2D–3D registration for cranial radiation therapy using a 3D kV CBCT and a single limited field-of-view 2D kV radiograph
© 2018 American Association of Physicists in Medicine Purpose: We present and evaluate a fully automated 2D–3D intensity-based registration framework using a single limited field-of-view (FOV) 2D kV radiograph and a 3D kV CBCT for 3D estimation of patient setup errors during brain radiotherapy. Methods: We evaluated two similarity measures, the Pearson correlation coefficient on image intensity values (ICC) and maximum likelihood measure with Gaussian noise (MLG), derived from the statistics of transmission images. Pose determination experiments were conducted on 2D kV radiographs in the anterior–posterior (AP) and left lateral (LL) views and 3D kV CBCTs of an anthropomorphic head phantom. In order to minimize radiation exposure and exclude nonrigid structures from the registration, limited FOV 2D kV radiographs were employed. A spatial frequency band useful for the 2D–3D registration was identified from the bone-to-no-bone spectral ratio (BNBSR) of digitally reconstructed radiographs (DRRs) computed from the 3D kV planning CT of the phantom. The images being registered were filtered accordingly prior to computation of the similarity measures. We evaluated the registration accuracy achievable with a single 2D kV radiograph and with the registration results from the AP and LL views combined. We also compared the performance of the 2D–3D registration solutions proposed to that of a commercial 3D–3D registration algorithm, which used the entire skull for the registration. The ground truth was determined from markers affixed to the phantom and visible in the CBCT images. Results: The accuracy of the 2D–3D registration solutions, as quantified by the root mean squared value of the target registration error (TRE) calculated over a radius of 3 cm for all poses tested, was ICCAP: 0.56 mm, MLGAP: 0.74 mm, ICCLL: 0.57 mm, MLGLL: 0.54 mm, ICC (AP and LL combined): 0.19 mm, and MLG (AP and LL combined): 0.21 mm. The accuracy of the 3D–3D registration algorithm was 0.27 mm. There was no significant difference in mean TRE for the 2D–3D registration algorithms using a single 2D kV radiograph with similarity measure and image view point. There was no significant difference in mean TRE between ICCLL, MLGLL, ICC (AP and LL combined), MLG (AP and LL combined), and the 3D–3D registration algorithm despite the smaller FOV used for the 2D–3D registration. While submillimeter registration accuracy was obtained with both ICC and MLG using a single 2D kV radiograph, combining the results from the two projection views resulted in a significantly smaller (P≤0.05) mean TRE. Conclusions: Our results indicate that it is possible to achieve submillimeter registration accuracy with both ICC and MLG using either single or dual limited FOV 2D kV radiographs of the head in the AP and LL views. The registration accuracy suggests that the 2D–3D registration solutions presented are suitable for the estimation of patient setup errors not only during conventional brain radiation therapy, but also during stereotactic procedures and proton radiation therapy where tighter setup margins are required.
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School of Medicine