Accuracy of patient setup positioning using surface-guided radiotherapy with deformable registration in cases of surface deformation.

The Catalyst™ HD (C-RAD Positioning AB, Uppsala, Sweden) is surface-guided radiotherapy (SGRT) equipment that adopts a deformable model. The challenge in applying the SGRT system is accurately correcting the setup error using a deformable model when the body of the patient is deformed. This study evaluated the effect of breast deformation on the accuracy of the setup correction of the SGRT system. Physical breast phantoms were used to investigate the relationship between the mean deviation setup error obtained from the SGRT system and the breast deformation. Physical breast phantoms were used to simulate extension and shrinkage deformation (-30 to 30 mm) by changing breast pieces. Three-dimensional (3D) Slicer software was used to evaluate the deformation. The maximum deformations in X, Y, and Z directions were obtained as the differences between the original and deformed breasts. We collected the mean deviation setup error from the SGRT system by replacing the original breast part with the deformed breast part. The mean absolute difference of lateral, longitudinal, vertical, pitch, roll, and yaw, between the rigid and deformable registrations was 2.4 ± 1.7 mm, 1.3 ± 1.2 mm, 6.4 ± 5.2 mm, 2.5° ± 2.5°, 2.2° ± 2.4°, and 1.0° ± 1.0°, respectively. Deformation in the Y direction had the best correlation with the mean deviation translation error (R = 0.949) and rotation error (R = 0.832). As the magnitude of breast deformation increased, both mean deviation setup errors increased, and there was greater error in translation than in rotation. Large deformation of the breast surface affects the setup correction. Deformation in the Y direction most affects translation and rotation errors.

Evaluation of the Block Matching deformable registration algorithm in the field of head-and-neck adaptive radiotherapy.

BACKGROUND AND PURPOSE: To compare the accuracy of the Block Matching deformable registration (DIR) against rigid image registration (RIR) for head-and-neck multi-modal images CT to cone-beam CT (CBCT) registration. MATERIAL AND METHODS: Planning-CT and weekly CBCT of 10 patients were used for this study. Several volumes, including medullary canal (MC), thyroid cartilage (TC), hyoid bone (HB) and submandibular gland (SMG) were transposed from CT to CBCT images using either DIR or RIR. Transposed volumes were compared with the manual delineation of these volumes on every CBCT. The parameters of similarity used for analysis were: Dice Similarity Index (DSI), 95%-Hausdorff Distance (95%-HD) and difference of volumes (cc). RESULTS: With DIR, the major mean difference of volumes was -1.4 cc for MC, revealing limited under-segmentation. DIR limited variability of DSI and 95%-HD. It significantly improved DSI for TC and HB and 95%-HD for all structures but SMG. With DIR, mean 95%-HD (mm) was 3.01 ± 0.80, 5.33 ± 2.51, 4.99 ± 1.69, 3.07 ± 1.31 for MC, TC, HB and SMG, respectively. With RIR, it was 3.92 ± 1.86, 6.94 ± 3.98, 6.44 ± 3.37 and 3.41 ± 2.25, respectively. CONCLUSION: Block Matching is a valid algorithm for deformable multi-modal CT to CBCT registration. Values of 95%-HD are useful for ongoing development of its application to the cumulative dose calculation.