition of using daily CBCT images directly for dose calculation is limited, owing to their reduced contrast compared to CT images, as shown in Fig. 1, and the large variation of Hounsfield Units caused by the increased amount of scattered radiation [7, 8]
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