Assessing the impact of choosing different deformable registration algorithms on cone-beam CT enhancement by histogram matching

Abstract

Background

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).

Methods and materials

Data 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.

Results

The 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.

Conclusion

The 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.

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