MR image-guided portal verification for brain treatment field.

PURPOSE: To investigate a method for the generation of digitally reconstructed radiographs directly from MR images (DRR-MRI) to guide a computerized portal verification procedure.

METHODS AND MATERIALS: Several major steps were developed to perform an MR image-guided portal verification procedure. Initially, a wavelet-based multiresolution adaptive thresholding method was used to segment the skin slice-by-slice in MR brain axial images. Some selected anatomical structures, such as target volume and critical organs, were then manually identified and were reassigned to relatively higher intensities. Interslice information was interpolated with a directional method to achieve comparable display resolution in three dimensions. Next, a ray-tracing method was used to generate a DRR-MRI image at the planned treatment position, and the ray tracing was simply performed on summation of voxels along the ray. The skin and its relative positions were also projected to the DRR-MRI and were used to guide the search of similar features in the portal image. A Canny edge detector was used to enhance the brain contour in both portal and simulation images. The skin in the brain portal image was then extracted using a knowledge-based searching technique. Finally, a Chamfer matching technique was used to correlate features between DRR-MRI and portal image.

RESULTS: The MR image-guided portal verification method was evaluated using a brain phantom case and a clinical patient case. Both DRR-CT and DRR-MRI were generated using CT and MR phantom images with the same beam orientation and then compared. The matching result indicated that the maximum deviation of internal structures was less than 1 mm. The segmented results for brain MR slice images indicated that a wavelet-based image segmentation technique provided a reasonable estimation for the brain skin. For the clinical patient case with a given portal field, the MR image-guided verification method provided an excellent match between features in both DRR-MRI and portal image. Moreover, target volume could be accurately visualized in the DRR-MRI and mapped over to the corresponding portal image for treatment verification. The accuracy of DRR-MRI was also examined by comparing it to the corresponding simulation image. The matching results indicated that the maximum deviation of anatomical features was less than 2.5 mm.

CONCLUSION: A method for MR image-guided portal verification of brain treatment field was developed. Although the radiographic appearance in the DRR-MRI is different from that in the portal image, DRR-MRI provides essential anatomical features (landmarks and target volume) as well as their relative locations to be used as references for computerized portal verification.